Revista IJIMAI

Analysis of Artificial Intelligence Policies for Higher Education in Europe

Analysis of Artificial Intelligence Policies for Higher Education in Europe Stracke, Christian M.; Griffiths, Dai; Pappa, Dimitra; Bećirović, Senad; Polz, Edda; Perla, Loredana; Di Grassi, Annamaria; Massaro, Stefania; Skenduli, Marjana Prifti; Burgos, Daniel; Punzo, Veronica; Amram, Denise; Ziouvelou, Xenia; Katsamori, Dora; Gabriel, Sonja; Nahar, Nurun; Schleiss, Johannes; Hollins, Paul This paper analyses 15 AI policies for higher education from eight European countries, drawn from individual universities, from consortia of universities and from government agencies. Based on an overview of current research findings, it focuses the comparison of different aspects among the selected AI policies. The analysis distinguishes between four potential target groups, namely students, teachers, education managers and policy makers. The paper aims at contributing to the further development and improvement of AI policies for higher education through the identification of commonalities and gaps within the existing AI policies. Moreover, it calls for further and in particular evidence-based research to identify the potential and practical impact of AI in higher education and highlights the need to combine AI use in (higher) education with education about AI, often called as AI literacy. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T10:40:28Z No. of bitstreams: 1 Analysis of Artificial Intelligence Policies for Higher Education in Europe.pdf: 1640740 bytes, checksum: 8d471bab074d26edb071f5e645194f74 (MD5); Made available in DSpace on 2026-03-11T10:40:28Z (GMT). No. of bitstreams: 1 Analysis of Artificial Intelligence Policies for Higher Education in Europe.pdf: 1640740 bytes, checksum: 8d471bab074d26edb071f5e645194f74 (MD5)

Effects of a Flipped Classroom Learning System Integrated With ChatGPT on Students: a Survey From China

Effects of a Flipped Classroom Learning System Integrated With ChatGPT on Students: a Survey From China Cheng, Jing; Mokmin, Nur Azlina Mohamed; Shen, Qi In design education, patterns and symbols representing traditional national cultures are often utilized as teaching materials. However, conventional teaching methods frequently fall short in aiding students' comprehension of these intricate symbolisms and abstract concepts, leading to reduced engagement and ineffective learning outcomes. Therefore, we aim to explore whether ChatGPT, as a powerful tool, can assist in solving this problem. Specifically, we integrate ChatGPT into a flipped classroom learning system to assess its effectiveness in enhancing students' understanding of traditional Chinese culture. This research contributes to the feasibility of integrating ChatGPT in design education, particularly in the context of Chinese culture. Additionally, it serves as an exploratory attempt to apply ChatGPT in teaching practices within the field of design. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T10:11:09Z No. of bitstreams: 1 Effects of a Flipped Classroom Learning System Integrated With ChatGPT on Students a Survey From China.pdf: 1495437 bytes, checksum: 6902398e040d33392da1e0585ae9e0f6 (MD5); Made available in DSpace on 2026-03-11T10:11:09Z (GMT). No. of bitstreams: 1 Effects of a Flipped Classroom Learning System Integrated With ChatGPT on Students a Survey From China.pdf: 1495437 bytes, checksum: 6902398e040d33392da1e0585ae9e0f6 (MD5)

Towards Promoting the Culture of Sharing: Using Blockchain and Artificial Intelligence in an Open Science Platform

Towards Promoting the Culture of Sharing: Using Blockchain and Artificial Intelligence in an Open Science Platform Denden, Mouna; Abed, Mourad Several studies in the literature have proposed the use of artificial intelligence (AI) tools to manage big data and further enhance collaboration between researchers on open science platforms, hence promoting the culture of safely sharing reliable data. Moreover, some other studies further proposed the use of blockchain technology to secure data, provide transparency in data analysis, and also keep track of all collaborations within open science platforms. Despite the importance of AI and blockchain technology in open science platforms, no study, to the best of our knowledge, has implemented and discussed the benefits of using both technologies together or how blockchain can enhance AI systems in open science. Therefore, to address this research gap, this study presents a newly developed open science platform that harnesses the power of AI and blockchain technologies to promote and foster a culture of sharing and seamless collaboration among universities worldwide. This platform was then validated through focus group analysis from the European University for Customised Education (EUNICE) partners, which is the project context of this present study. The findings revealed that the use of AI and blockchain enabled researchers and institutions to share open science more effectively. Specifically, the use of AI features in Open REUNICE enhanced data management processes, particularly by improving metadata accuracy, searchability and reusability, thereby addressing critical needs in research workflows. Additionally, the use of Blockchain was found to play a critical role in addressing legal challenges and enhancing user trust. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T10:06:33Z No. of bitstreams: 1 Towards Promoting the Culture of Sharing Using Blockchain and Artificial Intelligence in an Open Science Platform.pdf: 1109690 bytes, checksum: 6f256836fc257589793f63badfd93289 (MD5); Made available in DSpace on 2026-03-11T10:06:33Z (GMT). No. of bitstreams: 1 Towards Promoting the Culture of Sharing Using Blockchain and Artificial Intelligence in an Open Science Platform.pdf: 1109690 bytes, checksum: 6f256836fc257589793f63badfd93289 (MD5)

Gaming as a Medium for the Expression of Citizens' Views on Environmental Dilemmas.

Gaming as a Medium for the Expression of Citizens' Views on Environmental Dilemmas. Griffiths, Dai; Ower, Jude; Hollins, Paul; Garg, Anchal The decline of traditional media and channels of communication has led to policymakers experiencing difficulty in understanding public sentiment. A case study was conducted to explore how games-based activities can be used to provide a link between citizens and policy makers. A system developed by PlanetPlay, and extended in the GREAT project, was used to embed a survey in the game SMITE. The intervention and survey questions were designed in collaboration with the United Nations Development Programme (UNDP) and the Hi-Rez game studio. The effectiveness of the infrastructure and the collaborative approach were demonstrated. The results revealed some significant differences in views on climate change between different age groups, genders, and education level. However, the data was heavily skewed towards males in the 18-35 age group, and to respondents in the United States, which limited the generalizability of the findings. It was concluded that in-game placement in collaboration with games studios is more effective than paid placement, and that a wider variety of games is needed to ensure that a study has an adequate range of respondent profiles. Finally, reflections are offered on the possible role of artificial intelligence in gathering such data. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T10:02:36Z No. of bitstreams: 1 Gaming as a Medium for the Expression of Citizens Views on Environmental Dilemmas.pdf: 557475 bytes, checksum: a751841e0440836bf9cf92a475c2c8c4 (MD5); Made available in DSpace on 2026-03-11T10:02:36Z (GMT). No. of bitstreams: 1 Gaming as a Medium for the Expression of Citizens Views on Environmental Dilemmas.pdf: 557475 bytes, checksum: a751841e0440836bf9cf92a475c2c8c4 (MD5)

Youth Expectations and Perceptions of Generative Artificial Intelligence in Higher Education

Youth Expectations and Perceptions of Generative Artificial Intelligence in Higher Education Cotino Arbelo, Andrea E.; González González, Carina S.; Molina Gil, Jezabel Artificial Intelligence (AI) is not a recent innovation, what’s new is how accessible its features have become across multiple devices, apps, and services. Sensationalistic news can distort public perception by exaggerating AI’s capabilities and risks. This leads to misconceptions and unrealistic expectations, causing misunderstandings about the true nature and limitation of these tools. Such distortions can undermine trust and hinder the effective adoption and integration of AI into society. This study aims to address this issue by exploring the expectations and perceptions of young individuals regarding Generative Artificial Intelligence (GAI) tools. It explores their understanding of GAI and related devices, such as virtual assistants, chatbots, and social robots, which can incorporate GAI. A total of N=100 university students engaged in this study by completing a digital questionnaire distributed through the virtual campus of the University of La Laguna. The quantitative analysis uncovered a significant gap in participants’ understanding of GAI terminology and its underlying mechanisms. Additionally, it shed light on a noteworthy gender based discrepancy in the expressed concerns. Participants commonly recognized their ability to communicate effectively with GAI, asserting that such interactions enhance their emotional well-being. Notably, virtual assistants and chatbots were perceived as more valuable tools compared to social robots within the educational realm. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T09:58:14Z No. of bitstreams: 1 Youth Expectations and Perceptions of Generative Artificial Intelligence in Higher Education.pdf: 799846 bytes, checksum: 2e0246100663ce629ce02052db2d1438 (MD5); Made available in DSpace on 2026-03-11T09:58:14Z (GMT). No. of bitstreams: 1 Youth Expectations and Perceptions of Generative Artificial Intelligence in Higher Education.pdf: 799846 bytes, checksum: 2e0246100663ce629ce02052db2d1438 (MD5)

Sentiment Analysis With Transformers Applied to Education: Systematic Review

Sentiment Analysis With Transformers Applied to Education: Systematic Review Pilicita Garrido, Anabel; Barra, Enrique Sentiment analysis, empowered by artificial intelligence, can play a critical role in assessing the impact of cultural factors on the advancement of Open Science and artificial intelligence. Additionally, it can offer valuable insights into the open data gathered within educational contexts. This article presents a systematic review of the use of Transformers models in sentiment analysis in education. A systematic review approach was used to analyze 41 articles from recognized digital databases. The results of the review provide a comprehensive understanding of previous research related to the use of Transformers models in education for the task of sentiment analysis, their benefits, challenges, as well as future areas of research that can lay the foundation for a more sustainable and effective education system. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T09:53:53Z No. of bitstreams: 1 Sentiment Analysis With Transformers Applied to Education Systematic Review.pdf: 626731 bytes, checksum: e9a28424b977fb97e38f6e4789327970 (MD5); Made available in DSpace on 2026-03-11T09:53:53Z (GMT). No. of bitstreams: 1 Sentiment Analysis With Transformers Applied to Education Systematic Review.pdf: 626731 bytes, checksum: e9a28424b977fb97e38f6e4789327970 (MD5)

AI Hallucinations? What About Human Hallucination?! Addressing Human Imperfection Is Needed for an Ethical AI

AI Hallucinations? What About Human Hallucination?! Addressing Human Imperfection Is Needed for an Ethical AI Tlili, Ahmed; Burgos, Daniel This study discusses how the human imperfection nature, also known as the human hallucination, could contribute to or emphasize technology (generally) and Artificial Intelligence (AI, particularly) hallucination. While the ongoing debate puts more efforts on improving AI for its ethical use, a shift should be made to also cover us, humans, who are the technology designer, developer, and user. Identifying and understanding the link between human and AI hallucination will ultimately help to develop effective and safe AI-powered systems that could have some positive societal impact in the long run. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T09:50:39Z No. of bitstreams: 1 AI Hallucinations What About Human Hallucination Addressing Human Imperfection Is Needed for an Ethical AI.pdf: 466298 bytes, checksum: 1bb9a9be820b29e6f6a1e9075c541f1e (MD5); Made available in DSpace on 2026-03-11T09:50:39Z (GMT). No. of bitstreams: 1 AI Hallucinations What About Human Hallucination Addressing Human Imperfection Is Needed for an Ethical AI.pdf: 466298 bytes, checksum: 1bb9a9be820b29e6f6a1e9075c541f1e (MD5)

Improved Fine-Tuned Reinforcement Learning From Human Feedback Using Prompting Methods for News Summarization

Improved Fine-Tuned Reinforcement Learning From Human Feedback Using Prompting Methods for News Summarization Pulari, Sini Raj; Umadevi, Maramreddy; Vasudevan, Shriram K. ChatGPT uses a generative pretrained transformer neural network model, which is under the larger umbrella of generative models. One major boom after ChatGPT is the advent of prompt engineering, which is the most critical part of ChatGPT that utilizes Large Language Models (LLM) and helps ChatGPT provide the desired outputs based on the style and tone of interactions carried out with it. Reinforcement learning from human feedback (RLHF) was used as the major aspect for fine-tuning LLM-based models. This work proposes a human selection strategy that is incorporated in the RLHF process to prevent undesirable consequences of the rightful choice of human reviewers for feedback. H-Rouge is a new metric proposed for humanized AI systems. A detailed evaluation of State-of-the-art summarization algorithms and prompt-based methods have been provided as part of the article. The proposed methods have introduced a strategy for human selection of RLHF models which employs multi-objective optimization to balance various goals encountered during the process with H-Rouge. This article will help nuance readers conduct research in the field of text summarization to start with prompt engineering in the summarization field, and future work will help them proceed in the right direction of research. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T09:47:13Z No. of bitstreams: 1 Improved Fine-Tuned Reinforcement Learning From Human Feedback Using Prompting Methods for News Summarization.pdf: 1439406 bytes, checksum: edccf242c6118f5570384935df8cc233 (MD5); Made available in DSpace on 2026-03-11T09:47:13Z (GMT). No. of bitstreams: 1 Improved Fine-Tuned Reinforcement Learning From Human Feedback Using Prompting Methods for News Summarization.pdf: 1439406 bytes, checksum: edccf242c6118f5570384935df8cc233 (MD5)

Aligning Figurative Paintings With Their Sources for Semantic Interpretation

Aligning Figurative Paintings With Their Sources for Semantic Interpretation Aslan, Sinem; Steels, Luc This paper reports steps in probing the artistic methods of figurative painters through computational algorithms. We explore a comparative method that investigates the relation between the source of a painting, typically a photograph or an earlier painting, and the painting itself. A first crucial step in this process is to find the source and to crop, standardize and align it to the painting so that a comparison becomes possible. The next step is to apply different low-level algorithms to construct difference maps for color, edges, texture, brightness, etc. From this basis, various subsequent operations become possible to detect and compare features of the image, such as facial action units and the emotions they signify. This paper demonstrates a pipeline we have built and tested using paintings by a renowned contemporary painter Luc Tuymans. We focus in this paper particularly on the alignment process, on edge difference maps, and on the utility of the comparative method for bringing out the semantic significance of a painting. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T09:43:30Z No. of bitstreams: 1 Aligning Figurative Paintings With Their Sources for Semantic Interpretation.pdf: 5986227 bytes, checksum: ed032401a3e4dfbcbb8bae7ff42d9e37 (MD5); Made available in DSpace on 2026-03-11T09:43:30Z (GMT). No. of bitstreams: 1 Aligning Figurative Paintings With Their Sources for Semantic Interpretation.pdf: 5986227 bytes, checksum: ed032401a3e4dfbcbb8bae7ff42d9e37 (MD5)

Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction

Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction Su, Zhan; Yu, Ruiyun; Zou, Shihao; Guo, Bingyang; Cheng, Li Human-Object Interaction (HOI) detection focuses on human-centered visual relationship detection, which is a challenging task due to the complexity and diversity of image content. Unlike most recent HOI detection works that only rely on paired instance-level information in the union range, our proposed Spatial-aware Multilevel Parsing Network (SMPNet) uses a multi-level information detection strategy, including instance-level visual features of detected human-object pair, part-level related features of the human body, and scene-level features extracted by the graph neural network. After fusing the three levels of features, the HOI relationship is predicted. We validate our method on two public datasets, V-COCO and HICO-DET. Compared with prior works, our proposed method achieves the state-of-the-art results on both datasets in terms of mAProle, which demonstrates the effectiveness of our proposed multi-level information detection strategy Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T09:40:02Z No. of bitstreams: 1 Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction.pdf: 8072831 bytes, checksum: f382c692472bfba9537e7a5ab5a4167d (MD5); Made available in DSpace on 2026-03-11T09:40:02Z (GMT). No. of bitstreams: 1 Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction.pdf: 8072831 bytes, checksum: f382c692472bfba9537e7a5ab5a4167d (MD5)

A Hybrid Multi-Person Fall Detection Scheme Based on Optimized YOLO and ST-GCN

A Hybrid Multi-Person Fall Detection Scheme Based on Optimized YOLO and ST-GCN Liu, Lei; Sun, Yeguo; Ge, Xianlei Human falls are a serious health issue for elderly and disabled people living alone. Studies have shown that if fallers could be helped immediately after a fall, it would greatly reduce their risk of death and the percentage of them requiring long-term treatment. As a real-time automatic fall detection solution, vision-based human fall detection technology has received extensive attention from researchers. In this paper, a hybrid model based on YOLO and ST-GCN is proposed for multi-person fall detection application scenarios. The solution uses the ST-GCN model based on a graph convolutional network to detect the fall action, and enhances the model with YOLO for accurate and fast recognition of multi-person targets. Meanwhile, our scheme accelerates the model through optimization methods to meet the model's demand for lightweight and real-time performance. Finally, we conducted performance tests on the designed prototype system and using both publicly available single-person datasets and our own multi-person dataset. The experimental results show that under better environmental conditions, our model possesses high detection accuracy compared to state-of-the-art schemes, while it significantly outperforms other models in terms of inference speed. Therefore, this hybrid model based on YOLO and ST-GCN, as a preliminary attempt, provides a new solution idea for multi-person fall detection for the elderly. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T09:36:18Z No. of bitstreams: 1 The Application of Deep Learning for Classification of Alzheimer's Disease Stages by Magnetic Resonance Imaging Data.pdf: 2311411 bytes, checksum: 0274fd67e29a8306be9a353c3585298e (MD5); Made available in DSpace on 2026-03-11T09:36:18Z (GMT). No. of bitstreams: 1 The Application of Deep Learning for Classification of Alzheimer's Disease Stages by Magnetic Resonance Imaging Data.pdf: 2311411 bytes, checksum: 0274fd67e29a8306be9a353c3585298e (MD5)

The Application of Deep Learning for Classification of Alzheimer's Disease Stages by Magnetic Resonance Imaging Data

The Application of Deep Learning for Classification of Alzheimer's Disease Stages by Magnetic Resonance Imaging Data Irfan, Muhammad; Shahrestani, Seyed; ElKhodr, Mahmoud Detecting Alzheimer’s disease (AD) in its early stages is essential for effective management, and screening for Mild Cognitive Impairment (MCI) is common practice. Among many deep learning techniques applied to assess brain structural changes, Magnetic Resonance Imaging (MRI) and Convolutional Neural Networks (CNN) have grabbed research attention because of their excellent efficiency in automated feature learning of a variety of multilayer perceptron. In this study, various CNNs are trained to predict AD on three different views of MRI images, including Sagittal, Transverse, and Coronal views. This research use T1-Weighted MRI data of 3 years composed of 2182 NIFTI files. Each NIFTI file presents a single patient's Sagittal, Transverse, and Coronal views. T1-Weighted MRI images from the ADNI database are first preprocessed to achieve better representation. After MRI preprocessing, large slice numbers require a substantial computational cost during CNN training. To reduce the slice numbers for each view, this research proposes an intelligent probabilistic approach to select slice numbers such that the total computational cost per MRI is minimized. With hyperparameter tuning, batch normalization, and intelligent slice selection and cropping, an accuracy of 90.05% achieve with the Transverse, 82.4% with Sagittal, and 78.5% with Coronal view, respectively. Moreover, the views are stacked together and an accuracy of 92.21% is achived for the combined views. In addition, results are compared with other studies to show the performance of the proposed approach for AD detection. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T09:32:21Z No. of bitstreams: 1 The Application of Deep Learning for Classification of Alzheimer's Disease Stages by Magnetic Resonance Imaging Data.pdf: 2311411 bytes, checksum: 0274fd67e29a8306be9a353c3585298e (MD5); Made available in DSpace on 2026-03-11T09:32:21Z (GMT). No. of bitstreams: 1 The Application of Deep Learning for Classification of Alzheimer's Disease Stages by Magnetic Resonance Imaging Data.pdf: 2311411 bytes, checksum: 0274fd67e29a8306be9a353c3585298e (MD5)

A Smart Healthcare System Using Consumer Electronics and Federated Learning to Automatically Diagnose Diabetic Foot Ulcers

A Smart Healthcare System Using Consumer Electronics and Federated Learning to Automatically Diagnose Diabetic Foot Ulcers Das, Sujit Kumar; Moparthi, Nageswara Rao; Namasudra, Suyel; González Crespo, Rubén; Taniar, David Privacy breaches on sensitive and widely distributed health data in consumer electronics (CE) demand novel strategies to protect privacy with correctness and proper operation maintenance. This work presents a scalable Federated Learning (FL) framework-based smart healthcare approach. Remote medical facilities frequently struggle with imbalanced datasets, including intermittent client connections to the FL global server. The proposed approach handled intermittent clients with diabetic foot ulcers (DFU) images. A data augmentation approach proposes to handle class imbalance problems during local model training. Also, a novel Convolutional Neural Network (CNN) architecture, ResKNet (K=4), is designed for client-side model training. The ResKNet is a sequence of distinctive residual blocks with 2D convolution, batch normalization, LeakyReLU activation, and skip connections (convolutional and identity). The proposed approach is evaluated for various client counts (5,10,15, and 20) and multiple test dataset sizes. The proposed framework can leverage consumer electronic devices and ensure secure data sharing among multiple sources. The potential of integrating the proposed approach with smartphones and wearable devices to provide highly secure data transmission is very high. The approach also helps medical institutions collaborate and develop a robust patient diagnostic model. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T09:28:05Z No. of bitstreams: 1 A Smart Healthcare System Using Consumer Electronics and Federated Learning to Automatically Diagnose Diabetic Foot Ulcers.pdf: 3506674 bytes, checksum: 08199520417a20c2f70a03c4565bf64f (MD5); Made available in DSpace on 2026-03-11T09:28:05Z (GMT). No. of bitstreams: 1 A Smart Healthcare System Using Consumer Electronics and Federated Learning to Automatically Diagnose Diabetic Foot Ulcers.pdf: 3506674 bytes, checksum: 08199520417a20c2f70a03c4565bf64f (MD5)

A Smart Healthcare System Using Consumer Electronics and Federated Learning to Automatically Diagnose Diabetic Foot Ulcers

A Smart Healthcare System Using Consumer Electronics and Federated Learning to Automatically Diagnose Diabetic Foot Ulcers Das, Sujit Kumar; Moparthi, Nageswara Rao; Namasudra, Suyel; González Crespo, Rubén; Taniar, David Privacy breaches on sensitive and widely distributed health data in consumer electronics (CE) demand novel strategies to protect privacy with correctness and proper operation maintenance. This work presents a scalable Federated Learning (FL) framework-based smart healthcare approach. Remote medical facilities frequently struggle with imbalanced datasets, including intermittent client connections to the FL global server. The proposed approach handled intermittent clients with diabetic foot ulcers (DFU) images. A data augmentation approach proposes to handle class imbalance problems during local model training. Also, a novel Convolutional Neural Network (CNN) architecture, ResKNet (K=4), is designed for client-side model training. The ResKNet is a sequence of distinctive residual blocks with 2D convolution, batch normalization, LeakyReLU activation, and skip connections (convolutional and identity). The proposed approach is evaluated for various client counts (5,10,15, and 20) and multiple test dataset sizes. The proposed framework can leverage consumer electronic devices and ensure secure data sharing among multiple sources. The potential of integrating the proposed approach with smartphones and wearable devices to provide highly secure data transmission is very high. The approach also helps medical institutions collaborate and develop a robust patient diagnostic model. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T09:22:15Z No. of bitstreams: 1 A Smart Healthcare System Using Consumer Electronics and Federated Learning to Automatically Diagnose Diabetic Foot Ulcers.pdf: 3506674 bytes, checksum: 08199520417a20c2f70a03c4565bf64f (MD5); Made available in DSpace on 2026-03-11T09:22:15Z (GMT). No. of bitstreams: 1 A Smart Healthcare System Using Consumer Electronics and Federated Learning to Automatically Diagnose Diabetic Foot Ulcers.pdf: 3506674 bytes, checksum: 08199520417a20c2f70a03c4565bf64f (MD5)

Editor’s Note

Editor’s Note Verdú, Elena The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) is a diamond open access journal which provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on artificial intelligence tools, theory, methodologies, systems, architectures integrating multiple technologies, problems including demonstrations of effectiveness, or tools that use AI with interactive multimedia techniques. The journal is supported by Universidad Internacional de La Rioja (UNIR) and by all those members of this multicultural community who, with a sense of commitment to the development of science, dedicate their knowledge and time to authoring, editing and reviewing tasks, and without whom this knowledge sharing project would not be possible. This regular issue begins with a series of five articles covering key advancements in the area of computing vision. In the following article, we move from the area of computer vision to another fast developing area, which is natural language processing (NLP). The following articles correspond to a monograph section on the Effects of Culture on Open Science and Artificial Intelligence in Education, compiled and edited by Tlili, Burgos and Kinshuk. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T09:12:36Z No. of bitstreams: 1 Editor’s Note.pdf: 59199 bytes, checksum: cf0c05a5b35d1993ade643aa8bf7eeb8 (MD5); Made available in DSpace on 2026-03-11T09:12:36Z (GMT). No. of bitstreams: 1 Editor’s Note.pdf: 59199 bytes, checksum: cf0c05a5b35d1993ade643aa8bf7eeb8 (MD5)

Evaluating Customer Segmentation Techniques in the Retail Sector

Evaluating Customer Segmentation Techniques in the Retail Sector Diyabi, Nur; Çakır, Duygu; Gül, Ömer Melih; Aytekin, Tevfik; Kadry, Seifedine In the current competitive corporate landscape, understanding client preferences and adapting marketing strategies accordingly has become crucial. This study evaluates the effectiveness of four machine learning algorithms (K-Means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Models (GMM), and Self-Organizing Maps (SOM)) for customer segmentation in the Turkish retail market. Two datasets were analyzed: a large-scale Turkish market sales dataset and a focused marketing campaign dataset. The research employed a comprehensive methodology encompassing data preparation, algorithm application, and performance evaluation using metrics such as the Calinski-Harabasz Index and Davies- Bouldin score. Results indicate that K-Means demonstrated superior performance in terms of interpretability and statistical validity. DBSCAN showed strengths in identifying non-spherical clusters, while GMM and SOM provided more granular segmentation. The findings offer actionable insights for Turkish retailers to optimize marketing strategies and enhance customer relationship management. This study contributes to the field of retail analytics by providing a methodological framework for evaluating customer segmentation techniques in specific market contexts. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T08:30:19Z No. of bitstreams: 1 Evaluating Customer Segmentation Techniques in the Retail Sector.pdf: 1344269 bytes, checksum: 24a95aafc7d1bc90e48f00971b2a85f4 (MD5); Made available in DSpace on 2026-03-11T08:30:19Z (GMT). No. of bitstreams: 1 Evaluating Customer Segmentation Techniques in the Retail Sector.pdf: 1344269 bytes, checksum: 24a95aafc7d1bc90e48f00971b2a85f4 (MD5)

A Multi-Session Evaluation of a Haptic Device in Normal and Critical Conditions: a Mars Analog Mission

A Multi-Session Evaluation of a Haptic Device in Normal and Critical Conditions: a Mars Analog Mission Manon, Julie; Vanderdonckt, Jean; Saint Guillain, Michael; Pletser, Vladimir; Wain, Cyril; Jacobs, Jean; Comein, Audrey; Drouet, Sirga; Meert, Julien; Sanchez Casla, Ignacio; Cartiaux, Olivier; Cornu, Olivier While visual interaction is typically evaluated as an instantaneous, one-shot activity that considers only a snapshot of factors, haptic interaction is more challenging to evaluate as it involves a continuous touch process evolving over time. To better understand how to evaluate haptic interaction, this paper performs a multisession evaluation of a haptic device to be used by astronauts in future lunar and Mars missions, based on eight factors. Three groups of two members (???? = 6 ) applied, either as operator or assistant, a newly developed external fixator (EZExFix) to fix a fracture of the tibial shaft. Astronauts had different levels of expertise, i.e., in anatomy, mechanical engineering, and without, and participated in eight timed runs. Among these eight matches, four sessions were conducted with different time frames and compared to a stress test, a reproduction of the experiment in very stressful conditions, and a session simulating critical conditions in an extra-vehicular activity. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T08:25:12Z No. of bitstreams: 1 A Multi-Session Evaluation of a Haptic Device in Normal and Critical Conditions.pdf: 877083 bytes, checksum: 85bc2ce8bad2a25e96029789a9e17eec (MD5); Made available in DSpace on 2026-03-11T08:25:12Z (GMT). No. of bitstreams: 1 A Multi-Session Evaluation of a Haptic Device in Normal and Critical Conditions.pdf: 877083 bytes, checksum: 85bc2ce8bad2a25e96029789a9e17eec (MD5)

Reliability of IBM’s Public Quantum Computers

Reliability of IBM’s Public Quantum Computers Pérez Antón, Raquel; Corbi, Alberto; López Sánchez, José Ignacio; Burgos, Daniel One of the challenges of the current ecosystem of quantum computers (QC) is the stabilization of the coherence associated with the entanglement of the states of their inner qubits. In this empirical study, we monitor the reliability of IBM’s public-access QCs network on a daily basis. Each of these state-of-the-art machines has a totally different qubit association, and this entails that for a given (same) input program, they may output a different set of probabilities for the assembly of results (including both the right and the wrong ones). Although we focus on the computing structure provided by the “Big Blue” company, our survey can be easily transferred to other currently available quantum mainframes. In more detail, we probe these quantum processors with an ad hoc designed computationally demanding quaternary search algorithm. As stated, this quantum program is executed every 24 hours (for nearly 100 days) and its goal is to put to the limit the operational capacity of this novel and genuine type of equipment. Next, we perform a comparative analysis of the obtained results according to the singularities of each computer and over the total number of executions. In addition, we subsequently apply (for 50 days) an improvement filtering to perform noise mitigation on the results obtained proposed by IBM. The Yorktown 5-qubit computer reaches noise filtering of up to 33% in one day, that is, a 90% confidence level is reached in the expected results. From our continuous and long-term tests, we derive that room still exists regarding the improvement of quantum calculators in order to guarantee enough confidence in the returned outcomes. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T08:16:47Z No. of bitstreams: 1 Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique.pdf: 1980156 bytes, checksum: 0aae57c42bf1ac2d4fbc69ed5fd20920 (MD5); Made available in DSpace on 2026-03-11T08:16:47Z (GMT). No. of bitstreams: 1 Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique.pdf: 1980156 bytes, checksum: 0aae57c42bf1ac2d4fbc69ed5fd20920 (MD5)

Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique

Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique Lakshmi, H. R.; Borra, Surekha With increasing copyright violation cases, watermarking of digital images is a very popular solution for securing online media content. Since some sensitive applications require image recovery after watermark extraction, reversible watermarking is widely preferred. This article introduces a Modified Quadratic Difference Expansion (MQDE) and fractal encryption-based reversible watermarking for securing the copyrights of images. First, fractal encryption is applied to watermarks using Tromino's L-shaped theorem to improve security. In addition, Cuckoo Search-Grey Wolf Optimization (CSGWO) is enforced on the cover image to optimize block allocation for inserting an encrypted watermark such that it greatly increases its invisibility. While the developed MQDE technique helps to improve coverage and visual quality, the novel data-driven distortion control unit ensures optimal performance. The suggested approach provides the highest level of protection when retrieving the secret image and original cover image without losing the essential information, apart from improving transparency and capacity without much tradeoff. The simulation results of this approach are superior to existing methods in terms of embedding capacity. With an average PSNR of 67 dB, the method shows good imperceptibility in comparison to other schemes. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T08:12:00Z No. of bitstreams: 1 Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique.pdf: 1980156 bytes, checksum: 0aae57c42bf1ac2d4fbc69ed5fd20920 (MD5); Made available in DSpace on 2026-03-11T08:12:00Z (GMT). No. of bitstreams: 1 Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique.pdf: 1980156 bytes, checksum: 0aae57c42bf1ac2d4fbc69ed5fd20920 (MD5)

Simulations for the Precise Modeling of Exercises Including Time, Grades and Number of Attempts

Simulations for the Precise Modeling of Exercises Including Time, Grades and Number of Attempts Jiménez-Macías, Alberto; Muñoz-Merino, Pedro J.; Delgado Kloos, Carlos Students’ interactions with exercises can reveal interesting features that can be used to redesign or effectively use the exercises during the learning process. The precise modeling of exercises includes how grades can evolve, depending on the number of attempts and time spent on the exercises. A missing aspect is how a precise relationship among grades, number of attempts, and time spent can be inferred from student interactions with exercises using machine learning methods, and how it differs depending on different factors. In this study, we analyzed the application of different machine-learning methods for modeling different scenarios by varying the probability of answering correctly, dataset sizes, and distributions. The results show that the model converged when the probability of random guessing was low. For exercises with an average of 2 attempts, the model converged to 200 interactions. However, increasing the number of interactions beyond 200 does not affect the accuracy of the model. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T08:08:17Z No. of bitstreams: 1 Simulations for the Precise Modeling of Exercises Including Time, Grades and Number of Attempts.pdf: 730266 bytes, checksum: c4f311227cef1c69af7b2270a0dabecc (MD5); Made available in DSpace on 2026-03-11T08:08:17Z (GMT). No. of bitstreams: 1 Simulations for the Precise Modeling of Exercises Including Time, Grades and Number of Attempts.pdf: 730266 bytes, checksum: c4f311227cef1c69af7b2270a0dabecc (MD5)

Learning Analytics Icons: Easy Comprehension of Data Treatment

Learning Analytics Icons: Easy Comprehension of Data Treatment Amo-Filva, Daniel; Alier, Marc; Fonseca, David; Garcia-Peñalvo, Francisco José; Casañ, María José The Learning Analytics approach adopted in education implies the gathering and processing of sensitive information and the generation of student profiles, which may have direct or indirect dire consequences for the students. The Educational institutions must manage this data processing according to the General Data Protection Regulation, respecting its principles of fairness when it comes to information gathering and processing. This implies that the students must be well informed and give explicit consent before their information is gathered and processed. The GDPR propose the usage of recognizable standardized icons to facilitate a general understanding and awareness of how personal data is deemed to be processed in each application context, like an online course. This paper presents a project that aims to provide a set of icons to inform about the treatment of educational data in the Learning Analytics processes and a survey about the student's comprehension of the icons, their meaning, and implications for their privacy and confidentiality. The result presented is a set of icons ready to be integrated into educational environments that apply Learning Analytics to increase transparency and facilitate the understanding of data processing. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T08:00:02Z No. of bitstreams: 1 Learning Analytics Icons Easy Comprehension of Data Treatment.pdf: 742924 bytes, checksum: d7cf786ac3a600774d92a70ed26dd70f (MD5); Made available in DSpace on 2026-03-11T08:00:02Z (GMT). No. of bitstreams: 1 Learning Analytics Icons Easy Comprehension of Data Treatment.pdf: 742924 bytes, checksum: d7cf786ac3a600774d92a70ed26dd70f (MD5)

An Effective Prediction Approach for the Management of Children Victims of Road Accidents

An Effective Prediction Approach for the Management of Children Victims of Road Accidents Saadi, F.; Atmani, B.; Henni, F.; Benfriha, H.; Addou, Z.; Guerbouz, R. Road traffic generates a considerable number of accidents each year. The management of injuries caused by these accidents is becoming a real public health problem. Faced with this latter, we propose a new clinical decision making approach based on case-based reasoning (CBR) and data mining (DM) techniques to speed up and improve the care of an injured child. The main idea is to preprocess the dataset before using K Nearest Neighbor (KNN) Classification Model. In this paper, an efficient predictive model is developed to predict the admission procedure of a child victim of a traffic accident in pediatric intensive care units. The evaluation of the proposed model is conducted on a real dataset elaborated by the authors and validated by statistical analysis. This novel model executes a selection of relevant attributes using data mining technique and integrates a CBR system to retrieve similar cases from an archive of cases of patients successfully treated with the proposed treatment plan. The results revealed that the proposed approach outperformed other models and the results of previous studies by achieving an accuracy of 91.66%. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:52:58Z No. of bitstreams: 1 An Effective Prediction Approach for the Management of Children Victims of Road Accidents.pdf: 456184 bytes, checksum: 3965144c96757b44a1f1e1382039e0ad (MD5); Made available in DSpace on 2026-03-10T16:52:58Z (GMT). No. of bitstreams: 1 An Effective Prediction Approach for the Management of Children Victims of Road Accidents.pdf: 456184 bytes, checksum: 3965144c96757b44a1f1e1382039e0ad (MD5)

Traffic Optimization Through Waiting Prediction and Evolutive Algorithms

Traffic Optimization Through Waiting Prediction and Evolutive Algorithms García, Francisco Traffic optimization systems require optimization procedures to optimize traffic light timing settings in order to improve pedestrian and vehicle mobility. Traffic simulators allow obtaining accurate estimates of traffic behavior by applying different timing configurations, but require considerable computational time to perform validation tests. For this reason, this project proposes the development of traffic optimizations based on the estimation of vehicle waiting times through the use of different prediction techniques and the use of this estimation to subsequently apply evolutionary algorithms that allow the optimizations to be carried out. The combination of these two techniques leads to a considerable reduction in calculation time, which makes it possible to apply this system at runtime. The tests have been carried out on a real traffic junction on which different traffic volumes have been applied to analyze the performance of the system. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:44:42Z No. of bitstreams: 1 Traffic Optimization Through Waiting Prediction and Evolutive Algorithms.pdf: 711723 bytes, checksum: c097d0a6f750ca2a64f026156e4c9f85 (MD5); Made available in DSpace on 2026-03-10T16:44:42Z (GMT). No. of bitstreams: 1 Traffic Optimization Through Waiting Prediction and Evolutive Algorithms.pdf: 711723 bytes, checksum: c097d0a6f750ca2a64f026156e4c9f85 (MD5)

Use of Data Mining for Intelligent Evaluation of Imputation Methods

Use of Data Mining for Intelligent Evaluation of Imputation Methods la Red Martínez, David L.; Primorac, Carlos R. In real-world situations, researchers frequently face the difficulty of missing values (MV), i.e., values not observed in a data set. Data imputation techniques allow the estimation of MV using different algorithms, by means of which important data can be imputed for a particular instance. Most of the literature in this field deals with different imputation methods. However, few studies deal with a comparative evaluation of the different methods as to provide more appropriate guidelines for the selection of the method to be applied to impute data for specific situations. The objective of this work is to show a methodology for evaluating the performance of imputation methods by means of new metrics derived from data mining processes, using quality metrics of data mining models. We started from the complete dataset that was amputated with different amputation mechanisms to generate 63 datasets with MV; these were imputed using Median, k-NN, k-Means and Hot-Deck imputation methods. The performance of the imputation methods was evaluated using new metrics derived from quality metrics of the data mining processes, performed with the original full file and with the imputed files. This evaluation is not based on measuring the error when imputing (usual operation), but on considering the similarity of the values of the quality metrics of the data mining processes obtained with the original file and with the imputed files. The results show that –globally considered and according to the new proposed metric, the imputation methods that showed the best performance were k-NN and k-Means. An additional advantage of the proposed methodology is that it provides predictive data mining models that can be used a posteriori. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:41:16Z No. of bitstreams: 1 Use of Data Mining for Intelligent Evaluation of Imputation Methods.pdf: 791527 bytes, checksum: 3cd9625f80fe5307b0d325d9a030d7a7 (MD5); Made available in DSpace on 2026-03-10T16:41:16Z (GMT). No. of bitstreams: 1 Use of Data Mining for Intelligent Evaluation of Imputation Methods.pdf: 791527 bytes, checksum: 3cd9625f80fe5307b0d325d9a030d7a7 (MD5)

Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data

Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data Galphade, Manisha; Nikam, V. B.; Banerjee, Biplab; Kiwelekar, Arvind W.; Sharma, Priyanka Currently, wind power is the fast growing area in the domain of renewable energy generation. Accurate prediction of wind power output in wind farms is crucial for addressing the challenges associated the power grid. This precise forecasting enables grid operators to enhance safety and optimize grid operations by effectively managing fluctuations in power generation, ensuring a reliable and stable energy supply. In recent years, there has been a significant rise in research and investigations conducted in this field. This study aims to develop a multivariate short-term wind power forecasting (WPF) model with the objective of enhancing forecasting precision. Among the various prediction models, deep learning models such as Long Short-Term Memory (LSTM) have demonstrated outstanding performance in the field of WPF. By adding multiple layers of LSTM networks, the model can capture more complex patterns. To improve the performance, data preprocessing is carried out using two techniques such as removal of missing values and imputing missing values using Random Forest Regressor (RFR). The comparison between the proposed Stacked LSTM model and other methods including vector autoregressive (VAR), Multiple Linear Regression, Gated Recurrent Unit (GRU) and Bidirectional LSTM (BiLSTM) has been experimented on two datasets. The experimental results show that after imputing missing values using RFR, the Stacked LSTM is optimized model for better performance than above mentioned reference models. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:37:45Z No. of bitstreams: 1 Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.pdf: 1302353 bytes, checksum: 4543961938eeff2158406c9a8ac0a471 (MD5); Made available in DSpace on 2026-03-10T16:37:45Z (GMT). No. of bitstreams: 1 Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.pdf: 1302353 bytes, checksum: 4543961938eeff2158406c9a8ac0a471 (MD5)

Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation

Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation Martínez Comesaña, Miguel; Martínez Torres, Javier; Javier, Pablo; López Gómez, Javier Optimising the use of the photovoltaic (PV) energy is essential to reduce fossil fuel emissions by increasing the use of solar power generation. In recent years, research has focused on physical simulations or artifical intelligence models attempting to increase the accuracy of PV generation predictions. The use of simulated data as pre-training for deep learning models has increased in different fields. The reasons are the higher efficiency in the subsequent training with real data and the possibility of not having real data available. This work presents a methodology, based on an deep learning model optimised with specific techniques and pre-trained with synthetic data, to estimate the generation of a PV system. A case study of a photovoltaic installation with 296 PV panels located in northwest Spain is presented. The results show that the model with proper pre-training trains six to seven times faster than a model without pre-training and three to four times faster than a model pre-trained with non-accurate simulated data. In terms of accuracy and considering a homogeneous training process, all models obtained average relative errors around 12%, except the model with incorrect pre-training which performs worse. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:33:56Z No. of bitstreams: 1 Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation.pdf: 874414 bytes, checksum: e985093a6df3c4486e1d497ba70f6f66 (MD5); Made available in DSpace on 2026-03-10T16:33:56Z (GMT). No. of bitstreams: 1 Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation.pdf: 874414 bytes, checksum: e985093a6df3c4486e1d497ba70f6f66 (MD5)

Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence

Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence Ordoñez, Hugo; Timarán Pereira, Ricardo; González Sanabria, Juan Sebastián Introduction: Currently, homelessness should not be seen as just another problem, but as a reality of inequality and the absence of social justice. In this sense, homeless people are subjected to social disengagement, lack of job opportunities or the instability of these, insecurity circumstances, these aspects being one of the causes associated with the consumption or addiction to psychoactive substances. Data: To define the proposed approach, data from the Census of Street Inhabitants - CHC- 2021 of the National Administrative Department of Statistics (DANE), which contains 19,375 records and 25 columns, were used. Methodology: This article presents an artificial intelligence approach that implements a model based on machine learning algorithms for identifying addiction trends to psychoactive substances in street dwellers in Colombia. Conclusions: Based on the results obtained, it is evident that the approach can serve as a support for decision making by municipal administrations in the definition of social public policies for the street-dwelling population in Colombia. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:30:02Z No. of bitstreams: 1 Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence.pdf: 565233 bytes, checksum: b4e896828cd0cfad07a43ea55b209154 (MD5); Made available in DSpace on 2026-03-10T16:30:02Z (GMT). No. of bitstreams: 1 Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence.pdf: 565233 bytes, checksum: b4e896828cd0cfad07a43ea55b209154 (MD5)

Explainable Artificial Intelligence-Based Diseases Diagnosis From Unstructured Clinical Data and Decision Making Using Blockchain Technologies

Explainable Artificial Intelligence-Based Diseases Diagnosis From Unstructured Clinical Data and Decision Making Using Blockchain Technologies M., Sumathi; Raja, S.P. In the digital era, health information is stored in digital form for easy maintenance, analysis and transfer. The proficiency of manual illness diagnosis and drug prediction in the medical field depends on the expertise availability, and experience of the specialists. In emergency and abnormal situation, the patient’s life completely depends on expert’s availability. Therefore, a different approach is needed to get around the difficulties in managing emergency cases. Artificial intelligence helps to take decisions in an accurate manner but does not provide the details of the decisions. The ability to treat emergency patients entirely depends on the particular hospitals. The clinical data includes numerical results, text prescriptions, scanned images, etc. Therefore, managing unstructured data with care is necessary for making clinical decisions. An explainable artificial intelligence-based disease diagnosis and blockchain-based decision-making system are presented in this work to address these challenges and improve patient care. A natural language processing system analyzes the unstructured data to identify different types of data and explainable AI diagnosis disease with justification and reason for the prediction. An ant colony optimization-based recommender system examines the predicted decision and identifies the specific drug for the disease. The disease decision and drug information are kept in a permissioned blockchain for confirmation. Decisions are validated by more than 50% of the experts present in the permissioned blockchain network, which consists of experts from various regions. As a result, the quickest and most accurate decisions possible are taken to handle emergency situations. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:26:30Z No. of bitstreams: 1 Explainable Artificial Intelligence-Based Diseases.pdf: 755211 bytes, checksum: 7b0d1912a5c5a87d6259ed890b6da20c (MD5); Made available in DSpace on 2026-03-10T16:26:30Z (GMT). No. of bitstreams: 1 Explainable Artificial Intelligence-Based Diseases.pdf: 755211 bytes, checksum: 7b0d1912a5c5a87d6259ed890b6da20c (MD5)

Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets

Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets Bobadilla, Jesús; Gutiérrez, Abraham The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datasets. We have tested the GANRS method by creating multiple synthetic datasets from three different real datasets taken as a source. Experiments include variations in the number of users in the synthetic datasets, as well as a different number of samples. We have also selected six state-of-the-art collaborative filtering deep learning models to test both their comparative performance and the GANRS method. The results show a consistent behavior of the generated datasets compared to the source ones; particularly, in the obtained values and trends of the precision and recall quality measures. The tested deep learning models have also performed as expected on all synthetic datasets, making it possible to compare the results with those obtained from the real source data. Future work is proposed, including different cold start scenarios, unbalanced data, and demographic fairness. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:22:47Z No. of bitstreams: 1 Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets.pdf: 1472576 bytes, checksum: 2860c05b019e926dcea84f05f95619de (MD5); Made available in DSpace on 2026-03-10T16:22:47Z (GMT). No. of bitstreams: 1 Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets.pdf: 1472576 bytes, checksum: 2860c05b019e926dcea84f05f95619de (MD5)

Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality

Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality Izquierdo Domenech, Juan; Linares Pellicer, Jordi; Ferri Molla, Isabel Augmented reality (AR) has become a powerful tool for assisting operators in complex environments, such as shop floors, laboratories, and industrial settings. By displaying synthetic visual elements anchored in real environments and providing information for specific tasks, AR helps to improve efficiency and accuracy. However, a common bottleneck in these environments is introducing all necessary information, which often requires predefined structured formats and needs more ability for multimodal and Natural Language (NL) interaction. This work proposes a new method for dynamically documenting complex environments using AR in a multimodal, non-structured, and interactive manner. Our method employs Large Language Models (LLMs) to allow experts to describe elements from the real environment in NL and select corresponding AR elements in a dynamic and iterative process. This enables a more natural and flexible way of introducing information, allowing experts to describe the environment in their own words rather than being constrained by a predetermined structure. Any operator can then ask about any aspect of the environment in NL to receive a response and visual guidance from the AR system, thus allowing for a more natural and flexible way of introducing and retrieving information. These capabilities ultimately improve the effectiveness and efficiency of tasks in complex environments. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:18:55Z No. of bitstreams: 1 Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality.pdf: 1472569 bytes, checksum: 751baa5d6dfe340da811523a6afd0c1e (MD5); Made available in DSpace on 2026-03-10T16:18:55Z (GMT). No. of bitstreams: 1 Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality.pdf: 1472569 bytes, checksum: 751baa5d6dfe340da811523a6afd0c1e (MD5)

Distinguishing Human From Machine: A Review of Advances and Challenges in AI-Generated Text Detection

Distinguishing Human From Machine: A Review of Advances and Challenges in AI-Generated Text Detection Fariello, Serena The rise of Large Language Models (LLMs) has dramatically altered the generation and spreading of textual content. This advancement offers benefits in various domains, including medicine, education, law, coding, and journalism, but also has negative implications, mainly related to ethical concerns. Preventing measures to mitigate negative implications pass through solutions that distinguish machine-generated text from humanwritten text. This study aims to provide a comprehensive review of existing literature for detecting LLMgenerated texts. Emerging techniques are categorized into five categories: watermarking, feature-based, neural-based, hybrid, and human-aided methods. For each introduced category, strengths and limitations are discussed, providing insights into their effectiveness and potential for future improvements. Moreover, available datasets and tools are introduced. Results demonstrate that, despite the good delimited performance, the multitude of languages to recognize, hybrid texts, the continuous improvement of algorithms for text generation and the lack of regulation require additional efforts for efficient detection. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:12:44Z No. of bitstreams: 1 Distinguishing Human From Machine A Review of Advances and Challenges in AI-Generated Text Detection.pdf: 465865 bytes, checksum: 9b07ebd124a70095cc94411c6a5a1a6d (MD5); Made available in DSpace on 2026-03-10T16:12:44Z (GMT). No. of bitstreams: 1 Distinguishing Human From Machine A Review of Advances and Challenges in AI-Generated Text Detection.pdf: 465865 bytes, checksum: 9b07ebd124a70095cc94411c6a5a1a6d (MD5)

Editor’s Note

Editor’s Note Morente Molinera, Juan Antonio The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) publishes articles discussing the latest current topics in the research literature. The emergence of ChatGPT and other similar models based on deep learning are dramatically changing the way people understand and use artificial intelligence. Despite the significant advances made in these types of techniques, which have been enormous in recent years, new learning methods are still needed. Specifically, we require methods that allow us to handle data correctly in specific environments, as well as provide learning methods with the necessary explainability that allows us to understand how they are reasoning. The latter is essential for creating ethical learning methods that do not make unfair decisions based on biased information. It is also important to identify data that have, in some way, reflected the reprehensible attitudes and reasoning that we as fallible human beings sometimes have. In short, artificial intelligence should reflect, if possible, the best of us rather than the worst. With this goal in mind, it is common to see in this issue of the journal an abundance of articles proposing new learning methods, many of which are based on Deep Learning and Data Mining. There are also articles on large language models, which are extremely important in the current artificial intelligence landscape. Of course, there are also articles on optimization methods and quantum computers, which are also of great importance in the field of artificial intelligence. Although generative artificial intelligence models are perhaps the ones that have people most intrigued, this is not the only current application of artificial intelligence. We are seeing how renewable energies, in particular those that come from the sun and wind, are playing an increasingly important role in global energy generation. As seen in recent events, such as the general blackout in Spain, the electricity system needs new methods that allow adequate regulation to prevent all kinds of possible failures. In this issue, two articles present new applications of artificial intelligence methods to renewable energy generation systems. Also noteworthy within this issue is the application of artificial intelligence in the field of teaching, where the aim is to provide a better learning experience for students and teachers. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:09:26Z No. of bitstreams: 1 Editor’s Note.pdf: 72460 bytes, checksum: d61494befc2e8cee6c6f9678d703da8b (MD5); Made available in DSpace on 2026-03-10T16:09:26Z (GMT). No. of bitstreams: 1 Editor’s Note.pdf: 72460 bytes, checksum: d61494befc2e8cee6c6f9678d703da8b (MD5)

Platform for Improving the User Experience in the Creation of Educational Multiplayer Video Games

Platform for Improving the User Experience in the Creation of Educational Multiplayer Video Games Sánchez Canella, Fernando; Pascual Espada, Jordán; Cid Rico, Irene Students’ motivation is one of the factors that directly affect academic performance. In recent years, teachers are looking for ways to motivate students during their training period. For example, making use of slides, videos, films, comics or games to increase students' motivation to improve their learning experience. Some research works have revealed that multiplayer games which include cooperation and competition, among other factors, are an extraordinary tool for enhancing students’ motivation. Current alternatives make it very complex for teachers to create multiplayer games for their students. The definition of the game requires many configurations and even technical knowledge. This research proposes a new platform that allows teachers to create multiplayer video games in a simple and fast way, improving the game creation process over current alternatives. The resulting games are also designed for to improve the student experience, and make it fun. These games do not only include trivia questions, but also use functional mechanisms from video games. The design of the generated games allows students to master the games in a short period of time during their classes. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T15:47:39Z No. of bitstreams: 1 Platform for Improving the User Experience in the Creation of Educational Multiplayer Video Games.pdf: 370917 bytes, checksum: 8ad91deb52bfcab9d34dacd6bc8afe31 (MD5); Made available in DSpace on 2026-03-10T15:47:39Z (GMT). No. of bitstreams: 1 Platform for Improving the User Experience in the Creation of Educational Multiplayer Video Games.pdf: 370917 bytes, checksum: 8ad91deb52bfcab9d34dacd6bc8afe31 (MD5)

Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation: the UEQ Family

Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation: the UEQ Family Kollmorgen, Jessica; Hinderks, Andreas; Thomaschewski, Jörg Measuring the user experience (UX) of products, systems and services is individual depending on the research question. On the one hand, the user’s goals and environment play a role in the subjective evaluation. On the other hand, different UX factors are relevant depending on the product. In this case, it is practical to have a questionnaire family as an aid, whose questionnaires are geared towards these different use cases. The User Experience Questionnaire (UEQ) family allows researchers and practitioners to choose the right tool for efficient UX measurement from three questionnaire versions. This article summarizes the UEQ, its short version (UEQ-S) and a modular framework (UEQ+) with overall 27 UX factors and purposes in over 30 different languages. In addition, specific instructions and assistance are provided for the statistical evaluation and interpretation of the questionnaire results. With the help of a key performance indicator (KPI), benchmarks and an importance-performance analysis (IPA), the realization of UX measurements is made easier for researchers and practitioners. To make it even more convenient to choose the right questionnaire from the UEQ family, influencing factors on the UX measurement and recommendations for action are given. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T15:44:06Z No. of bitstreams: 1 Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation.pdf: 434063 bytes, checksum: 002e410b3900c0fb3c7080e0981b38bd (MD5); Made available in DSpace on 2026-03-10T15:44:06Z (GMT). No. of bitstreams: 1 Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation.pdf: 434063 bytes, checksum: 002e410b3900c0fb3c7080e0981b38bd (MD5)

Combating Misinformation and Polarization in the Corporate Sphere: Integrating Social, Technological and AI Strategies

Combating Misinformation and Polarization in the Corporate Sphere: Integrating Social, Technological and AI Strategies Tejero, Alberto; Pisoni, Galena; Lashkari, Ziba Habibi; Rios Aguilar, Sergio In an era where misinformation and polarization present significant challenges, this research examines the root causes within social networks and assesses how corporations can use AI technologies for prompt detection. This research uses a dual approach: a "telephone game" with 225 participants from a Spanish university to study the spread of misinformation, and interviews with 15 experts from three French tech companies to investigate technological solutions. The findings indicate that almost one-third of participants inadvertently contribute to polarization, and around one-quarter propagated misinformation. The study also identifies the existing tools enhanced by AI and Machine Learning that effectively detect misinformation and polarization in corporate settings. This investigation provides crucial insights for practitioners to strengthen their strategies against misinformation and technical challenges and opportunities. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T15:39:48Z No. of bitstreams: 1 Combating Misinformation and Polarization in the Corporate Sphere.pdf: 309075 bytes, checksum: db6ffe3ce4ba578fd9d7392e4facc16b (MD5); Made available in DSpace on 2026-03-10T15:39:48Z (GMT). No. of bitstreams: 1 Combating Misinformation and Polarization in the Corporate Sphere.pdf: 309075 bytes, checksum: db6ffe3ce4ba578fd9d7392e4facc16b (MD5)

Automatic Surveillance of People and Objects on Railway Tracks

Automatic Surveillance of People and Objects on Railway Tracks Martínez Núñez, Domingo; López Hernández, Fernando Carlos; Rainer Granados, J. Javier This paper describes the development and evaluation of a surveillance system for the detection of people and objects on railroad tracks in real time. Firstly, the paper evaluates several background subtraction techniques including CNNs and the object detection library called YOLO. Then we describe a novel strategy to mitigate the occlusion caused by the perspective of the camera and the integration of an alarms and pre-alarms policy. To evaluate its performance, we have implemented and automated the control and notification aspects of the surveillance system using computer vision techniques. This setup, running on a standard PC, achieves an average frame rate of 15 FPS and a latency of 0.54 seconds per frame, meeting real-time expectations in terms of both false alarms and precision in operational mode. The results from experiments conducted with a publicly available recorded video dataset from Metro de Madrid facilities demonstrate significant improvements over current state-of the-art solutions. These improvements include better accident anticipation and enhanced information provided to the operator using a standard low-cost camera. Consequently, we conclude that the approach described in this paper is both effective and a more practical, cost-efficient alternative to the other solutions reviewed. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T15:10:54Z No. of bitstreams: 1 Automatic Surveillance of People and Objects on Railway Tracks.pdf: 471901 bytes, checksum: 95951b5a4386c4fbc12f3570d95c43f2 (MD5); Made available in DSpace on 2026-03-10T15:10:54Z (GMT). No. of bitstreams: 1 Automatic Surveillance of People and Objects on Railway Tracks.pdf: 471901 bytes, checksum: 95951b5a4386c4fbc12f3570d95c43f2 (MD5)

Multiscale Attentional Squeeze-And-Excitation Network for Person Re-Identification

Multiscale Attentional Squeeze-And-Excitation Network for Person Re-Identification Guo, Tiancun; Zhou, Qiang; Gao, Mingliang; Jeon, Gwanggil; Camacho, David In recent years, with the advancement of deep learning, person re-identification (Re-ID) has become increasingly significant. The existing person Re-ID methods primarily focus on optimizing network architecture to enhance Re-ID task performance. However, these methods often overlook the importance of valuable features in distinguishing Re-ID tasks, leading to reduced model efficacy in complex scenarios. As a solution, we utilize the attention mechanism to develop the lightweight multiscale Attentional Squeeze-and-Excitation Network (MASENet) that can distinguish between significant and non-significant features. Specifically, we utilize the SEAttention (SE) module to amplify important feature channels and suppress redundant ones. Additionally, the Spatial Group Enhance (SGE) module is introduced to enable networks to enhance semantic learning expression and suppress potential noise autonomously. We conduct comprehensive experiments on Market1501, MSMT17, and VeRi-776 datasets and cross-domain experiments on MSMT17 Ñ Market1501 to validate the model performance. Experimental results prove that the proposed MASENet achieves competitive performance across all experiments. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T15:01:51Z No. of bitstreams: 1 Multiscale Attentional Squeeze-And-Excitation Network for Person Re-Identification.pdf: 736449 bytes, checksum: 34ea53121002bf315ec3e13ad6571854 (MD5); Made available in DSpace on 2026-03-10T15:01:51Z (GMT). No. of bitstreams: 1 Multiscale Attentional Squeeze-And-Excitation Network for Person Re-Identification.pdf: 736449 bytes, checksum: 34ea53121002bf315ec3e13ad6571854 (MD5)

Prediction of COVID-19 Using a Clinical Dataset With Machine Learning Approaches

Prediction of COVID-19 Using a Clinical Dataset With Machine Learning Approaches Suruliandi, A.; Rayan, R. Ame; Raja, S. P. COVID-19 is an infectious disease that spreads quickly from person to another. The pandemic, which spread worldwide over time, presents huge risks in terms of blood clotting, breathing problems and heart attacks, sometimes with fatal consequences if not detected early. The PCR test, CT scans, X-rays, and blood tests are methods commonly employed to detect the disease, though the PCR test is, without question, considered the gold standard. The American Center for Disease Control and Prevention (CDC) reports that the PCR has an 80% accuracy rate. An alternative to the PCR is clinical data, which is less expensive, easy to collect, and offers better accuracy. Machine learning, with its rich feature selection and classification methods, helps detect COVID-19 at the earliest stages, using clinical test results. This research proposes a clinical dataset and offers a comparative analysis of feature selection and classification algorithms for detecting COVID-19. Filter-based feature selection methods such as the ANOVA-F, chi-square, mutual information and Pearson correlation, along with wrapperbased methods such as Recursive Feature Elimination (RFE) and Sequential Forward Selection (SFS) were used to choose a subset of features from the feature set. The selected features were thereafter applied to the Support Vector Machine (SVM), Naïve Bayes, K-NN (K-Nearest Neighbor) and Logistic Regression(LR) classification algorithms to detect Coronavirus Disease. The experimental results of the comparative study show that the clinical dataset provides better accuracy at 94.8%, with mutual information and the SVM classifier. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T14:57:49Z No. of bitstreams: 1 Prediction of COVID-19 Using a Clinical Dataset With Machine Learning Approaches.pdf: 772806 bytes, checksum: 1a4176226460cd5514e73a1c0da4a6e1 (MD5); Made available in DSpace on 2026-03-10T14:57:49Z (GMT). No. of bitstreams: 1 Prediction of COVID-19 Using a Clinical Dataset With Machine Learning Approaches.pdf: 772806 bytes, checksum: 1a4176226460cd5514e73a1c0da4a6e1 (MD5)

TKU-PSO: An Efficient Particle Swarm Optimization Model for Top-K High-Utility Itemset Mining

TKU-PSO: An Efficient Particle Swarm Optimization Model for Top-K High-Utility Itemset Mining Carstensen, Simen; Lin, Jerry Chun Wei Top-k high-utility itemset mining (top- HUIM) is a data mining procedure used to identify the most valuable patterns within transactional data. Although many algorithms are proposed for this purpose, they require substantial execution times when the search space is vast. For this reason, several meta-heuristic models have been applied in similar utility mining problems, particularly evolutionary computation (EC). These algorithms are beneficial as they can find optimal solutions without exploring the search space exhaustively. However, there are currently no evolutionary heuristics available for top-k HUIM. This paper addresses this issue by proposing an EC-based particle swarm optimization model for top-k HUIM, which we call TKU-PSO. In addition, we have developed several strategies to relieve the computational complexity throughout the algorithm. First, redundant and unnecessary candidate evaluations are avoided by utilizing explored solutions and estimating itemset utilities. Second, unpromising items are pruned during execution based on a thresholdraising concept we call minimum solution fitness. Finally, the traditional population initialization approach is revised to improve the model’s ability to find optimal solutions in huge search spaces. Our results show that TKU-PSO is faster than state-of-the-art competitors in all datasets tested. Most notably, existing algorithms could not complete certain experiments due to excessive runtimes, whereas our model discovered the correct solutions within seconds. Moreover, TKU-PSO achieved an overall accuracy of 99.8% compared to 16.5% with the current heuristic approach, while memory usage was the smallest in 2/3 of all tests. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T14:48:57Z No. of bitstreams: 1 TKU-PSO An Efficient Particle Swarm Optimization Model for Top-K High-Utility Itemset Mining.pdf: 797963 bytes, checksum: 65eb3b395f4ae5aa6076d01176e325c4 (MD5); Made available in DSpace on 2026-03-10T14:48:57Z (GMT). No. of bitstreams: 1 TKU-PSO An Efficient Particle Swarm Optimization Model for Top-K High-Utility Itemset Mining.pdf: 797963 bytes, checksum: 65eb3b395f4ae5aa6076d01176e325c4 (MD5)

Optimal Target-Oriented Knowledge Transportation For Aspect-Based Multimodal Sentiment Analysis

Optimal Target-Oriented Knowledge Transportation For Aspect-Based Multimodal Sentiment Analysis Zhang, Linhao; Jin, Li; Xu, Guangluan; Li, Xiaoyu; Sun, Xian; Zhang, Zequn; Zhang, Yanan Aspect-based multimodal sentiment analysis under social media scenario aims to identify the sentiment polarities of each aspect term, which are mentioned in a piece of multimodal user-generated content. Previous approaches for this interdisciplinary multimodal task mainly rely on coarse-grained fusion mechanisms from the data-level or decision-level, which have the following three shortcomings:(1) ignoring the category knowledge of the sentiment target mentioned in the text) in visual information. (2) unable to assess the importance of maintaining target interaction during the unimodal encoding process, which results in indiscriminative representations considering various aspect terms. (3) suffering from the semantic gap between multiple modalities. To tackle the above challenging issues, we propose an optimal target-oriented knowledge transportation network (OtarNet) for this task. Firstly, the visual category knowledge is explicitly transported through input space translation and reformulation. Secondly, with the reformulated knowledge containing the target and category information, the target sensitivity is well maintained in the unimodal representations through a multistage target-oriented interaction mechanism. Finally, to eliminate the distributional modality gap by integrating complementary knowledge, the target-sensitive features of multiple modalities are implicitly transported based on the optimal transport interaction module. Our model achieves state-of-theart performance on three benchmark datasets: Twitter-15, Twitter-17 and Yelp, together with the extensive ablation study demonstrating the superiority and effectiveness of OtarNet. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T14:44:20Z No. of bitstreams: 1 Optimal Target-Oriented Knowledge Transportation For Aspect-Based Multimodal Sentiment Analysis.pdf: 1152006 bytes, checksum: 28abb3788caafe9c124f2fc62b97bf5d (MD5); Made available in DSpace on 2026-03-10T14:44:20Z (GMT). No. of bitstreams: 1 Optimal Target-Oriented Knowledge Transportation For Aspect-Based Multimodal Sentiment Analysis.pdf: 1152006 bytes, checksum: 28abb3788caafe9c124f2fc62b97bf5d (MD5)

A Robust Framework for Speech Emotion Recognition Using Attention Based Convolutional Peephole LSTM

A Robust Framework for Speech Emotion Recognition Using Attention Based Convolutional Peephole LSTM Paramasivam, Ramya; Lavanya, K.; Divakarachari, Parameshachari Bidare; Camacho, David Speech Emotion Recognition (SER) plays an important role in emotional computing which is widely utilized in various applications related to medical, entertainment and so on. The emotional understanding improvises the user machine interaction with a better responsive nature. The issues faced during SER are existence of relevant features and increased complexity while analyzing of huge datasets. Therefore, this research introduces a wellorganized framework by introducing Improved Jellyfish Optimization Algorithm (IJOA) for feature selection, and classification is performed using Convolutional Peephole Long Short-Term Memory (CP-LSTM) with attention mechanism. The raw data acquisition takes place using five datasets namely, EMO-DB, IEMOCAP, RAVDESS, Surrey Audio-Visual Expressed Emotion (SAVEE) and Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D). The undesired partitions are removed from the audio signal during pre-processing and fed into phase of feature extraction using IJOA. Finally, CP LSTM with attention mechanisms is used for emotion classification. As the final stage, classification takes place using CP-LSTM with attention mechanisms. Experimental outcome clearly shows that the proposed CP-LSTM with attention mechanism is more efficient than existing DNN-DHO, DH-AS, D-CNN, CEOAS methods in terms of accuracy. The classification accuracy of the proposed CP-LSTM with attention mechanism for EMO-DB, IEMOCAP, RAVDESS and SAVEE datasets are 99.59%, 99.88%, 99.54% and 98.89%, which is comparably higher than other existing techniques. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T13:02:22Z No. of bitstreams: 1 A Robust Framework for Speech Emotion Recognition Using Attention Based Convolutional Peephole LSTM.pdf: 702337 bytes, checksum: 0a1e512c40ee7c108ce218b4af7828a0 (MD5); Made available in DSpace on 2026-03-10T13:02:22Z (GMT). No. of bitstreams: 1 A Robust Framework for Speech Emotion Recognition Using Attention Based Convolutional Peephole LSTM.pdf: 702337 bytes, checksum: 0a1e512c40ee7c108ce218b4af7828a0 (MD5)

Predicting Tree Growth and Transpiration in Forests: An Analysis of a Small-Scale Dataset With Pareto Optimized Tsaug Augmentation

Predicting Tree Growth and Transpiration in Forests: An Analysis of a Small-Scale Dataset With Pareto Optimized Tsaug Augmentation Maskeliūnas, Rytis; Damaševičius, Robertas; Odusam, Modupe; Sidabrienė, Diana; Augustaitis, Algirdas; Mozgeris, Gintautas The study demonstrates the potential of specifically developed data augmentation in estimating tree growth and transpiration by emphasizing the influence of environmental variables, such as photosynthetically active radiation (PAR), air temperature, and relative humidity—on tree growth predictions. The investigation utilizes data obtained from two hemi-boreal semi-natural mixed conifer deciduous forest sites in the Aukstaitija National Park in Lithuania. Field measurements included xylem sap flow measurements and stem circumference increment growth. The dataset utilized in the analysis consisted of four trees per species and contained information on tree growth, transpiration, and solar angle measurements. Pareto-optimized Tsaug augmentation techniques were employed to diversify the dataset, generating augmented time series to improve diversity and minimize distortion. The results of the correlation analysis indicated significant relationships between environmental variables and tree growth and transpiration. The Prophet based prediction model, notably when trained with augmented data, outperformed other models in predicting tree growth and perspiration variables (MAPE ranging from 0.0017 to 0.01). This was particularly evident for FACP, FAGP, and FADP variables, showcasing substantial improvement with augmented data. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T08:10:32Z No. of bitstreams: 2 Predicting Tree Growth and Transpiration in Forests.pdf: 6350547 bytes, checksum: ffc4a4d5d11641ba99b287683fe72a1d (MD5) Predicting Tree Growth and Transpiration in Forests.pdf: 6350547 bytes, checksum: ffc4a4d5d11641ba99b287683fe72a1d (MD5); Made available in DSpace on 2026-03-10T08:10:32Z (GMT). No. of bitstreams: 2 Predicting Tree Growth and Transpiration in Forests.pdf: 6350547 bytes, checksum: ffc4a4d5d11641ba99b287683fe72a1d (MD5) Predicting Tree Growth and Transpiration in Forests.pdf: 6350547 bytes, checksum: ffc4a4d5d11641ba99b287683fe72a1d (MD5)

An Adaptive Salp-Stochastic-Gradient-Descent-Based Convolutional LSTM With MapReduce Framework for the Prediction of Rainfall

An Adaptive Salp-Stochastic-Gradient-Descent-Based Convolutional LSTM With MapReduce Framework for the Prediction of Rainfall Manoj, S. Oswalt; Kumar, Abhishek; Dubey, Ashutosh Kumar; Ananth, J. P. Rainfall prediction is considered to be an esteemed research area that impacts the day-to-day life of Indians. The predominant income source of most of the Indian population is agriculture. It helps the farmers to make the appropriate decisions pertaining to cultivation and irrigation. The primary objective of this investigation is to develop a technique for rainfall prediction utilising the MapReduce framework and the convolutional long short-term memory (ConvLSTM) method to circumvent the limitations of higher computational requirements and the inability to process a large number of data points. In this work, an adaptive salp-stochastic-gradientdescent-based ConvLSTM (adaptive S-SGD-based ConvLSTM) system has been developed to predict rainfall accurately to process the long time series data and to eliminate the vanishing problems. To optimize the hyperparameter of the convLSTM model, the S-SGD methodology proposed combine the SGD and the salp swarm algorithm (SSA). The adaptive S-SGD based ConvLSTM has been developed by integrating the adaptive concept in S-SGD. It tunes the weights of ConvLSTM optimally to achieve better prediction accuracy. Assessment measures, such as the percentage root mean square difference (PRD) and mean square error (MSE), were employed to compare the suggested method with previous approaches. The developed system demonstrates high prediction accuracy, achieving minimal values for MSE (0.0042) and PRD (0.8450). Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T16:56:05Z No. of bitstreams: 1 An Adaptive Salp-Stochastic-Gradient-Descent-Based Convolutional LSTM With MapReduce Framework for the Prediction of Rainfall.pdf: 840750 bytes, checksum: ab83fa3761d1e8fbb514b1de528ecfe6 (MD5); Made available in DSpace on 2026-03-09T16:56:05Z (GMT). No. of bitstreams: 1 An Adaptive Salp-Stochastic-Gradient-Descent-Based Convolutional LSTM With MapReduce Framework for the Prediction of Rainfall.pdf: 840750 bytes, checksum: ab83fa3761d1e8fbb514b1de528ecfe6 (MD5)

Measuring the Difference Between Pictures From Controlled and Uncontrolled Sources to Promote a Destination. A Deep Learning Approach

Measuring the Difference Between Pictures From Controlled and Uncontrolled Sources to Promote a Destination. A Deep Learning Approach Diaz Pacheco, AngeL; Álvarez Carmona, Miguel A.; Rodríguez González, Ansel Y.; Carlos, Hugo; Aranda, Ramón Promoting a destination is a major task for Destination Marketing Organizations (DMOs). Although DMOs control, to some extent, the information presented to travelers (controlled sources), there are other different sources of information (uncontrolled sources) that could project an unfavorable image of the destination. Measuring differences between information sources would help design strategies to mitigate negative factors. In this way, we propose a deep learning-based approach to automatically measure the changes between images from controlled and uncontrolled information sources. Our approach exempts experts from the time-consuming task of assessing enormous quantities of pictures to track changes. To our best knowledge, this work is the first work that focuses on this issue using technological paradigms. Notwithstanding this, our approach paves novel pathways to acquire strategic insights that can be harnessed for the augmentation of destination development, the refinement of recommendation systems, the analysis of online travel reviews, and myriad other pertinent domains. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T16:52:08Z No. of bitstreams: 1 Measuring the Difference Between Pictures From Controlled and Uncontrolled Sources to Promote a Destination. A Deep Learning Approach.pdf: 957662 bytes, checksum: 14760a4d6ccbf933247a0c428df68e6a (MD5); Made available in DSpace on 2026-03-09T16:52:08Z (GMT). No. of bitstreams: 1 Measuring the Difference Between Pictures From Controlled and Uncontrolled Sources to Promote a Destination. A Deep Learning Approach.pdf: 957662 bytes, checksum: 14760a4d6ccbf933247a0c428df68e6a (MD5)

Performance and Communication Cost of Deep Neural Networks in Federated Learning Environments: An Empirical Study

Performance and Communication Cost of Deep Neural Networks in Federated Learning Environments: An Empirical Study Alotaibi, Basmah K.; Khan, Fakhri Alam; Qawqzeh, Yousef; Jeon, Gwanggil; Camacho, David Federated learning, a distributive cooperative learning approach, allows clients to train the model locally using their data and share the trained model with a central server. When developing a federated learning environment, a deep/machine learning model needs to be chosen. The choice of the learning model can impact the model performance and the communication cost since federated learning requires the model exchange between clients and a central server in several rounds. In this work, we provide an empirical study to investigate the impact of using three different neural networks (CNN, VGG, and ResNet) models in image classification tasks using two different datasets (Cifar-10 and Cifar-100) in a federated learning environment. We investigate the impact of using these models on the global model performance and communication cost under different data distribution that are IID data and non-IID data distribution. The obtained results indicate that using CNN and ResNet models provide a faster convergence than VGG model. Additionally, these models require less communication costs. In contrast, the VGG model necessitates the sharing of numerous bits over several rounds to achieve higher accuracy under the IID data settings. However, its accuracy level is lower under non-IID data distributions than the other models. Furthermore, using a light model like CNN provides comparable results to the deeper neural network models with less communication cost, even though it may require more communication rounds to achieve the target accuracy in both datasets. CNN model requires fewer bits to be shared during communication than other models. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T16:48:22Z No. of bitstreams: 1 Performance and Communication Cost of Deep Neural Networks in Federated Learning Environments An Empirical Study.pdf: 794820 bytes, checksum: f3c58e1d4566710f4a58038bd98a794e (MD5); Made available in DSpace on 2026-03-09T16:48:22Z (GMT). No. of bitstreams: 1 Performance and Communication Cost of Deep Neural Networks in Federated Learning Environments An Empirical Study.pdf: 794820 bytes, checksum: f3c58e1d4566710f4a58038bd98a794e (MD5)

Editor’s Note

Editor’s Note García Martínez Eyre i Canals, Yamila Artificial Intelligence (AI) is a scientific discipline that aims to drive disruptive scenarios for science-based technical developments that solve complex problems. The IJIMAI journal’s scope is precisely to demonstrate how the combination of two factors — technical foundations and sought-after applications — must guide future AI developments to find solutions to complex real-world problems. This IJIMAI publication opens with an article that considers the current framework for AI fundamentals: how can we improve AI technology to find solutions to real-unsolved problems? The initial answer seems to be related with a desired self-consistent procedure: let machines learn from our experience. In the article by Alotaibi et al., the analysis of neural networks in terms of the parameters used, how they work, and how do they respond to the problem itself led the authors to a rationale for decision-making regarding the performance of different neural models. The immediate question that arises is whether there are any universal and fundamental criteria that can be used to define the models that guide AI methods. Apparently, there are not such universal methods, and we are faced with a challenging open problem. Subsequent manuscripts will provide readers with more in-depth insights into this issue. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T16:43:57Z No. of bitstreams: 1 Editor’s Note.pdf: 72460 bytes, checksum: d61494befc2e8cee6c6f9678d703da8b (MD5); Made available in DSpace on 2026-03-09T16:43:57Z (GMT). No. of bitstreams: 1 Editor’s Note.pdf: 72460 bytes, checksum: d61494befc2e8cee6c6f9678d703da8b (MD5)

AI Powered Commentary and Camera Direction in E-Sports

AI Powered Commentary and Camera Direction in E-Sports Narayanan, Swathi Jamjal; Joseph, Kevin Winston; Sirohi, Devansh; Chaudhary, Harsh; Shivkumar, Hitesh Real-time, AI-driven commentary and camera direction provide revolutionary possibilities to improve spectator engagement and comprehension of live events in the rapidly advancing world of e-sports. This paper proposes an autonomous system designed to both generate dynamic commentary as well as control the spectator camera for live-streamed e-sports matches, specifically focusing on League of Legends (LoL), a popular Multiplayer Online Battle Arena (MOBA) game. It incorporates the use of GPT-4o with Vision and OpenAI’s TTS API. Synchronization of commentary with real-time camera movements is one of the major challenges tackled. This is done using a camera tracking and scene change detection algorithm that effectively adjusts the commentary to changing scenes in real-time by utilizing computer vision techniques. Further, two neural architectures for AI-driven camera control: a 2D Convolutional-LSTM (Conv-LSTM) model that concentrates on independent spatial and temporal analysis, and a 3D CNN model that combines these features to forecast camera movements in a more comprehensive way are presented. Evaluations on fluency, relevance, and strategic depth metrics, show that our integrated system improves viewer experience by providing deep and coherent narratives that are contextually aligned with the game dynamics. The proposed models are evaluated quantitatively in capturing spectator camera movement patterns. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T16:21:30Z No. of bitstreams: 1 AI Powered Commentary and Camera Direction in E-Sports.pdf: 3709678 bytes, checksum: aed05a810871b511f14e69d84e5d5236 (MD5); Made available in DSpace on 2026-03-09T16:21:30Z (GMT). No. of bitstreams: 1 AI Powered Commentary and Camera Direction in E-Sports.pdf: 3709678 bytes, checksum: aed05a810871b511f14e69d84e5d5236 (MD5)

The Application of Large Language Models and Virtual Assistants in Project Management Research: A Review

The Application of Large Language Models and Virtual Assistants in Project Management Research: A Review Gil Ruiz, Jesús; Zayas-Gallardo, Javier; Díaz Rodríguez, Hernán The rapid evolution of generative artificial intelligence (AI) is transforming project management practices by enhancing efficiency, productivity and adaptability in decision-making processes. The integration of large language models (LLMs) into project management research and practice is reviewed, with a particular focus on virtual assistants as decision support tools. State-of-the-art models such as Mistral, Large Language Model Meta AI (LLaMa), Bidirectional Encoder Representations from Transformers (BERT) and T5, are assessed for their potential to automate complex project tasks, extract insights from project datasets, and optimize decision-making across various project management domains and business sectors. Generative AI is shown to surpass traditional project management systems by not only analysing historical project data but also generating new strategies and solutions in real time. Applications include project risk assessment, resource allocation optimisation, stakeholder communication and project performance prediction. The role of fine-tuning and retraining LLMs to adapt them to industry-specific project management challenges is also examined enhancing relevance and performance across diverse business environments. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T15:06:11Z No. of bitstreams: 1 The Application of Large Language Models and Virtual Assistants in Project Management Research.pdf: 652904 bytes, checksum: 50aafbb737b9685e39f76a1e23b5e7dd (MD5); Made available in DSpace on 2026-03-09T15:06:11Z (GMT). No. of bitstreams: 1 The Application of Large Language Models and Virtual Assistants in Project Management Research.pdf: 652904 bytes, checksum: 50aafbb737b9685e39f76a1e23b5e7dd (MD5)

Blending Language Models and Domain-Specific Languages in Computer Science Education. A Case Study on API RESTFul

Blending Language Models and Domain-Specific Languages in Computer Science Education. A Case Study on API RESTFul Jurado, Francisco; Rodríguez, Francy D.; Chavarriaga, Enrique; Rojas, Luis Since Computer Science students are used to applying both General Purpose Programming Languages (GPPLs) and Domain-Specific Languages (DSLs), Generative Artificial Intelligence based on Language Models (LMs) can help them on automatic tasks, allowing them to focus on more creative tasks and higher skills. However, the teaching and evaluation of technical tasks in Computer Science can be inefficient and prone to errors. Thus, the main objective of this article is to explore the performance of LMs compared to that of undergraduate Computer Science students in a specific case study: designing and implementing RESTful APIs DSLs. This research aims to determine if LMs can enhance the efficiency and accuracy of these processes. Our case study involved 39 students and 5 different LMs that must use the two DSLs we also designed for their task assignment. To evaluate performance, we applied uniform criteria to student and LMs-generated solutions, enabling a comparative analysis of accuracy and effectiveness. With a case study comparing performance between students and LMs, this article contributes to checking to what extent LMs are able to carry out software development tasks involving the use of new DSLs specially designed for highly specific settings in a similar way as well-qualified Computer Science students are able to. The results underscore the importance of welldefined DSLs and effective prompting processes for optimal LM performance. Specifically, LMs demonstrated high variability in task execution, with two GPT-based LMs achieving similar grades to those scored by the best of the students for every task, obtaining 0.78 and 0.92 on a normalized scale [0, 1], with 0.23 and 0.14 Standard Deviation for ChatGPT-4 and ChatGPT-4o respectively. After the experience, we can conclude that a well-defined DSL and a proper prompting process, providing the LM with metadata, persistent prompts, and a good knowledge base, are crucial for good LM performance. When LMs receive the right prompts, both large and small LMs can achieve excellent results depending on the task. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T14:48:50Z No. of bitstreams: 1 Blending Language Models and Domain-Specific Languages in Computer Science Education. A Case Study on API RESTFul.pdf: 4031896 bytes, checksum: 5fc863a540c8e5b936bbe5966bcf8669 (MD5); Made available in DSpace on 2026-03-09T14:48:50Z (GMT). No. of bitstreams: 1 Blending Language Models and Domain-Specific Languages in Computer Science Education. A Case Study on API RESTFul.pdf: 4031896 bytes, checksum: 5fc863a540c8e5b936bbe5966bcf8669 (MD5)

AI Prediction and Teaching Strategies for a Two-Phase Engine in a Smart Learning Platform

AI Prediction and Teaching Strategies for a Two-Phase Engine in a Smart Learning Platform Real-Fernández, Alberto; García-Sigüenza, Javier; Llorens-Largo, Faraón; Molina-Carmona, Rafael The impact and progress of Information Technologies has led to a process of change in most environments of our society, specially education. Even more with the current rise of Artificial Intelligence, what has led to the creation of different new tools aiming to improve the learning experience. This fact has contributed to the creation of systems that aim to adapt the learning process to each individual learner and offer them a personalised experience. The problem of letting automated systems manage the whole learning process is the lack of human factor, but learning objectives and teacher criteria are crucial. That is why this research proposes a solution that combines the potential of AI without neglecting the teacher decision. Concretely, the proposal is an AI model that selects the most suitable activity to each learner. To do so, this proposed model is structured in two phases. The first is the prediction phase, in which the model predicts the score a learner will obtain and the time they will spend to complete an activity. Then, in the second phase, the selection of a single activity is done by means of instructional strategies. These strategies are based on the previously obtained metrics and establish the criteria to follow for selecting activities. The selected strategy is always set by the teacher, who will guide the learners through the process. With this model, this research proposes a combination of AI techniques with human decision-making. Instead of relying the learning process to an automated engine, it includes the teacher as the one to guide the AI by making the last decision. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T14:45:05Z No. of bitstreams: 1 AI Prediction and Teaching Strategies for a Two-PhaseEngine in a Smart Learning Platform.pdf: 1755160 bytes, checksum: 2e86e87974bd89b61f9a1eb045ed5776 (MD5); Made available in DSpace on 2026-03-09T14:45:05Z (GMT). No. of bitstreams: 1 AI Prediction and Teaching Strategies for a Two-PhaseEngine in a Smart Learning Platform.pdf: 1755160 bytes, checksum: 2e86e87974bd89b61f9a1eb045ed5776 (MD5)

UPMVM: A Metrics Verification Model for Urdu Poetry

UPMVM: A Metrics Verification Model for Urdu Poetry Zaman, Asia; Ud-Din, Zia; Iqbal, Sajid; Al Shuhail, Asma Urdu poetry retains a prominent position in the cultural heritage of Urdu language. Rhyme schemes and meters are frequently employed in poetry, which follow specific patterns and structures. Natural Language Processing has the capacity to recognize and analyze these patterns, which is beneficial in the investigation of poetic forms. This research presents the UPMVM (Urdu Poetry Metrics Verification Model), a novel rulebased architecture, designed for detecting meter of any given Urdu ghazal verse. In this work, we propose an algorithm that consists of sixteen steps that identifies the Arud meter in the Urdu verses using a custom developed system. This application will not only assist professional poets but also enable students to examine poetry within the framework of prosody principles. The accurate analysis of the prosody of any poetry relies on the act of uttering words rather than on a written record. UPMVM consists of two phases: 1) The primary objective of the initial phase is to consolidate all available literature of the Arud system into a unified digital platform, then develop individual and combined DFA of each identified meter for pattern recognition; 2) the second phase is about the algorithmic implementation. All these rhythmical patterns are matched with 290 Arud meters and their sub-meters developed during this study. The implementation strategy of phase 2 comprises of five essential sub-phases including tokenization, orthography, syllable identification, weight assignment, and meter detection. For evaluation of the proposed method, three different datasets are used for feature extraction, token identification and performance measurement for identification of rhythmic patterns in Urdu poetry. The UPMVM model reached to promising outcome with an average accuracy of 94%. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T14:36:58Z No. of bitstreams: 1 UPMVM A Metrics Verification Model for Urdu Poetry.pdf: 5797789 bytes, checksum: da7654d73fe50f6aaca78712a3fe24ac (MD5); Made available in DSpace on 2026-03-09T14:36:58Z (GMT). No. of bitstreams: 1 UPMVM A Metrics Verification Model for Urdu Poetry.pdf: 5797789 bytes, checksum: da7654d73fe50f6aaca78712a3fe24ac (MD5)

Multi-Class Dental CBCT Segmentation in Data- Constrained Scenarios Through Transformers

Multi-Class Dental CBCT Segmentation in Data- Constrained Scenarios Through Transformers Giménez-Aguilar, Rafael C.; Paraíso-Medina, Sergio; García-Remesal, Miguel; Pradíes Ramiro, Guillermo Jesús; Bonfanti-Gris, Monica; Alonso-Calvo, Raúl Accurate segmentation of dental structures from cone-beam computed tomography (CBCT) images has become an active research field due to the widespread use of this technology in clinical practice. In recent years, contributions have shifted from traditional computer vision methods to deep learning-based approaches. However, most of these works are based solely on convolutional neural networks (CNNs), whereas the image segmentation state-of-the-art is currently moving towards attention-based architectures. Furthermore, contributions on dental CBCTs predominantly present methods focused on a single object category, mainly teeth. In this article we tackle the segmentation of multiple oral structures by implementing previously unutilized query-based segmentation transformers. The proposed method achieves similar results to the stateof- the-art, especially on tooth segmentation, while employing a considerably smaller training dataset than prior contributions. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T14:33:05Z No. of bitstreams: 1 Multi-Class Dental CBCT Segmentation in Data-Constrained Scenarios Through Transformers.pdf: 2239302 bytes, checksum: 9add49d933551e3e343789ba9467739a (MD5); Made available in DSpace on 2026-03-09T14:33:05Z (GMT). No. of bitstreams: 1 Multi-Class Dental CBCT Segmentation in Data-Constrained Scenarios Through Transformers.pdf: 2239302 bytes, checksum: 9add49d933551e3e343789ba9467739a (MD5)

Robust Federated Learning With Contrastive Learning and Meta-Learning

Robust Federated Learning With Contrastive Learning and Meta-Learning Zhang, Huan; Chen, Yuxian; Li, Kuanching; Li, Yuhui; Zhou, Sisi; Liang, Wei; Poniszewska-Maranda, Aneta Federated learning is regarded as an effective approach to addressing data privacy issues in the era of artificial intelligence. Still, it faces the challenges of unbalanced data distribution and client vulnerability to attacks. Current research solves these challenges but ignores the situation where abnormal updates account for a large proportion, which may cause the aggregated model to contain excessive abnormal information to deviate from the normal update direction, thereby reducing model performance. Some are not suitable for non-Independent and Identically Distribution (non IID) situations, which may lead to the lack of information on small category data under non-IID and, thus, inaccurate prediction. In this work, we propose a robust federated learning architecture, called FedCM, which integrates contrastive learning and meta-learning to mitigate the impact of poisoned client data on global model updates. The approach improves features by leveraging extracted data characteristics combined with the previous round of local models through contrastive learning to improve accuracy. Additionally, a meta-learning method based on Gaussian noise model parameters is employed to fine-tune the local model using a global model, addressing the challenges posed by non-independent and identically distributed data, thereby enhancing the model’s robustness. Experimental validation is conducted on real datasets, including CIFAR10, CIFAR100, and SVHN. The experimental results show that FedCM achieves the highest average model accuracy across all proportions of attacked clients. In the case of a non-IID distribution with a parameter of 0.5 on CIFAR10, under attack client proportions of 0.2, 0.5, and 0.8, FedCM improves the average accuracy compared to the baseline methods by 8.2%, 7.9%, and 4.6%, respectively. Across different proportions of attacked clients, FedCM achieves at least 4.6%, 5.2%, and 0.45% improvements in average accuracy on the CIFAR10, CIFAR100, and SVHN datasets, respectively. FedCM converges faster in all training groups, especially showing a clear advantage on the SVHN dataset, where the number of training rounds required for convergence is reduced by approximately 34.78% compared to other methods. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T13:30:05Z No. of bitstreams: 1 Robust Federated Learning With Contrastive Learning and Meta-Learning.pdf: 3831396 bytes, checksum: 90f38925e9ef075b0d00a682cd267905 (MD5); Made available in DSpace on 2026-03-09T13:30:05Z (GMT). No. of bitstreams: 1 Robust Federated Learning With Contrastive Learning and Meta-Learning.pdf: 3831396 bytes, checksum: 90f38925e9ef075b0d00a682cd267905 (MD5)

PRESTO: A Recommender of Musical Collaborations Based on Heterogeneous Graph Neural Networks

PRESTO: A Recommender of Musical Collaborations Based on Heterogeneous Graph Neural Networks Terroso-Saenz, Fernando; Soto, Jesús; Muñoz, Andrés; Roose, Philippe The music industry is now more complex and competitive than ever before. In recent years, the search for collaborations with other artists has become a common strategy for musicians to maintain their presence in the sector. Besides, existing music streaming services such as Spotify have exposed large data feeds that can be used to develop innovative services within the realm of music. In this context, the present work introduces PRESTO, a novel recommendation system to suggest musicians for new collaborations with other artists by means of an ensemble of Graph Neural Networks. The system is fed with a heterogeneous graph representing the time evolution and the stationary aspects of a musician’s career. Finally, the proposal has been evaluated with a dataset comprising more than 200,000 artists, with an average F1 score above 0.75. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T13:23:42Z No. of bitstreams: 1 PRESTO A Recommender of Musical Collaborations.pdf: 6405036 bytes, checksum: 43a5d995695acffdd554a64843b0e3e2 (MD5); Made available in DSpace on 2026-03-09T13:23:42Z (GMT). No. of bitstreams: 1 PRESTO A Recommender of Musical Collaborations.pdf: 6405036 bytes, checksum: 43a5d995695acffdd554a64843b0e3e2 (MD5)

Recommender Systems: Learning Collaborative Filtering Similarity Measures Using Siamese Networks

Recommender Systems: Learning Collaborative Filtering Similarity Measures Using Siamese Networks Bobadilla, Jesús; Gutierrez, Abraham Improving current similarity measures in the collaborative filtering Recommender Systems is relevant, since it contributes to different applications such as to get better big data representations of users and items, to implement dynamic browsers able to navigate through data, and to explain recommendation results. Currently, there are many statistically based similarity measures, some of them tailored to the extraordinarily sparse collaborative filtering scenario. Nevertheless, the hypothesis of the paper is that using neural networks, learnt similarity measures can be obtained that improve existing ones. To accomplish the task, the typical neural models cannot be used, and it is necessary to focus on the similarity learning area, in which the goal is to make the model learn, which is a similarity function able to measure how similar two objects are. Siamese networks adequately implement the similarity learning concept, and we have adapted them to collaborative filtering particularities. The results in different scenarios show significant improvements compared to the state-of-the-art. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T13:13:20Z No. of bitstreams: 1 Recommender Systems.pdf: 2150587 bytes, checksum: 4f500a3c2bc899557bf7e273e92cb677 (MD5); Made available in DSpace on 2026-03-09T13:13:20Z (GMT). No. of bitstreams: 1 Recommender Systems.pdf: 2150587 bytes, checksum: 4f500a3c2bc899557bf7e273e92cb677 (MD5)

Source Credibility Assessment in the Realm of Information Disorder: A Literature Review

Source Credibility Assessment in the Realm of Information Disorder: A Literature Review Cosentino, Alessia; de Maio, Carmen; Furno, Domenico; Gallo, Mariacristina; Loia, Vincenzo The proliferation of information disorder in the digital age has sparked a growing concern regarding the credibility of sources disseminating information. This review examines the evolving landscape of source credibility within information disorder. The review synthesizes key findings and trends related to the factors influencing source credibility, including available tools, shared indicators, and existing methods experimented with in calculating source credibility. The analysis highlights that from a more commercial point of view, several tools are aimed at analyzing the content’s credibility and studying the sources’ credibility. However, from a methodological point of view, there is still something more to do. Indicators that can be used to carry out a source credibility assessment focus on the structure and design of the source, excluding others indicating how the page traffic could be. As for the techniques to be used to assess the credibility of a source, it emerged that more innovative techniques, such as deep-learning, are being developed alongside slightly more classical statistical methods. The review analyzes 23 papers from Conferences and 22 from Journals published in recent years. It also identifies avenues for future inquiry and the development of effective strategies to combat the challenges posed by misinformation in the digital era. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T13:09:15Z No. of bitstreams: 1 Source Credibility Assessment in the Realm of Information Disorder.pdf: 1288035 bytes, checksum: 288f3c7999f4fb1dd7915adaa340ceff (MD5); Made available in DSpace on 2026-03-09T13:09:15Z (GMT). No. of bitstreams: 1 Source Credibility Assessment in the Realm of Information Disorder.pdf: 1288035 bytes, checksum: 288f3c7999f4fb1dd7915adaa340ceff (MD5)

Editor's Note

Editor's Note Verdú, Elena The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) provides a forum for researchers and professionals to share recent advances in artificial intelligence (AI) and its wide range of applications. This issue brings together contributions that reflect the growing diversity of AI research, covering topics such as information credibility assessment in social media, recommender systems, federated learning and data privacy, medical image analysis, educational technologies, large language models and virtual assistants, intelligent multimedia systems, and environmental monitoring. Collectively, these works illustrate how current AI methods continue to expand across disciplines, addressing both theoretical challenges and real-world problems. Downloads Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-09T13:02:32Z No. of bitstreams: 1 Editor’s Note.pdf: 93685 bytes, checksum: 045066c7184207c043bbacf864d7a80c (MD5); Made available in DSpace on 2026-03-09T13:02:32Z (GMT). No. of bitstreams: 1 Editor’s Note.pdf: 93685 bytes, checksum: 045066c7184207c043bbacf864d7a80c (MD5)

Security Model for the Internet of Things, Through Blockchain

Security Model for the Internet of Things, Through Blockchain Díaz Gutiérrez, Yesid; Cueva-Lovelle, Juan Manuel; Candia Herrera, Diana Carolina Due to the proliferation of computer crimes related to information vulnerability handled by people and entities and evidenced in attacks of financial, commercial, personal and even family nature; a need has been identified to implement, security strategies and protocols in each and every one of these areas, which make possible the effective protection of the integrity and privacy of data. Regarding this, there are protection schemes such as cryptography and reliable time stamping which undoubtedly have managed to partially solve this problem by attacking structural and crucial points. However, the evolution in the technology field has been currently represented in the fourth industrial revolution and its context towards 4.0 technologies and smart industries; various technologies have been positioned in the emerging and disruptive categories, among which the Internet of Things (IoT) stands out. This technology has become the target of multiple computer attacks, due to the processes of Extraction, Transformation, Loading and Transmission of large volumes of data. Alongside its widespread connection to the Internet, it’s become a strategic target for such attacks. A possible alternative solution to this situation is blockchain, which allows information to be public and stored in different blocks, which makes it easier to guarantee the integrity of information based on the following aspects: • Identification of the attacked and / or compromised information, which can be marked as invalid information. • Public report of the attack. • Information backup in another block to facilitate its recovery. In this regard, it is important to highlight that these functional and technological characteristics offered by the blockchain, facilitate the management of information and its integrity. However, it is necessary and essential to previously guarantee the structure of the information generated; as some processes of Business Intelligence (BI), such as the Extraction, Transformation and Load scheme (ELT), would be of great relevance and support during the development of this procedure. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-06T12:25:21Z No. of bitstreams: 1 Security Model for the Internet of Things, Through Blockchain.pdf: 450339 bytes, checksum: 6744590dde281fb739e033db5658a521 (MD5); Made available in DSpace on 2026-03-06T12:25:21Z (GMT). No. of bitstreams: 1 Security Model for the Internet of Things, Through Blockchain.pdf: 450339 bytes, checksum: 6744590dde281fb739e033db5658a521 (MD5)

An Adaptive Framework for Resource Allocation Management in 5G Vehicular Networks

An Adaptive Framework for Resource Allocation Management in 5G Vehicular Networks Vijayan, Rajilal Manathala; Granelli, Fabrizio; Umamakeswari, A. Vehicle-to-everything (V2X) communication is crucial in vehicular networks, for enhancing traffic safety by ensuring dependable and low latency services. However, interference has a significant impact on V2X communication when channel states are changed in a high mobility environment. Integration of next generation cellular networks such as 5G in V2X communication can solve this issue. Also, successful resource allocation among users achieves a better interference control in high mobility scenarios. This work proposes a novel resource allocation strategy for 5G cellular V2X communication based on clustering technique and Deep Reinforcement Learning (DRL) with the aim of maximizing systems energy efficiency and MVNO’s profit. DRL is used to distribute communication resources for the best interference control in high mobility scenarios. To reduce signalling overhead in DRL deployments, the proposed method adopted RRH grouping and vehicle clustering technique. The overall architecture is implemented in two phases. The first phase addresses the RRH grouping and vehicle clustering technique with the objective of maximising the energy efficiency of the system and the second phase addresses the technique of employing DRL in conjunction with bidding to optimise MVNO’s profit. Second phase addresses the resource allocation which is implemented in two level stage. First level addresses the bidding of resources to BS using bidding and DRL techniques and the second level addresses the resource allocation to users using Dueling DQN technique. Through simulations, the proposed algorithm's performance is compared with the existing algorithms and the results depicts the improved performance of the proposed system. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-06T12:20:20Z No. of bitstreams: 1 An Adaptive Framework for Resource Allocation Management in 5G Vehicular Networks.pdf: 1076396 bytes, checksum: 3309ad5626d22e3cdb0b884466631f03 (MD5); Made available in DSpace on 2026-03-06T12:20:20Z (GMT). No. of bitstreams: 1 An Adaptive Framework for Resource Allocation Management in 5G Vehicular Networks.pdf: 1076396 bytes, checksum: 3309ad5626d22e3cdb0b884466631f03 (MD5)

Identification of Monkeypox Disease Based on MpoxNet and Swin Transformer Models Using Mobile Application

Identification of Monkeypox Disease Based on MpoxNet and Swin Transformer Models Using Mobile Application Sadesh, S.; Thangaraj, Rajasekaran; Pandiyan, P.; Priya, R. Devi; Naveen, Palanichamy Humankind is still reeling from the devastating impact of the Covid-19 pandemic, yet another looming threatis the potential global spread of the monkeypox virus. While monkeypox may not pose the same level of lethality or contagion as COVID-19, its significant spread across countries is cause for concern. Already, outbreaks have been reported in 75 nations worldwide. Despite sharing clinical characteristics with smallpox, including lesions and rashes, monkeypox symptoms are frequently mistaken for those of other poxviruses such as chickenpox and cowpox. Consequently, accurate early diagnosis of monkeypox by healthcare professionals remains challenging. Automated monkeypox identification using Deep Learning (DL) techniques presents a promising avenue for addressing this challenge. In this study, a modified deep convolutional neural network (DCNN) model named MpoxNet is proposed for the identification of monkeypox disease. The performance of MpoxNet is evaluated against established DCNN models, including ResNet50, VGG16, VGG19, DenseNet121, DenseNet169, Xception, InceptionResNetV2, and MobileNetV2. This study addresses the pressing challenge of monkeypox identification by proposing MpoxNet. With the aim of enhancing early detection and containment efforts, MpoxNet's performance is evaluated against established DCNN models across two distinct datasets: MSLD and MSID Dataset. Results reveal MpoxNet's superior test accuracy of 94.82% on the MSLD Dataset, surpassing other models. However, evaluation on the MSID Dataset highlights variations in performance, emphasizing the influence of dataset characteristics. Additionally, the introduction of the Swin Transformer model demonstrates exceptional performance on the MSLD and the MSID Dataset and, achieving an accuracy of 98%. These findings underscore the importance of considering diverse datasets and leveraging advanced techniques for robust monkeypox detection systems. Integration of MpoxNet with a mobile application offers a promising solution for rapid and precise monkeypox disease detection, providing valuable insights for future research and real-world deployment strategies to effectively combat the global spread of monkeypox. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-06T12:15:38Z No. of bitstreams: 1 Identification of Monkeypox Disease Based on MpoxNet and Swin Transformer Models Using Mobile Application.pdf: 783729 bytes, checksum: c12c03c73eb88128014adcf20f2337ba (MD5); Made available in DSpace on 2026-03-06T12:15:38Z (GMT). No. of bitstreams: 1 Identification of Monkeypox Disease Based on MpoxNet and Swin Transformer Models Using Mobile Application.pdf: 783729 bytes, checksum: c12c03c73eb88128014adcf20f2337ba (MD5)

Self-Supervised Attentive Feature Learning for Alzheimer’s Disease Detection

Self-Supervised Attentive Feature Learning for Alzheimer’s Disease Detection Elmannai, Hela; Saleem, Nasir; Bourouis, Sami; Alkanhel, Reem Ibrahim Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that leads to memory loss and a decline in cognitive abilities. It primarily affects older adults and is the most common cause of dementia. Using deep learning, models can analyze brain imaging scans to detect specific patterns and biomarkers associated with the disease. Supervised learning models achieve high accuracy rates, but they require a large amount of data sets and labelled medical images. Self-supervised learning can achieve high accuracy rates with fewer training data. This study proposes a self-supervised attentive feature learning network (SSA-Net) for classifying Alzheimer’s disease. The proposed approach leverages self-supervised learning and attention mechanisms to enhance the accuracy and reliability of the classifying model. We employ ResNet-50, incorporating attentive activation, which replaces the ReLU activation, improving the ability of the neural model to focus on the most relevant features in the input medical images. We use SimCLR (Simple Framework for Contrastive Learning of Visual Representations) with the ResNet-50 backbone as a self-supervised learning framework that effectively learns high-quality visual representations in brain MRI (Magnetic Resonance Imaging) scans without labelling. We used the Kaggle Alzheimer’s classification dataset (KACD) containing brain MRI scans for training and testing. Experimental results on the KACD dataset show that the proposed attentive self-supervised ResNet50 reached 99.7% classification accuracy compared to the traditional ResNet50 with 98.1% accuracy. Evaluation metrics show the effectiveness of the proposed SSA-Net for the efficient classification of Alzheimer’s disease. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-06T10:42:32Z No. of bitstreams: 1 Self-Supervised Attentive Feature Learning for Alzheimer’s Disease Detection.pdf: 893051 bytes, checksum: 97b61d8ee0b13f3aea3f086ae205f8ea (MD5); Made available in DSpace on 2026-03-06T10:42:33Z (GMT). No. of bitstreams: 1 Self-Supervised Attentive Feature Learning for Alzheimer’s Disease Detection.pdf: 893051 bytes, checksum: 97b61d8ee0b13f3aea3f086ae205f8ea (MD5)

Attention Embedded Residual Bottleneck CNN Architecture for Breast Cancer Diagnosis in Ultrasound Images

Attention Embedded Residual Bottleneck CNN Architecture for Breast Cancer Diagnosis in Ultrasound Images Fatima, Mamuna; Khan, Muhammad Attique; Shaheen, Saima; Kadry, Seifedine; Alqahtani, Omar; Alouane, M. Turki-Hadj Breast cancer (BrC) stands as the predominant cancer among women, resulting in a substantial global mortality toll each year. Early detection plays a pivotal role in diminishing mortality rates. Manual diagnosis of BrC is time-intensive, intricate, and prone to errors, emphasizing the necessity for an automated system for timely detection. Various imaging methods have been investigated, underscoring the crucial need for accurate detection to prevent unwarranted treatments and biopsies. Recent years have witnessed substantial exploration and enhancement in the application of DL for efficiently processing medical images. This study aiming to create an effective and resilient DL framework for BrC detection and classification. The steps are contrast enhancement and augmentation, a hybrid CNN network ‘BrC-DeepRBNet’ is introduced that is built from scratch and incorporates several design elements including residual blocks, bottleneck architecture, and a self-attention mechanism. This framework is employed to construct two networks, one comprising of 107 layers and the other with 149 layers. Moreover, the network capitalizes on the benefits offered by batch normalization (BN) and group normalization (GN), utilizes ReLU and leaky ReLU as activation functions, and integrates Max pooling layer into its architecture in a series of residual-bottleneck blocks. Further, for feature fusion horizontal approach is used and optimization is done using generalized normal distribution optimization (GNDO). The selected features are further classified using neural network classifiers. The introduced framework achieved the highest classification accuracy at 97.05% with publicly available BUS dataset. A detailed ablation study is presented that demonstrates the superior performance of the presented approach, surpassing various pre-trained models (i.e. AlexNet, InceptionV3, ResNet50, and ResNet101) and existing BrC detection and classification techniques. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-06T10:36:41Z No. of bitstreams: 1 Attention Embedded Residual Bottleneck CNN Architecture for Breast Cancer Diagnosis in Ultrasound Images.pdf: 1319325 bytes, checksum: 4835fb493fbddb081c3c59346e1bdd4c (MD5); Made available in DSpace on 2026-03-06T10:36:41Z (GMT). No. of bitstreams: 1 Attention Embedded Residual Bottleneck CNN Architecture for Breast Cancer Diagnosis in Ultrasound Images.pdf: 1319325 bytes, checksum: 4835fb493fbddb081c3c59346e1bdd4c (MD5)

Ensemble Diabetic Retinopathy Severity Classification Framework With Optimized VGG16, Resnet, and Inception Features

Ensemble Diabetic Retinopathy Severity Classification Framework With Optimized VGG16, Resnet, and Inception Features Sheela, A. Jeba; Krishnamurthy, M. Background problem: Diabetic Retinopathy (DR) is characterized by high glucose levels in the blood, which can lead to permanent vision loss and microvascular complications. Various deep learning techniques for DR analysis tend to be more complex and may experience delays in delivering accurate results, thereby limiting their application in clinical settings. Implementing real-time predictionand severity analysisof DR can address this problem by providing real-time diagnostic insights based on DR severity levels. Aim: So, this paper is intended to offer a new DR detection and severity classification model with the highranking-based ensemble learning approach. Methodology: The preprocessed and segmented images are utilized in the feature extraction processusing ensemble architecture which incorporated VGG16, Resnet, and Inception to get three sets of features. The optimal features are selected using an Adaptive Scavenger-Based Dingo Optimization Algorithm (AS-DOX) to achieve the efficient classification of DR severity. The optimization constraint stake place in the HighRanking-Based Deep Ensemble Learning (HR-DEL) model helps to enhance the efficacy of classification for the offered approach. The simulation analysis provides enhanced performance with the accurate classification of the designed DR severity classification approach by comparing it with other baseline methods. Result: From the result analysis, the offered method achieves 96.6 % accuracy and sensitivity rate. Moreover, it achieves a 90.52% precision rate. Conclusion: Thus, the designed DR severity classification model attains better performance, and also it is utilized for early detection of DR severity. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-06T10:30:32Z No. of bitstreams: 1 Ensemble Diabetic Retinopathy Severity Classification Framework With Optimized VGG16, Resnet, and Inception Features.pdf: 1425672 bytes, checksum: 1c1ae01ff8659faf7cdf408c333e09ad (MD5); Made available in DSpace on 2026-03-06T10:30:32Z (GMT). No. of bitstreams: 1 Ensemble Diabetic Retinopathy Severity Classification Framework With Optimized VGG16, Resnet, and Inception Features.pdf: 1425672 bytes, checksum: 1c1ae01ff8659faf7cdf408c333e09ad (MD5)

On the Use of Large Language Models at Solving Math Problems: A Comparison Between GPT-4, LlaMA-2 and Gemini

On the Use of Large Language Models at Solving Math Problems: A Comparison Between GPT-4, LlaMA-2 and Gemini García Navarro, Alejandro L.; Koneva, Nataliia; Hernández, José Alberto; Sánchez-Macián, Alfonso In November 2022, ChatGPT v3.5 was announced to the world. Since then, Generative Artificial Intelligence (GAI) has appeared in the news almost daily, showing impressive capabilities at solving multiple tasks that have surprised the research community and the world in general. Indeed the number of tasks that ChatGPT and other Large Language Models (LLMs) can do are unimaginable, especially when dealing with natural text. Text generation, summarisation, translation, and transformation (into poems, songs, or other styles) are some of its strengths. However, when it comes to reasoning or mathematical calculations, ChatGPT finds difficulties. In this work, we compare different flavors of ChatGPT (v3.5, v4, and Wolfram GPT) at solving 20 mathematical tasks, from high school and first-year engineering courses. We show that GPT-4 is far more powerful than ChatGPT-3.5, and further that the use of Wolfram GPT can even slightly improve the results obtained with GPT-4 at these mathematical tasks. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-04T16:46:41Z No. of bitstreams: 1 On the Use of Large Language Models at Solving Math Problems.pdf: 416406 bytes, checksum: ec1a652ce78b2e3a3d30e6fb54f768e6 (MD5); Made available in DSpace on 2026-03-04T16:46:41Z (GMT). No. of bitstreams: 1 On the Use of Large Language Models at Solving Math Problems.pdf: 416406 bytes, checksum: ec1a652ce78b2e3a3d30e6fb54f768e6 (MD5)

Geometrics Assisted Rubbing Generation and Semantics Enhanced Detection for Small and Dense OBI Character

Geometrics Assisted Rubbing Generation and Semantics Enhanced Detection for Small and Dense OBI Character Wan, Xiuan; Fang, Yuchun; Wu, Jiahua; Pan, Shouyong Character detection is essential for subsequent Oracle Bone Inscription (OBI) research. However, the lack of labeled data and the complexity of small and dense OBI characters are the main difficulties in OBI detection research. In this paper, we propose a framework for rubbing generation that can automatically build up largescale rubbing samples with verisimilar scenarios to noisy wild OBI through geometric and morphological construction combined with style transferring. Moreover, we propose a semantic-enhanced detection model aiming at small and dense OBI through the fusion of multi-resolution feature maps with the enriched feature in the YOLOv5s backbone. We introduce the higher resolution and the Soft-NMS into the proposed OBI detection model to solve the overlapping of small and dense OBI characters. The augmented dataset improves the performance of benchmark object detection models in the real OBI detection task when sufficient data is lacking. Furthermore, the proposed OBI detection model can provide easy and preferable access to OBI detection even with a small number of labeled data and obtain preferable results. Experiments ascertain the effectiveness of the proposed OBI generation framework and the proposed OBI detection model. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-02-25T16:34:26Z No. of bitstreams: 1 Geometrics Assisted Rubbing Generation and Semantics Enhanced Detection for Small and Dense OBI Character.pdf: 2212538 bytes, checksum: 09e6fef4d982a81652653756e6970bff (MD5); Made available in DSpace on 2026-02-25T16:34:26Z (GMT). No. of bitstreams: 1 Geometrics Assisted Rubbing Generation and Semantics Enhanced Detection for Small and Dense OBI Character.pdf: 2212538 bytes, checksum: 09e6fef4d982a81652653756e6970bff (MD5)

Three Dimensional Tree Modeling Based on the Skeleton Path Optimization and Geometrical Shapes

Three Dimensional Tree Modeling Based on the Skeleton Path Optimization and Geometrical Shapes Li, Xin; Zhou, Xuan; Xu, Sheng Nowadays, the 3D individual tree reconstruction has played a significant role in the phenotypic study of trees. This paper proposes a new automatic method for extracting skeletons of individual trees and reconstructing 3D models. Firstly, the Euclidean clustering is performed to obtain center points of candidate branch regions. Then, the initial skeletons of LiDAR point clouds are obtained by slicing clusters in three dimensions. Secondly, skeleton points are completed by the proposed branch tracking. Then, the radius of the branches is accurately estimated from the branches. Thirdly, optimal points are interpolated in appropriate directions to refine skeletons of individual trees. Then, the Laplacian algorithm is conducted for smoothing branches. After that, optimal geometric shapes are formulated to reconstruct the final 3D tree models. Experimental results show that the average accuracy of our individual tree models is up to 97.49%, which shows a promising algorithm in 3D tree reconstructions. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-02-25T16:27:48Z No. of bitstreams: 1 Three Dimensional Tree Modeling Based on the Skeleton Path Optimization and Geometrical Shapes.pdf: 1783238 bytes, checksum: 78e5941b5dd4a843db7d0c416d3cf06d (MD5); Made available in DSpace on 2026-02-25T16:27:48Z (GMT). No. of bitstreams: 1 Three Dimensional Tree Modeling Based on the Skeleton Path Optimization and Geometrical Shapes.pdf: 1783238 bytes, checksum: 78e5941b5dd4a843db7d0c416d3cf06d (MD5)

Posture Estimation of Curve Running Motion Using Nano-Biosensor and Machine Learning

Posture Estimation of Curve Running Motion Using Nano-Biosensor and Machine Learning Wu, Xiaoming; Cao, Yu; Wang, Yu; Li, Bing; Yang, Haitao; Raja, S. P. Curve running is a common form of training and competition. Conducting research on posture estimation during curve running can provide more accurate training and competition data for athletes. However, due to the unique nature of curve running, traditional posture estimation methods neglect the temporal changes in athlete posture, resulting in a decrease in estimation accuracy. Therefore, a posture estimation method for curve running motion using nano-biosensor and machine learning is proposed. First, the motion parameters of humans are collected by nano-biosensor, and the posture coordinates are obtained preliminarily. Second, the posture coordinates are established according to the human motion parameters, and the curve running posture data is obtained and filtered to obtain more accurate data. Finally, the Bayesian network in machine learning is used to continuously track the posture, and a nonlinear equation is established to fuse the posture angle obtained by the sensor and the posture tracked by the Bayesian network, to realize the posture estimation of curve running motion. The results show that the proposed estimation method has a good motion posture estimation effect, and the hip joint estimation error, knee joint estimation error and ankle joint estimation error are all less than 5°, and the endpoint displacement estimation offset rate is less than 2%. It can realize accurate motion posture estimation of curve running motion, and has important application value in the field of track training. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-02-25T16:21:22Z No. of bitstreams: 1 Posture Estimation of Curve Running Motion Using Nano Biosensor and Machine Learning.pdf: 508410 bytes, checksum: 9f8033f27a6485bc48c75b4c6ca909f9 (MD5); Made available in DSpace on 2026-02-25T16:21:22Z (GMT). No. of bitstreams: 1 Posture Estimation of Curve Running Motion Using Nano Biosensor and Machine Learning.pdf: 508410 bytes, checksum: 9f8033f27a6485bc48c75b4c6ca909f9 (MD5)

A Realtime Classroom Assessment System for Analysis of Students’ Evaluation of Teaching Through a Deep Learning and Emotional Contagion Mechanism

A Realtime Classroom Assessment System for Analysis of Students’ Evaluation of Teaching Through a Deep Learning and Emotional Contagion Mechanism Lin, Kuan-Cheng; Lin, Ya-Hsuan; Chen, Ya-Hsuan Student evaluations of teacher performance are often derived from end-of-semester assessments, significantly impacting the authenticity of teaching evaluations but failing to provide real-time feedback. In addition, teachers' emotional states affect student performance, including in terms of learning motivation and classroom participation, which reflect the students' emotional state. This teacher-student emotional contagion mechanism focuses on the interaction of teacher-student emotions and can be used to observe the quality of instructional performance. Therefore, automatically detecting teacher-student emotional interaction and then providing real-time class satisfaction feedback can provide teachers with a more effective basis for adjusting classroom content. This research proposes an end-to-end classroom real-time teaching evaluation system based on automatic facial-emotion recognition, which can accurately detect and directly analyze the emotions of students and teachers in streaming frames. The system consists of two parts: First, a YOLO model based on deep learning approaches is used to automatically detect the emotional states of teachers and students during the teaching process; Then, combining the emotional contagion mechanism with the teaching evaluation scale, teaching satisfaction can be predicted using a Long Short-Term Memory (LSTM) model to output a classroom satisfaction score within a fixed period. Further analysis of the testing dataset confirms that the model has a high reliability in predicting teaching satisfaction. Research results show the proposed system can achieve an emotional recognition accuracy rate of 98.1% for teachers and 99.5% for students based on the emotion datasets. Further development could potentially provide teachers with strategies to improve classroom teaching effectiveness, better understand students' emotions and learning motivation, and improve learning outcomes. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-02-25T16:15:50Z No. of bitstreams: 1 A Realtime Classroom Assessment System for Analysis.pdf: 410131 bytes, checksum: 7244389b1751d418174f081e546ef794 (MD5); Made available in DSpace on 2026-02-25T16:15:50Z (GMT). No. of bitstreams: 1 A Realtime Classroom Assessment System for Analysis.pdf: 410131 bytes, checksum: 7244389b1751d418174f081e546ef794 (MD5)

ChatGPT, Generative AI, Mathematical Problems, Wolfram Mathematica

ChatGPT, Generative AI, Mathematical Problems, Wolfram Mathematica García Navarro, Alejandro L.; Koneva, Nataliia; Hernández, José Alberto; Sánchez-Macián, Alfonso In November 2022, ChatGPT v3.5 was announced to the world. Since then, Generative Artificial Intelligence (GAI) has appeared in the news almost daily, showing impressive capabilities at solving multiple tasks that have surprised the research community and the world in general. Indeed the number of tasks that ChatGPT and other Large Language Models (LLMs) can do are unimaginable, especially when dealing with natural text. Text generation, summarisation, translation, and transformation (into poems, songs, or other styles) are some of its strengths. However, when it comes to reasoning or mathematical calculations, ChatGPT finds difficulties. In this work, we compare different flavors of ChatGPT (v3.5, v4, and Wolfram GPT) at solving 20 mathematical tasks, from high school and first-year engineering courses. We show that GPT-4 is far more powerful than ChatGPT-3.5, and further that the use of Wolfram GPT can even slightly improve the results obtained with GPT-4 at these mathematical tasks. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-02-25T16:07:52Z No. of bitstreams: 1 On the Use of Large Language Models at Solving Math Problems.pdf: 416406 bytes, checksum: ec1a652ce78b2e3a3d30e6fb54f768e6 (MD5); Made available in DSpace on 2026-02-25T16:07:52Z (GMT). No. of bitstreams: 1 On the Use of Large Language Models at Solving Math Problems.pdf: 416406 bytes, checksum: ec1a652ce78b2e3a3d30e6fb54f768e6 (MD5)

A Sustainable Deep Learning Paradigm for Reliable Energy Prediction in Next-Gen Consumer Electronics

A Sustainable Deep Learning Paradigm for Reliable Energy Prediction in Next-Gen Consumer Electronics Jeon, Gwangil; Ahmed, Imran; Han, Sanghoon In the rapidly evolving consumer electronics landscape, the imperative for sustainable energy solutions necessitates the development of accurate energy prediction methodologies. Traditional energy prediction models often fall short in accounting for the dynamic characteristics of renewable energy sources, particularly wind and solar. This limitation is pronounced in consumer electronics, where precise energy forecasting is pivotal for achieving optimal device performance and energy efficiency. To address this gap, we present a sustainable deep learning paradigm using Long Short-Term Memory (LSTM) networks to capture the complex temporal patterns inherent in renewable energy data. This paper introduces a novel and sustainable deep learning approach that significantly enhances energy prediction accuracy within the context of next-generation consumer electronics. By leveraging the capabilities of an LSTM-based model, we utilize an extensive dataset comprising hourly records of wind and solar energy production from the French grid since 2020. Our research addresses the inherent challenges in precise energy prediction, a cornerstone for efficient energy management and consumption optimization in emerging technology ecosystems. Through comprehensive data preprocessing, feature engineering, and rigorous training, the LSTM model demonstrates exceptional proficiency, achieving an impressive 82% accuracy in predicting energy production. This underscores its efficacy in capturing intricate temporal relationships and patterns within renewable energy data, facilitating its integration into next-generation consumer electronics. Our proposed paradigm addresses a critical need and paves the way for a future where accurate energy prediction fuels eco-conscious technology. In conclusion, this study contributes to a more sustainable energy landscape by advancing the development of reliable and efficient energy prediction methodologies for the evolving realm of next-generation consumer electronics. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-02-25T16:01:01Z No. of bitstreams: 1 A Sustainable Deep Learning Paradigm for Reliable Energy Prediction in Next Gen Consumer Electronics.pdf: 572480 bytes, checksum: 15dbecabf4998c3cebec7cfc3700902d (MD5); Made available in DSpace on 2026-02-25T16:01:01Z (GMT). No. of bitstreams: 1 A Sustainable Deep Learning Paradigm for Reliable Energy Prediction in Next Gen Consumer Electronics.pdf: 572480 bytes, checksum: 15dbecabf4998c3cebec7cfc3700902d (MD5)

IAtraj: Multi-Modal Trajectory Prediction Through Contextual Information Spatio-Temporal Interaction and Awareness

IAtraj: Multi-Modal Trajectory Prediction Through Contextual Information Spatio-Temporal Interaction and Awareness Wang, Xiaoliang; Zhou, Lian; Li, Kuan-Ching; Zheng, Shiqi; Fan, Huijing Accurately and feasibly predicting the future trajectories of autonomous vehicles is a critically important task. However, this task faces significant challenges due to the variability of driving intentions and the complexity of social interactions. These challenges primarily arise from the need to understand one’s driving behaviors and model the interaction information of the surrounding environment. A substantial amount of research has been focused on integrating interaction information from the surrounding environment, mainly using raster images or High-Definition maps (HD maps). However, the real-time update of environmental maps and the high computational cost associated with processing interaction information using compatible technologies such as vision have become limiting factors. Additionally, ineffective simulation and modeling of real driving scenarios, coupled with inadequate understanding of contextual environmental information, result in lower prediction accuracy. To overcome these challenges, we propose a multi-modal trajectory prediction model based on sequence modeling namely IAtraj, incorporating multiple attention mechanisms, focuses on the three critical elements in real traffic scenarios: the target agent’s historical trajectory, effective interactions with neighboring vehicles, and lane supervision and retention strategies. To better model these elements, we design modules for Temporal Interaction (TI), Spatial Interaction (SI), and Lane Awareness (LA). Through extensive experiments conducted on the publicly available nuScenes dataset, IAtraj exhibits outstanding performance, successfully addressing the challenges of temporal dependencies in trajectory sequences and the representation of scene changes. Finally, comprehensive ablation experiments validate the effectiveness of each significant module, reinforcing the reliability and robustness of IAtraj in dealing with complex traffic scenarios. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-02-25T15:46:42Z No. of bitstreams: 1 IAtraj Multi-Modal Trajectory Prediction Through Contextual Information Spatio-Temporal Interaction and Awareness.pdf: 1423128 bytes, checksum: 60d5f9e813b3a6ff8100670b5134b89d (MD5); Made available in DSpace on 2026-02-25T15:46:42Z (GMT). No. of bitstreams: 1 IAtraj Multi-Modal Trajectory Prediction Through Contextual Information Spatio-Temporal Interaction and Awareness.pdf: 1423128 bytes, checksum: 60d5f9e813b3a6ff8100670b5134b89d (MD5)

A 2D Clustering Based Hotspot Identification Approach for Spatio-Temporal Crime Prediction

A 2D Clustering Based Hotspot Identification Approach for Spatio-Temporal Crime Prediction Iqbal, Muhammad Faisal Buland; Ullah, Aman; Alhomoud, Ahmed; Hussain, Tariq; Attar, Razaz Waheeb; Ouyang, Jianquan; Alnfiai, Mrim M.; Hatamleh, Wesam Atef This research introduces a method for predicting where crimes will occur based on clustering activity in the area. Hotspots, or locations with a disproportionately high number of crimes, are located by a combination of spatial and temporal grouping methods employed by this strategy. Crime forecasting models use these hotspots to predict where crimes will occur. The approach's efficacy is tested using actual crime data, and it successfully predicts future crimes in high-crime zones. Law enforcement agencies can use the proposed method to protect the public better, and it shows promise as a tool for crime prediction. Academic research into the topic of foreseeing criminal behavior is a newer development. Researchers in this discipline have discovered that criminal behavior has universal patterns. These patterns may help law enforcement agencies plan for criminal activities. Predictive policing, hotspot analysis, and geographical profiling are examples of when crime forecasting has been useful. Several aspects of the census, such as the average yearly income and literacy rate, are related to the prevalence of crime in a certain area. Indicators of potentially criminal behavior, these characteristics may be seen as markers. This investigation aims to discover if any clues can be gleaned from past criminal behavior that may be utilized to forecast future criminal behavior. Using machine learning and 2-D Hotspot analysis, we propose a method for the spatiotemporal prediction of criminal activity within the scope of this study. Clustering is a method used in 2-dimensional hotspot analysis. Methods of modern categorization, both with and without hotspot analysis, are used to evaluate the suggested model's efficacy. It is found that the model that incorporates hotspot analysis performs better than the one that does not. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-02-25T15:27:47Z No. of bitstreams: 1 A 2D Clustering Based Hotspot Identification Approach for Spatio-Temporal Crime Prediction.pdf: 561649 bytes, checksum: 3df958bf45fed378586ac4711cc20fc4 (MD5); Made available in DSpace on 2026-02-25T15:27:47Z (GMT). No. of bitstreams: 1 A 2D Clustering Based Hotspot Identification Approach for Spatio-Temporal Crime Prediction.pdf: 561649 bytes, checksum: 3df958bf45fed378586ac4711cc20fc4 (MD5)

Performance and Communication Cost of Deep Neural Networks in Federated Learning Environments: An Empirical Study

Performance and Communication Cost of Deep Neural Networks in Federated Learning Environments: An Empirical Study Alotaibi, Basmah K.; Khan, Fakhri Alam; Qawqzeh, Yousef; Jeon, Gwanggil; Camacho, David Federated learning, a distributive cooperative learning approach, allows clients to train the model locally using their data and share the trained model with a central server. When developing a federated learning environment, a deep/machine learning model needs to be chosen. The choice of the learning model can impact the model performance and the communication cost since federated learning requires the model exchange between clients and a central server in several rounds. In this work, we provide an empirical study to investigate the impact of using three different neural networks (CNN, VGG, and ResNet) models in image classification tasks using two different datasets (Cifar-10 and Cifar-100) in a federated learning environment. We investigate the impact of using these models on the global model performance and communication cost under different data distribution that are IID data and non-IID data distribution. The obtained results indicate that using CNN and ResNet models provide a faster convergence than VGG model. Additionally, these models require less communication costs. In contrast, the VGG model necessitates the sharing of numerous bits over several rounds to achieve higher accuracy under the IID data settings. However, its accuracy level is lower under non-IID data distributions than the other models. Furthermore, using a light model like CNN provides comparable results to the deeper neural network models with less communication cost, even though it may require more communication rounds to achieve the target accuracy in both datasets. CNN model requires fewer bits to be shared during communication than other models. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2025-01-27T16:52:16Z No. of bitstreams: 1 Performance and Communication Cost of Deep Neural Networks in Federated Learning Environments.pdf: 2020700 bytes, checksum: c69172fdf55709aa480ba111a5668352 (MD5); Made available in DSpace on 2025-01-27T16:52:16Z (GMT). No. of bitstreams: 1 Performance and Communication Cost of Deep Neural Networks in Federated Learning Environments.pdf: 2020700 bytes, checksum: c69172fdf55709aa480ba111a5668352 (MD5)

Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation: the UEQ Family

Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation: the UEQ Family Kollmorgen, Jessica; Hinderks, Andreas; Thomaschewski, Jörg Measuring the user experience (UX) of products, systems and services is individual depending on the research question. On the one hand, the user’s goals and environment play a role in the subjective evaluation. On the other hand, different UX factors are relevant depending on the product. In this case, it is practical to have a questionnaire family as an aid, whose questionnaires are geared towards these different use cases. The User Experience Questionnaire (UEQ) family allows researchers and practitioners to choose the right tool for efficient UX measurement from three questionnaire versions. This article summarizes the UEQ, its short version (UEQ-S) and a modular framework (UEQ+) with overall 27 UX factors and purposes in over 30 different languages. In addition, specific instructions and assistance are provided for the statistical evaluation and interpretation of the questionnaire results. With the help of a key performance indicator (KPI), benchmarks and an importance-performance analysis (IPA), the realization of UX measurements is made easier for researchers and practitioners. To make it even more convenient to choose the right questionnaire from the UEQ family, influencing factors on the UX measurement and recommendations for action are given. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2024-09-04T08:58:29Z No. of bitstreams: 1 Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation.pdf: 1322255 bytes, checksum: 92d13d179b4814ac6aea197ce85e8253 (MD5); Made available in DSpace on 2024-09-04T08:58:29Z (GMT). No. of bitstreams: 1 Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation.pdf: 1322255 bytes, checksum: 92d13d179b4814ac6aea197ce85e8253 (MD5)

Automatic Surveillance of People and Objects on Railway Tracks

Automatic Surveillance of People and Objects on Railway Tracks Martínez Núñez, Domingo; López Hernández, Fernando Carlos; Rainer Granados, J. Javier This paper describes the development and evaluation of a surveillance system for the detection of people and objects on railroad tracks in real time. Firstly, the paper evaluates several background subtraction techniques including CNNs and the object detection library called YOLO. Then we describe a novel strategy to mitigate the occlusion caused by the perspective of the camera and the integration of an alarms and pre-alarms policy. To evaluate its performance, we have implemented and automated the control and notification aspects of the surveillance system using computer vision techniques. This setup, running on a standard PC, achieves an average frame rate of 15 FPS and a latency of 0.54 seconds per frame, meeting real-time expectations in terms of both false alarms and precision in operational mode. The results from experiments conducted with a publicly available recorded video dataset from Metro de Madrid facilities demonstrate significant improvements over current state-of the-art solutions. These improvements include better accident anticipation and enhanced information provided to the operator using a standard low-cost camera. Consequently, we conclude that the approach described in this paper is both effective and a more practical, cost-efficient alternative to the other solutions reviewed. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2024-09-04T08:45:08Z No. of bitstreams: 1 Automatic Surveillance of People and Objects on Railway Tracks.pdf: 1743672 bytes, checksum: 5dfd567742e3def6b4c8446fdd9ca175 (MD5); Made available in DSpace on 2024-09-04T08:45:08Z (GMT). No. of bitstreams: 1 Automatic Surveillance of People and Objects on Railway Tracks.pdf: 1743672 bytes, checksum: 5dfd567742e3def6b4c8446fdd9ca175 (MD5)

Editor’s Note

Editor’s Note Mu-Yen, Chen; C. K. Hung, Patrick With (EC) the rise of global economy and Electronic Commerce (EC), efficient inter-organizational planning and deployment for value chain processes have become important. Radio-frequency Identification (RFID), Near Field Communication (NFC), and related wireless technologies are evaluated to be some of the most significant technological innovations in the twenty-first century. In the past few years, wireless and context-awareness technology have led to much hope and optimism. The mainstream press hails these innovations as the avant-garde in technology and business. The Internet of Everything (IoE) goal is the intelligent connection of people, process, data, and things. The IoE describes a world where billions of objects have sensors to detect, measure, and assess their status, all connected over public or private networks using standard and proprietary protocols. Hence, this special issue investigates the state-of-art AI and deep learning approaches for successful systems or applications in the IoE environment. In addition, this special issue also wants to understand the direct and indirect effects of using these smart technologies to build language information processing based on the Web of Things (WoT) around the smart cities and societies. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2024-09-04T07:42:11Z No. of bitstreams: 1 Editorial.pdf: 57324 bytes, checksum: 5223830d24ee8c02a8626180dc5aec60 (MD5); Made available in DSpace on 2024-09-04T07:42:11Z (GMT). No. of bitstreams: 1 Editorial.pdf: 57324 bytes, checksum: 5223830d24ee8c02a8626180dc5aec60 (MD5)

Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks for Safer Maritime Transportation

Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks for Safer Maritime Transportation Acarer, Tayfun Throughout history, maritime transportation has been preferred for international and intercontinental trade thanks to its lower cost than other transportation ways, which have a risk of ship accidents. To avoid these risks, underwater wireless sensor networks can be used as a robust and safe solution by monitoring maritime environment where energy resources are critical. Energy constraints must be solved to enable continuous data collection and communication for environmental monitoring and surveillance so they can last. Their energy limitations and battery replacement difficulties can be addressed with a path planning approach.This paper considers the energy-aware path planning problem with autonomous underwater vehicles by five commonly used approaches, namely, Ant Colony Optimization-based Approach, Particle Swarm Optimization-based Approach, Teaching Learning-based Optimization-based Approach, Genetic Algorithm-based Approach, Grey Wolf Optimizer-based Approach. Simulations show that the system converges faster and performs better with genetic algorithm than the others. This paper also considers the case where direct traveling paths between some node pairs should be avoided due to several reasons including underwater flows, too narrow places for travel, and other risks like changing temperature and pressure. To tackle this case, we propose a modified genetic algorithm, the Safety-Aware Genetic Algorithm-based Approach, that blocks the direct paths between those nodes. In this scenario, the Safety-Aware Genetic Algorithm-based approach provides just a 3% longer path than the Genetic Algorithm-based approach which is the best approach among all these approaches. This shows that the Safety-Aware Genetic Algorithm-based approach performs very robustly. With our proposed robust and energy-efficient path-planning algorithms, the data gathered by sensors can be collected very quickly with much less energy, which enables the monitoring system to respond faster for ship accidents. It also reduces total energy consumption of sensors by communicating them closely and so extends the network lifetime. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2024-09-03T15:34:28Z No. of bitstreams: 1 Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks for Safer Maritime Transportation.pdf: 1136398 bytes, checksum: c439f2ab4117c44a0ceb71f6494fcd27 (MD5); Made available in DSpace on 2024-09-03T15:34:28Z (GMT). No. of bitstreams: 1 Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks for Safer Maritime Transportation.pdf: 1136398 bytes, checksum: c439f2ab4117c44a0ceb71f6494fcd27 (MD5)

Predicting Consumer Electronics E-Commerce: Technology Acceptance Model and Logistics Service Quality

Predicting Consumer Electronics E-Commerce: Technology Acceptance Model and Logistics Service Quality Wu, Cheng-Feng; Zhang, Kunkun; Lin, Meng-Chen; Chiou, Chei-Chang In online shopping for consumer electronics, information and physical flows are crucial determinants of consumer purchase intentions. This study examines these factors by integrating the Technology Acceptance Model with logistics service quality, analyzing the relationship between retailers and consumers in e-commerce. The focus is on how information and physical flows, as critical supply chain elements, affect consumers' decisions to purchase online. A structural model and machine learning algorithm with SHapley Additive exPlanations are employed to analyze the data, providing a comprehensive analysis of the Technology Acceptance Model in conjunction with logistics service quality. The findings reveal that attitude, perceived usefulness, and informativeness are the most influential factors affecting consumers' purchase intention. This study contributes to the understanding of consumer behavior in the context of e-commerce platforms for consumer electronic products by integrating the Technology Acceptance Model and logistics service quality theoretical perspectives and analyzing the data using innovative techniques, specifically, Shapley Additive Explanations. This research offers valuable insights into the significant role of various features in shaping consumers' purchase intention in the context of online e-commerce platforms for consumer electrical products. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2024-09-03T15:20:07Z No. of bitstreams: 1 Predicting Consumer Electronics E-Commerce Technology Acceptance Model and Logistics Service Quality.pdf: 693126 bytes, checksum: 87919cc7e704958addc421323ece5da9 (MD5); Made available in DSpace on 2024-09-03T15:20:07Z (GMT). No. of bitstreams: 1 Predicting Consumer Electronics E-Commerce Technology Acceptance Model and Logistics Service Quality.pdf: 693126 bytes, checksum: 87919cc7e704958addc421323ece5da9 (MD5)

Design of Traffic Electronic Information Signal Acquisition System Based on Internet of Things Technology and Artificial Intelligence

Design of Traffic Electronic Information Signal Acquisition System Based on Internet of Things Technology and Artificial Intelligence Hongling, Wang This study aims to devise a traffic electronic information signal acquisition system employing Internet of Things and artificial intelligence technologies, offering a novel approach to address prevailing challenges related to traffic congestion and safety. Initially, the hardware circuit for the high-speed signal acquisition control core is developed, leveraging Field-Programmable Gate Array technology. This facilitates wireless monitoring of signal acquisition. Subsequently, a comprehensive time signal acquisition system is formulated, encompassing modules for communication, acquisition, storage, adaptive measurement, and signal analysis. The geomagnetic acquisition module within this system is utilized for collecting geomagnetic signals, which are then translated into switch signals indicating the presence or absence of vehicles. These signals are subsequently transmitted to the geomagnetic signal processor. Experimental results pertaining to the signal acquisition system reveal a notable peak storage speed of 200KB/s, considering the utilization of one million sampling points. Across a series of tests, the maximum relative error of the obtained results ranges from 2.2% to 2.7%, underscoring the consistency and reliability of the measurements. In comparison to existing testing devices, the system exhibits heightened accuracy in test results, rendering it more apt for traffic signal acquisition applications. In conclusion, this study accomplishes the collection and dissemination of diverse traffic information, furnishing robust support for traffic control and ensuring safe operations. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2024-09-03T15:01:46Z No. of bitstreams: 1 Design of Traffic Electronic Information Signal Acquisition System Based on Internet of Things Technology and Artificial Intelligence.pdf: 413839 bytes, checksum: 60a21bee29bd7ed62a03a51d3a79749c (MD5); Made available in DSpace on 2024-09-03T15:01:46Z (GMT). No. of bitstreams: 1 Design of Traffic Electronic Information Signal Acquisition System Based on Internet of Things Technology and Artificial Intelligence.pdf: 413839 bytes, checksum: 60a21bee29bd7ed62a03a51d3a79749c (MD5)

Semi-Supervised Machine Learning Approaches for Thyroid Disease Prediction and its Integration With the Internet of Everything

Semi-Supervised Machine Learning Approaches for Thyroid Disease Prediction and its Integration With the Internet of Everything Agraz, Melih Thyroid disorders are critical conditions that considerably affect a person’s general health, and may lead to additional health complications. Notably, these conditions often remain undetected in individuals who show "normal" results on traditional thyroid function tests. To enhance the diagnostic accuracy for thyroid disorders, such as hypothyroidism and hyperthyroidism, this study leveraged digital health records and explored semisupervised learning methods. We intentionally removed the labels from subjects initially categorized as "normal," incorporating them into our dataset as unlabeled data. The goal was to overcome the limitations of conventional diagnostic techniques, which may fail to detect subtle imbalances in thyroid hormones. In pursuit of this objective, we employed a combination of semi-supervised learning methods, namely FixMatch, Co-training, and self-training, in conjunction with supervised learning algorithms, specifically Naive Bayes and logistic regression. Our findings indicate that the FixMatch algorithm surpassed traditional supervised learning methods in various metrics, including accuracy (0.9054), sensitivity (0.9494), negative predictive value (0.9365), and F1 score (0.9146). Additionally, we propose a framework for integrating these diagnostic tools into the Internet of Everything (IoE) to promote early detection and facilitate improved healthcare outcomes. This research highlights the potential of semi-supervised learning techniques in the diagnosis of thyroid disorders and offers a roadmap for harnessing the IoE in healthcare advancement. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-08-07T15:34:48Z No. of bitstreams: 1 Semi-Supervised Machine Learning Approaches.pdf: 1416476 bytes, checksum: a99c331898f8c23044ac4ad09d770bf0 (MD5); Made available in DSpace on 2024-08-07T15:34:48Z (GMT). No. of bitstreams: 1 Semi-Supervised Machine Learning Approaches.pdf: 1416476 bytes, checksum: a99c331898f8c23044ac4ad09d770bf0 (MD5)

Constructing the Public Opinion Crisis Prediction Model Using CNN and LSTM Techniques Based on Social Network Mining

Constructing the Public Opinion Crisis Prediction Model Using CNN and LSTM Techniques Based on Social Network Mining Yan, Lou; Ren, Zhipeng; Zhang, Yong; Tao, Zhonghui; Zhao, Yizu This research endeavors to address the persistent dissemination of public opinion within social networks, mitigate the propagation of inappropriate content on these platforms, and enhance the overall service quality of social networks. To achieve these objectives, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) techniques are employed in this research to develop a predictive model for anticipating public opinion crises in social network mining. This model furnishes users with a valuable reference for subsequent decisionmaking processes. The initial phase of this research involves the collection of user behavior data from social networks using IoT technologies, serving as the basis for extensive big data analysis and neural network research. Subsequently, a social network text categorization model is constructed by amalgamating the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture, elucidating the training procedures of deep learning methodologies within CNN and LSTM networks. The effectiveness of this approach is subsequently validated through comparisons with other deep learning techniques. Based on the obtained results and findings, the CNN-LSTM model demonstrates a noteworthy accuracy rate of 92.19% and an exceptionally low loss value of 0.4075. Of particular significance is the classification accuracy of the CNN-LSTM algorithm within social network datasets, which surpasses that of alternative algorithms, including CNN (by 6.31%), LSTM (by 4.43%), RNN (by 3.51%), Transformer (by 40.29%), and Generative Adversarial Network (GAN) (by 4.49%). This underscores the effectiveness of the CNN-LSTM algorithm in the realm of social network text classification. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-08-07T15:29:05Z No. of bitstreams: 1 Constructing the Public Opinion Crisis Prediction Model Using CNN and LSTM Techniques Based on Social Network Mining.pdf: 3327316 bytes, checksum: 4dc82685d20fce25bac3f02ec4bed882 (MD5); Made available in DSpace on 2024-08-07T15:29:05Z (GMT). No. of bitstreams: 1 Constructing the Public Opinion Crisis Prediction Model Using CNN and LSTM Techniques Based on Social Network Mining.pdf: 3327316 bytes, checksum: 4dc82685d20fce25bac3f02ec4bed882 (MD5)

The Human Motion Behavior Recognition by Deep Learning Approach and the Internet of Things

The Human Motion Behavior Recognition by Deep Learning Approach and the Internet of Things Li, Hui; Liu, Huayang; Zhao, Wei; Liu, Hao This paper is dedicated to exploring the practical implementation of deep learning and Internet of Things (IoT) technology within systems designed for recognizing human motion behavior. It places a particular emphasis on evaluating performance in complex environments, aiming to mitigate challenges such as poor robustness and high computational workload encountered in conventional human motion behavior recognition approaches by employing Convolutional Neural Networks (CNN). The primary focus is on enhancing the performance of human motion behavior recognition systems for real-world scenarios, optimizing them for real-time accuracy, and enhancing their suitability for practical applications. Specifically, the paper investigates human motion behavior recognition using CNN, where the parameters of the CNN model are fine-tuned to improve recognition performance. The paper commences by delineating the process and methodology employed for human motion recognition, followed by an in-depth exploration of the CNN model's application in recognizing human motion behavior. To acquire data depicting human motion behavior in authentic settings, the Internet of Things (IoT) is utilized for extracting relevant information from the living environment. The dataset chosen for human motion behavior recognition is the Royal Institute of Technology (KTH) database. The analysis demonstrates that the network training loss function reaches a minimum value of 0.0001. Leveraging the trained CNN model, the recognition accuracy for human motion behavior achieves peak performance, registering an average accuracy of 94.41%. Notably, the recognition accuracy for static motion behavior generally exceeds that for dynamic motion behavior across different models. The CNN-based human motion behavior recognition method exhibits promising results in both static and dynamic behavior recognition scenarios. Furthermore, the paper advocates for the use of IoT in collecting human motion behavior data in real-world living environments, contributing to the advancement of human motion behavior recognition technology and its application in diverse domains such as intelligent surveillance and health management. The research findings carry significant implications for furthering the development of human motion behavior recognition technology and enhancing its applications in areas such as intelligent surveillance and health management. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-08-07T15:15:54Z No. of bitstreams: 1 The Human Motion Behavior Recognition.pdf: 4269382 bytes, checksum: f53f0f7796ab666535653b73a3c92f2a (MD5); Made available in DSpace on 2024-08-07T15:15:54Z (GMT). No. of bitstreams: 1 The Human Motion Behavior Recognition.pdf: 4269382 bytes, checksum: f53f0f7796ab666535653b73a3c92f2a (MD5)

Enhancing Tennis Serve Scoring Efficiency: An AI Deep Learning Approach

Enhancing Tennis Serve Scoring Efficiency: An AI Deep Learning Approach Liu, Jing-Wei The playing field of a tennis competition is a dynamic and complex formative environment given the following preliminary knowledge: (a) the basic technical, tactical, situational, and special types of shots used by the opponent; (b) the hitting area of the tennis player; (c) the place of service; (d) the ball drop position; and (d) batting efficiency and other related information that may improve the chances of victory. In this study, we propose an AI classification model for tennis serve scores. Using a deep learning algorithm, the model automatically tracks and classifies the serve scores of professional tennis players from video data. We first defined the players’ techniques, volleys, and placements of strokes and serves. Subsequently, we defined the referee's tennis terms and the voice in deciding on a serve score. Finally, we developed a deep learning model to automatically classify the serving position, landing position, and use of tennis techniques. The methodology was applied in the context of 10 matches played by Roger Federer and Rafael Nadal. The proposed deep learning algorithm achieved a 98.27% accuracy in the automatic classification of serve scores, revealing that Nadal outscored Federer by 2.1% in terms of serve-scoring efficiency. These results are expected to facilitate the automatic comparison and classification of shots in future studies, enabling coaches to adjust tactics in a timely manner and thereby improve the chances of winning. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-08-07T09:57:58Z No. of bitstreams: 1 Enhancing Tennis Serve Scoring Efficiency.pdf: 605618 bytes, checksum: 3d89479d4e4a17f81ca2de5e1e9469e8 (MD5); Made available in DSpace on 2024-08-07T09:57:58Z (GMT). No. of bitstreams: 1 Enhancing Tennis Serve Scoring Efficiency.pdf: 605618 bytes, checksum: 3d89479d4e4a17f81ca2de5e1e9469e8 (MD5)

Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data

Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data Galphade, Manisha; Nikam, V. B.; Banerjee, Biplab; Kiwelekar, Arvind W.; Sharma, Priyanka Currently, wind power is the fast growing area in the domain of renewable energy generation. Accurate prediction of wind power output in wind farms is crucial for addressing the challenges associated the power grid. This precise forecasting enables grid operators to enhance safety and optimize grid operations by effectively managing fluctuations in power generation, ensuring a reliable and stable energy supply. In recent years, there has been a significant rise in research and investigations conducted in this field. This study aims to develop a multivariate short-term wind power forecasting (WPF) model with the objective of enhancing forecasting precision. Among the various prediction models, deep learning models such as Long Short-Term Memory (LSTM) have demonstrated outstanding performance in the field of WPF. By adding multiple layers of LSTM networks, the model can capture more complex patterns. To improve the performance, data preprocessing is carried out using two techniques such as removal of missing values and imputing missing values using Random Forest Regressor (RFR). The comparison between the proposed Stacked LSTM model and other methods including vector autoregressive (VAR), Multiple Linear Regression, Gated Recurrent Unit (GRU) and Bidirectional LSTM (BiLSTM) has been experimented on two datasets. The experimental results show that after imputing missing values using RFR, the Stacked LSTM is optimized model for better performance than above mentioned reference models. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-08-07T09:45:34Z No. of bitstreams: 1 Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.pdf: 4664292 bytes, checksum: b0bcc11428bf8e464f01f44dc7503748 (MD5); Made available in DSpace on 2024-08-07T09:45:35Z (GMT). No. of bitstreams: 1 Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.pdf: 4664292 bytes, checksum: b0bcc11428bf8e464f01f44dc7503748 (MD5)

Posture Estimation of Curve Running Motion Using Nano-Biosensor and Machine Learning

Posture Estimation of Curve Running Motion Using Nano-Biosensor and Machine Learning Wu, Xiaoming; Cao, Yu; Wang, Yu; Li, Bing; Yang, Haitao; Raja, S.P. Curve running is a common form of training and competition. Conducting research on posture estimation during curve running can provide more accurate training and competition data for athletes. However, due to the unique nature of curve running, traditional posture estimation methods neglect the temporal changes in athlete posture, resulting in a decrease in estimation accuracy. Therefore, a posture estimation method for curve running motion using nano-biosensor and machine learning is proposed. First, the motion parameters of humans are collected by nano-biosensor, and the posture coordinates are obtained preliminarily. Second, the posture coordinates are established according to the human motion parameters, and the curve running posture data is obtained and filtered to obtain more accurate data. Finally, the Bayesian network in machine learning is used to continuously track the posture, and a nonlinear equation is established to fuse the posture angle obtained by the sensor and the posture tracked by the Bayesian network, to realize the posture estimation of curve running motion. The results show that the proposed estimation method has a good motion posture estimation effect, and the hip joint estimation error, knee joint estimation error and ankle joint estimation error are all less than 5°, and the endpoint displacement estimation offset rate is less than 2%. It can realize accurate motion posture estimation of curve running motion, and has important application value in the field of track training. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-08-07T09:33:41Z No. of bitstreams: 1 Posture Estimation of Curve Running Motion Using.pdf: 1581880 bytes, checksum: 7224bfc356a2127cf2d323af67935337 (MD5); Made available in DSpace on 2024-08-07T09:33:41Z (GMT). No. of bitstreams: 1 Posture Estimation of Curve Running Motion Using.pdf: 1581880 bytes, checksum: 7224bfc356a2127cf2d323af67935337 (MD5)

Editor’s Note

Editor’s Note Montenegro-Marin, Carlos Enrique This regular issue consists of 16 articles that use artificial intelligence or computational systems to come up with new solutions and solve problems more effectively. The issue showcases the use of Artificial Intelligence or computational systems that contribute to new knowledge with innovative applications. In this issue you can find different articles on game theory, models for collaborative filtering, text classification, fake news detection system, identification system, semi eager classifier, longitudinal segmented analysis, etc. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-06-14T11:07:13Z No. of bitstreams: 1 Editor’s Note.pdf: 56673 bytes, checksum: 6eb61f727eb6292a27ecad841527463a (MD5); Made available in DSpace on 2024-06-14T11:07:13Z (GMT). No. of bitstreams: 1 Editor’s Note.pdf: 56673 bytes, checksum: 6eb61f727eb6292a27ecad841527463a (MD5)

Anti-Diabetic Therapeutic Medicinal Plant Identification Using Deep Fused Discriminant Subspace Ensemble (D2 SE)

Anti-Diabetic Therapeutic Medicinal Plant Identification Using Deep Fused Discriminant Subspace Ensemble (D2 SE) Sasikaladevi, N.; Pradeepa, S.; Revathi, A.; Vimal, S.; Dhiman, Gaurav About 422 million people worldwide have diabetes, the majority living in low-and middle-income countries, and 1.5 million deaths are directly attributed to diabetes each year. According to the Botanical Survey of India, India is home to more than 8,000 species of medicinal plants. The natural medications with antidiabetic activity are widely formulated because they have better compatibility with human body, are easily available and have less side effects. They may act as an alternative source of antidiabetic agents. The fused deep neural network (DNN) model with Discriminant Subspace Ensemble is designed to identify the diabetic plants from VNPlant200 data set. Here, the deep features are extracted using DenseNet201 and the matrix-based discriminant analysis is adopted to learn the discriminative feature subspace for classification. To further improve the performance of discriminative subspace, a nearest neighbors technique is used to produce a subspace ensemble for final diabetic therapeutic medicinal plant image classification. The developed model attained the highest accuracy of 97.5% which is better compared to other state of art algorithms. Finally, the model is integrated into a mobile app for robust classification of anti-diabetic therapeutic medicinal plant with real field images. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-06-12T13:46:04Z No. of bitstreams: 1 Anti-Diabetic Therapeutic Medicinal Plant Identification Using Deep Fused Discriminant Subspace Ensemble (D2 SE).pdf: 2837535 bytes, checksum: 84a1a31340b5d4c20faa500db155e513 (MD5); Made available in DSpace on 2024-06-12T13:46:04Z (GMT). No. of bitstreams: 1 Anti-Diabetic Therapeutic Medicinal Plant Identification Using Deep Fused Discriminant Subspace Ensemble (D2 SE).pdf: 2837535 bytes, checksum: 84a1a31340b5d4c20faa500db155e513 (MD5)

User Revocation-Enabled Access Control Model Using Identity-Based Signature in the Cloud Computing Environment

User Revocation-Enabled Access Control Model Using Identity-Based Signature in the Cloud Computing Environment Kumar, Tarun; Kumar, Prabhat; Namasudra, Suyel Nowadays, a lot of data is stored in the cloud for sharing purposes across various domains. The increasing number of security issues with cloud data raises confidentiality concerns about keeping these stored or shared data. Advanced encryption and decryption techniques in cloud computing environments can be considered useful to achieve this aspect. However, an unresolved yet critical challenge in cloud data-sharing systems is the revocation of malicious users. One of the common methods for revocation involves periodically updating users' private keys. This approach increases the workload of the Key Generation Center (KGC) as the number of users increases. In this work, an efficient Revocable Identity-Based Signature (RIBS) scheme is proposed, wherein the revocation functionality is delegated to an External Revocation Server (ERS). This proposed scheme allows only the non-revoked users to access the system resources, thus, providing restricted access control. Here, the ERS generates a secret time key for signature generation based on a revoked user list. In the proposed method, a user uses its private key and secret time key to sign a message. Furthermore, to maintain data confidentiality, symmetric encryption and Elliptic Curve Cryptography (ECC) based asymmetric encryption techniques are used before outsourcing data to the cloud server. The results illustrate that the proposed scheme outperforms some of the existing schemes by providing reduced computation costs. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-06-12T13:32:42Z No. of bitstreams: 1 User Revocation-Enabled Access Control Model Using Identity-Based Signature in the Cloud Computing Environment.pdf: 889672 bytes, checksum: 40f9013559d6d5cdf1211875ce5d9189 (MD5); Made available in DSpace on 2024-06-12T13:32:42Z (GMT). No. of bitstreams: 1 User Revocation-Enabled Access Control Model Using Identity-Based Signature in the Cloud Computing Environment.pdf: 889672 bytes, checksum: 40f9013559d6d5cdf1211875ce5d9189 (MD5)

Lightweight Real-Time Recurrent Models for Speech Enhancement and Automatic Speech Recognition

Lightweight Real-Time Recurrent Models for Speech Enhancement and Automatic Speech Recognition Dhahbi, Sami; Saleem, Nasir; Gunawan, Teddy Surya; Bourouis, Sami; Ali, Imad; Trigui, Aymen; Algarni, Abeer D. Traditional recurrent neural networks (RNNs) encounter difficulty in capturing long-term temporal dependencies. However, lightweight recurrent models for speech enhancement are important to improve noisy speech, while being computationally efficient and able to capture long-term temporal dependencies efficiently. This study proposes a lightweight hourglass-shaped model for speech enhancement (SE) and automatic speech recognition (ASR). Simple recurrent units (SRU) with skip connections are implemented where attention gates are added to the skip connections, highlighting the important features and spectral regions. The model operates without relying on future information that is well-suited for real-time processing. Combined acoustic features and two training objectives are estimated. Experimental evaluations using the short time speech intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and word error rates (WERs) indicate better intelligibility, perceptual quality, and word recognition rates. The composite measures further confirm the performance of residual noise and speech distortion. With the TIMIT database, the proposed model improves the STOI and PESQ by 16.21% and 0.69 (31.1%) whereas with the LibriSpeech database, the model improves STOI by 16.41% and PESQ by 0.71 (32.9%) over the noisy speech. Further, our model outperforms other deep neural networks (DNNs) in seen and unseen conditions. The ASR performance is measured using the Kaldi toolkit and achieves 15.13% WERs in noisy backgrounds. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-05-13T16:16:38Z No. of bitstreams: 1 Lightweight Real-Time Recurrent Models for Speech Enhancement and Automatic Speech Recognition.pdf: 2470761 bytes, checksum: 74a0aaefed5f55e8bfcb6ef7c937544c (MD5); Made available in DSpace on 2024-05-13T16:16:38Z (GMT). No. of bitstreams: 1 Lightweight Real-Time Recurrent Models for Speech Enhancement and Automatic Speech Recognition.pdf: 2470761 bytes, checksum: 74a0aaefed5f55e8bfcb6ef7c937544c (MD5)

Evaluating the Impact of Pumping on Groundwater Level Prediction in the Chuoshui River Alluvial Fan Using Artificial Intelligence Techniques

Evaluating the Impact of Pumping on Groundwater Level Prediction in the Chuoshui River Alluvial Fan Using Artificial Intelligence Techniques Su, Yu-Sheng; Hu, Yu-Cheng; Wu, Yun-Chin; Lo, Ching-Teng Over the past decade, excessive groundwater extraction has been the leading cause of land subsidence in Taiwan's Chuoshui River Alluvial Fan (CRAF) area. To effectively manage and monitor groundwater resources, assessing the effects of varying seasonal groundwater extraction on groundwater levels is necessary. This study focuses on the CRAF in Taiwan. We applied three artificial intelligence techniques for three predictive models: multiple linear regression (MLR), support vector regression (SVR), and Long Short-Term Memory Networks (LSTM). Each prediction model evaluated the extraction rate, considering temporal and spatial correlations. The study aimed to predict groundwater level variations by comparing the results of different models. This study used groundwater level and extraction data from the CRAF area in Taiwan. The dataset we constructed was the input variable for predicting groundwater level variations. The experimental results show that the LSTM method is the most suitable and stable deep learning model for predicting groundwater level variations in the CRAF, Taiwan, followed by the SVR method and finally the MLR method. Additionally, when considering different distances and depths of pumping data at groundwater level monitoring stations, it was found that the Guosheng and Hexing groundwater level monitoring stations are best predicted using pumping data within a distance of 20 kilometers and a depth of 20 meters. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-05-13T16:01:02Z No. of bitstreams: 1 Evaluating the Impact of Pumping on Groundwater Level Prediction in the Chuoshui River Alluvial Fan Using Artificial Intelligence Techniques.pdf: 2739489 bytes, checksum: 8b91534008eeb41e6c20dd05d89810a1 (MD5); Made available in DSpace on 2024-05-13T16:01:02Z (GMT). No. of bitstreams: 1 Evaluating the Impact of Pumping on Groundwater Level Prediction in the Chuoshui River Alluvial Fan Using Artificial Intelligence Techniques.pdf: 2739489 bytes, checksum: 8b91534008eeb41e6c20dd05d89810a1 (MD5)

Learning Analytics Icons: Easy Comprehension of Data Treatment

Learning Analytics Icons: Easy Comprehension of Data Treatment Amo-Filva, David; Alier, Marc; Fonseca, David; García-Peñalvo, Francisco; Casañ, María José The Learning Analytics approach adopted in education implies the gathering and processing of sensitive information and the generation of student profiles, which may have direct or indirect dire consequences for the students. The Educational institutions must manage this data processing according to the General Data Protection Regulation, respecting its principles of fairness when it comes to information gathering and processing. This implies that the students must be well informed and give explicit consent before their information is gathered and processed. The GDPR propose the usage of recognizable standardized icons to facilitate a general understanding and awareness of how personal data is deemed to be processed in each application context, like an online course. This paper presents a project that aims to provide a set of icons to inform about the treatment of educational data in the Learning Analytics processes and a survey about the student's comprehension of the icons, their meaning, and implications for their privacy and confidentiality. The result presented is a set of icons ready to be integrated into educational environments that apply Learning Analytics to increase transparency and facilitate the understanding of data processing. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-05-13T15:40:44Z No. of bitstreams: 1 Learning Analytics Icons.pdf: 3014284 bytes, checksum: 95d65cc7e996eb92d9d9269016b1c905 (MD5); Made available in DSpace on 2024-05-13T15:40:44Z (GMT). No. of bitstreams: 1 Learning Analytics Icons.pdf: 3014284 bytes, checksum: 95d65cc7e996eb92d9d9269016b1c905 (MD5)

Combating Misinformation and Polarization in the Corporate Sphere: Integrating Social, Technological and AI Strategies

Combating Misinformation and Polarization in the Corporate Sphere: Integrating Social, Technological and AI Strategies Tejero, Alberto; Pisoni, Galena; Lashkari, Ziba Habibi; Rios-Aguilar, Sergio In an era where misinformation and polarization present significant challenges, this research examines the root causes within social networks and assesses how corporations can use AI technologies for prompt detection. This research uses a dual approach: a "telephone game" with 225 participants from a Spanish university to study the spread of misinformation, and interviews with 15 experts from three French tech companies to investigate technological solutions. The findings indicate that almost one-third of participants inadvertently contribute to polarization, and around one-quarter propagated misinformation. The study also identifies the existing tools enhanced by AI and Machine Learning that effectively detect misinformation and polarization in corporate settings. This investigation provides crucial insights for practitioners to strengthen their strategies against misinformation and technical challenges and opportunities. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-04-04T10:34:45Z No. of bitstreams: 1 Combating Misinformation and Polarization in the Corporate Sphere.pdf: 826855 bytes, checksum: 5cb46bb4803eff5b6e621fbd28ae934d (MD5); Made available in DSpace on 2024-04-04T10:34:45Z (GMT). No. of bitstreams: 1 Combating Misinformation and Polarization in the Corporate Sphere.pdf: 826855 bytes, checksum: 5cb46bb4803eff5b6e621fbd28ae934d (MD5)

Platform for Improving the User Experience in the Creation of Educational Multiplayer Video Games

Platform for Improving the User Experience in the Creation of Educational Multiplayer Video Games Sánchez-Canella, Fernando; Pascual-Espada, Jordán; Cid-Rico, Irene Students’ motivation is one of the factors that directly affect academic performance. In recent years, teachers are looking for ways to motivate students during their training period. For example, making use of slides, videos, films, comics or games to increase students' motivation to improve their learning experience. Some research works have revealed that multiplayer games which include cooperation and competition, among other factors, are an extraordinary tool for enhancing students’ motivation. Current alternatives make it very complex for teachers to create multiplayer games for their students. The definition of the game requires many configurations and even technical knowledge. This research proposes a new platform that allows teachers to create multiplayer video games in a simple and fast way, improving the game creation process over current alternatives. The resulting games are also designed for to improve the student experience, and make it fun. These games do not only include trivia questions, but also use functional mechanisms from video games. The design of the generated games allows students to master the games in a short period of time during their classes Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-04-04T10:22:31Z No. of bitstreams: 1 Platform for Improving the User Experience in the Creation of Educational Multiplayer Video Games.pdf: 937491 bytes, checksum: 67db8e057c9c8add1aa9c7733f453ba7 (MD5); Made available in DSpace on 2024-04-04T10:22:31Z (GMT). No. of bitstreams: 1 Platform for Improving the User Experience in the Creation of Educational Multiplayer Video Games.pdf: 937491 bytes, checksum: 67db8e057c9c8add1aa9c7733f453ba7 (MD5)

What Drives IoT-Based Smart Pet Appliances Usage Intention? The Perspective of the Unified Theory of Acceptance and Use of Technology Model

What Drives IoT-Based Smart Pet Appliances Usage Intention? The Perspective of the Unified Theory of Acceptance and Use of Technology Model Chen, Chia-Chen; Lin, Chia-Pei The advancement of IOT (Internet of Things) has facilitated the development of smart pet appliances, and the market for these products has growing rapidly, this study seeks to identify key factors for pet owner adoption of “smart” pet appliances. The Unified Theory of Acceptance and Use of Technology (UTAUT) a wellestablished model in the field of IOT research is used as the main framework, integrating brand trust, perceived value and perceived enjoyment as the basis for hypothesis formulation and testing based on data collected through questionnaires distributed through online social platforms. Reliability analysis, validity analysis and structural equation model analysis were carried out through confirmatory factor analysis to test the variables and research hypotheses. Results for the UTAUT indicate that effort expectancy has a direct impact on performance expectancy, while performance expectancy, effort expectancy and facilitating condition all have a positive impact on intention. While social influence does not directly or significantly affect use intention, it can indirectly affect intention through perceived value and perceived enjoyment. Brand trust does not have a significant impact on use intention, but can indirectly affect use intention through perceived value. This study further compares user age and number of smart pet home appliances owned to better understand the impact of demographic factors. Findings indicate that, for users under the age of 30, effort expectancy has no significant impact on use intention, while brand trust has no significant impact on perceived value among users over 30. Among the research results based on age as a basis, the impact of hardships in the ethnic group in the age of 30 is not significant, nor do facilitating conditions or perceived value have significant impact on use intention. For users with one smart pet device at home, neither favorable conditions not perceived value have significant impact on use intention, while for users with two smart pet devices, perceived enjoyment does not significantly impact use intention. These finding have potential reference value for future related research in the IOT or smart pet home appliance research field. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-04-04T09:47:52Z No. of bitstreams: 1 What Drives IoT-Based Smart Pet Appliances Usage Intention.pdf: 643943 bytes, checksum: efc27e1ab5afdf7df22a3f0a26d725b9 (MD5); Made available in DSpace on 2024-04-04T09:47:52Z (GMT). No. of bitstreams: 1 What Drives IoT-Based Smart Pet Appliances Usage Intention.pdf: 643943 bytes, checksum: efc27e1ab5afdf7df22a3f0a26d725b9 (MD5)

Optimal Target-Oriented Knowledge Transportation For Aspect-Based Multimodal Sentiment Analysis

Optimal Target-Oriented Knowledge Transportation For Aspect-Based Multimodal Sentiment Analysis Zhang, Linhao; Jin, Li; Xu, Guangluan; Li, Xiaoyu; Sun, Xian; Zhang, Zequn; Zhang, Yanan; Li, Qui Aspect-based multimodal sentiment analysis under social media scenario aims to identify the sentiment polarities of each aspect term, which are mentioned in a piece of multimodal user-generated content. Previous approaches for this interdisciplinary multimodal task mainly rely on coarse-grained fusion mechanisms from the data-level or decision-level, which have the following three shortcomings:(1) ignoring the category knowledge of the sentiment target mentioned in the text) in visual information. (2) unable to assess the importance of maintaining target interaction during the unimodal encoding process, which results in indiscriminative representations considering various aspect terms. (3) suffering from the semantic gap between multiple modalities. To tackle the above challenging issues, we propose an optimal target-oriented knowledge transportation network (OtarNet) for this task. Firstly, the visual category knowledge is explicitly transported through input space translation and reformulation. Secondly, with the reformulated knowledge containing the target and category information, the target sensitivity is well maintained in the unimodal representations through a multistage target-oriented interaction mechanism. Finally, to eliminate the distributional modality gap by integrating complementary knowledge, the target-sensitive features of multiple modalities are implicitly transported based on the optimal transport interaction module. Our model achieves state-of-theart performance on three benchmark datasets: Twitter-15, Twitter-17 and Yelp, together with the extensive ablation study demonstrating the superiority and effectiveness of OtarNet. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-14T08:25:07Z No. of bitstreams: 1 Optimal Target-Oriented Knowledge Transportation For Aspect-Based Multimodal Sentiment Analysis.pdf: 4233371 bytes, checksum: ced0026a05664ba2651abb2695e17303 (MD5); Made available in DSpace on 2024-03-14T08:25:07Z (GMT). No. of bitstreams: 1 Optimal Target-Oriented Knowledge Transportation For Aspect-Based Multimodal Sentiment Analysis.pdf: 4233371 bytes, checksum: ced0026a05664ba2651abb2695e17303 (MD5)

Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence

Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence Ordoñez, Hugo; Timarán-Pereira, Ricardo; González-Sanabria, Juan-Sebastián Introduction: Currently, homelessness should not be seen as just another problem, but as a reality of inequality and the absence of social justice. In this sense, homeless people are subjected to social disengagement, lack of job opportunities or the instability of these, insecurity circumstances, these aspects being one of the causes associated with the consumption or addiction to psychoactive substances. Data: To define the proposed approach, data from the Census of Street Inhabitants - CHC- 2021 of the National Administrative Department of Statistics (DANE), which contains 19,375 records and 25 columns, were used. Methodology: This article presents an artificial intelligence approach that implements a model based on machine learning algorithms for identifying addiction trends to psychoactive substances in street dwellers in Colombia. Conclusions: Based on the results obtained, it is evident that the approach can serve as a support for decision making by municipal administrations in the definition of social public policies for the street-dwelling population in Colombia. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-14T08:09:41Z No. of bitstreams: 1 Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence.pdf: 1336440 bytes, checksum: ec9d9c591214655d5dcfd2d716316b3e (MD5); Made available in DSpace on 2024-03-14T08:09:41Z (GMT). No. of bitstreams: 1 Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence.pdf: 1336440 bytes, checksum: ec9d9c591214655d5dcfd2d716316b3e (MD5)

An Effective Prediction Approach for the Management of Children Victims of Road Accidents

An Effective Prediction Approach for the Management of Children Victims of Road Accidents Saadi, F.; Baghdad, Atmani; Henni, F.; Benfriha, H.; Addou, Z.; Guerbouz, R. Road traffic generates a considerable number of accidents each year. The management of injuries caused by these accidents is becoming a real public health problem. Faced with this latter, we propose a new clinical decision making approach based on case-based reasoning (CBR) and data mining (DM) techniques to speed up and improve the care of an injured child. The main idea is to preprocess the dataset before using K Nearest Neighbor (KNN) Classification Model. In this paper, an efficient predictive model is developed to predict the admission procedure of a child victim of a traffic accident in pediatric intensive care units. The evaluation of the proposed model is conducted on a real dataset elaborated by the authors and validated by statistical analysis. This novel model executes a selection of relevant attributes using data mining technique and integrates a CBR system to retrieve similar cases from an archive of cases of patients successfully treated with the proposed treatment plan. The results revealed that the proposed approach outperformed other models and the results of previous studies by achieving an accuracy of 91.66%. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-14T07:52:35Z No. of bitstreams: 1 An Effective Prediction Approach for the Management of Children Victims of Road Accidents.pdf: 1178579 bytes, checksum: 0cb3e65716dc21576ea41f3d59ad934e (MD5); Made available in DSpace on 2024-03-14T07:52:35Z (GMT). No. of bitstreams: 1 An Effective Prediction Approach for the Management of Children Victims of Road Accidents.pdf: 1178579 bytes, checksum: 0cb3e65716dc21576ea41f3d59ad934e (MD5)

Generative Artificial Intelligence in Education: From Deceptive to Disruptive

Generative Artificial Intelligence in Education: From Deceptive to Disruptive Alier, Marc; García-Peñalvo, Francisco; Camba, Jorge D. Generative Artificial Intelligence (GenAI) has emerged as a promising technology that can create original content, such as text, images, and sound. The use of GenAI in educational settings is becoming increasingly popular and offers a range of opportunities and challenges. This special issue explores the management and integration of GenAI in educational settings, including the ethical considerations, best practices, and opportunities. The potential of GenAI in education is vast. By using algorithms and data, GenAI can create original content that can be used to augment traditional teaching methods, creating a more interactive and personalized learning experience. In addition, GenAI can be utilized as an assessment tool and for providing feedback to students using generated content. For instance, it can be used to create custom quizzes, generate essay prompts, or even grade essays. The use of GenAI as an assessment tool can reduce the workload of teachers and help students receive prompt feedback on their work. Incorporating GenAI in educational settings also poses challenges related to academic integrity. With availability of GenAI models, students can use them to study or complete their homework assignments, which can raise concerns about the authenticity and authorship of the delivered work. Therefore, it is important to ensure that academic standards are maintained, and the originality of the student's work is preserved. This issue highlights the need for implementing ethical practices in the use of GenAI models and ensuring that the technology is used to support and not replace the student's learning experience. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T13:44:15Z No. of bitstreams: 1 Generative Artificial Intelligence in Education.pdf: 978561 bytes, checksum: 533e391d47ba81cd9a858d8b510f476a (MD5); Made available in DSpace on 2024-03-12T13:44:15Z (GMT). No. of bitstreams: 1 Generative Artificial Intelligence in Education.pdf: 978561 bytes, checksum: 533e391d47ba81cd9a858d8b510f476a (MD5)

Ethical Implications and Principles of Using Artificial Intelligence Models in the Classroom: A Systematic Literature Review

Ethical Implications and Principles of Using Artificial Intelligence Models in the Classroom: A Systematic Literature Review Tang, Lin; Su, Yu-Sheng The increasing use of artificial intelligence (AI) models in the classroom not only brings a large number of benefits, but also has a variety of ethical implications. To provide effective education, it is now necessary to understand the ethical implications of using AI models in the classroom, and the principles for avoiding and addressing these ethical implications. However, existing research on the ethical implications of using AI models in the classroom is rather sparse, and a holistic overview is lacking. Therefore, this study seeks to offer an overview of research on the ethical implications, ethical principles and the future research directions and practices of using AI models in the classroom through a systematic literature review. Out of 1,445 initially identified publications between 2013 and 2023, 32 articles were included for final coding analysis, identified using explicit inclusion and exclusion criteria. The findings revealed five main ethical implications, namely algorithmic bias and discrimination, data privacy leakage, lack of transparency, decreased autonomy, and academic misconduct, with algorithmic bias being the most prominent (i.e., the number of existing studies is the most), followed by privacy leakage, whereas decreased autonomy and academic misconduct were relatively understudied; and six main ethical principles, namely fairness, privacy, transparency, accountability, autonomy and beneficence, with fairness being the most prominent ethical principle (i.e., the number of existing studies is the most), followed by privacy, while autonomy and beneficence were relatively understudied. Future directions of research are given, and guidelines for future practice are provided: (1) further substantive discussion, understanding and solution of ethical implications are required; (2) the precise mechanism of ethical principles of using AI models in the classroom remains to be elucidated and extended to the implementation phase; and (3) the ethical implications of the use of AI models in the classroom require accurate assessment. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T13:30:39Z No. of bitstreams: 1 Ethical Implications and Principles of Using Artificial Intelligence Models in the Classroom.pdf: 454767 bytes, checksum: 3722e4324633951952250b959168ccec (MD5); Made available in DSpace on 2024-03-12T13:30:39Z (GMT). No. of bitstreams: 1 Ethical Implications and Principles of Using Artificial Intelligence Models in the Classroom.pdf: 454767 bytes, checksum: 3722e4324633951952250b959168ccec (MD5)

Can Generative AI Solve Geometry Problems? Strengths and Weaknesses of LLMs for Geometric Reasoning in Spanish

Can Generative AI Solve Geometry Problems? Strengths and Weaknesses of LLMs for Geometric Reasoning in Spanish Parra, Verónica; Sureda, Patricia; Corica, Ana; Schiaffino, Silvia; Godoy, Daniela Generative Artificial Intelligence (AI) has emerged as a disruptive technology that is challenging traditional teaching and learning practices. Question-answering in natural language fosters the use of chatbots, such as ChatGPT, Bard and others, that generate text based on pre-trained Large Language Models (LLMs). The performance of these models in certain areas, like Math problem solving is receiving a crescent attention as it directly impacts on its potential use in educational settings. Most of these evaluations, however, concentrate on the construction and use of benchmarks comprising diverse Math problems in English. In this work, we discuss the capabilities of most used LLMs within the subfield of Geometry, in view of the relevance of this subject in high-school curricula and the difficulties exhibited by even most advanced multimodal LLMs to deal with geometric notions. This work focuses on Spanish, which is additionally a less resourced language. The answers of three major chatbots, based on different LLMs, were analyzed not only to determine their capacity to provide correct solutions, but also to categorize the errors found in the reasoning processes described. Understanding LLMs strengths and weaknesses in a field like Geometry can be a first step towards the design of more informed methodological proposals to include these technologies in classrooms as well as the development of more powerful automatic assistance tools based on generative AI. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T13:21:58Z No. of bitstreams: 1 Can Generative AI Solve Geometry Problems.pdf: 2743517 bytes, checksum: fe171389fc630d3009e3563d71f1ece1 (MD5); Made available in DSpace on 2024-03-12T13:21:58Z (GMT). No. of bitstreams: 1 Can Generative AI Solve Geometry Problems.pdf: 2743517 bytes, checksum: fe171389fc630d3009e3563d71f1ece1 (MD5)

A Cybernetic Perspective on Generative AI in Education: From Transmission to Coordination

A Cybernetic Perspective on Generative AI in Education: From Transmission to Coordination Griffiths, Dai; Frías-Martínez, Enrique; Tlili, Ahmed; Burgos, Daniel The recent sudden increase in the capabilities of Large Language Models (LLMs), and generative AI in general, has astonished education professionals and learners. In formulating a response to these developments, educational institutions are constrained by a lack of clarity concerning human-machine communication and its relationship to models of education. Ideas and models from the cybernetic tradition can help to fill this gap. Two paradigms are distinguished: (1) the transmission paradigm (combining the model of learning implied by the instruments and processes of formal education and the conduit model of communication), and (2) the coordination paradigm (combining the constructivist model of learning and the coordination model of communication). It is proposed that these paradigms have long coexisted in educational practice in a modus vivendi, which is disrupted by LLMs. If an LLM can pass an examination, then from within the transmission paradigm this can only understood as demonstrating that the LLM has indeed learned and understood the material being assessed. At the same time, we know that LLMs do not in fact have the capacity to learn and understand, but rather generate a simulacrum of intelligence. It is argued that this paradox prevents educational institutions from formulating a coherent response to generative AI systems. However, within the coordination paradigm the interactions of LLMs and education institutions can be more easily understood and can be situated in a conversational model of learning. These distinctions can help institutions, educational leaders, and teachers, to frame the complex and nuanced questions raised by GenAI, and to chart a course towards its effective use in education. More specifically, they indicate that to benefit fully from the capabilities of generative AI education institutions need to recognize the validity of the coordination paradigm and adapt their processes and instruments accordingly. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T13:07:12Z No. of bitstreams: 1 A Cybernetic Perspective on Generative AI in Education.pdf: 547220 bytes, checksum: da8b47a5871354b7eada1c123edbb5cc (MD5); Made available in DSpace on 2024-03-12T13:07:12Z (GMT). No. of bitstreams: 1 A Cybernetic Perspective on Generative AI in Education.pdf: 547220 bytes, checksum: da8b47a5871354b7eada1c123edbb5cc (MD5)

Virtual Reality and Language Models, a New Frontier in Learning

Virtual Reality and Language Models, a New Frontier in Learning Izquierdo-Domenech, Juan; Linares-Pellicer, Jordi; Ferri-Molla, Isabel The proposed research introduces an innovative Virtual Reality (VR) and Large Language Model (LLM) architecture to enhance the learning process across diverse educational contexts, ranging from school to industrial settings. everaging the capabilities of LLMs and Retrieval-Augmented Generation (RAG), the architecture centers around an immersive VR application. This application empowers students of all backgrounds to interactively engage with their environment by posing questions and receiving informative responses in text format and with visual hints in VR, thereby fostering a dynamic learning experience. LLMs with RAG act as the backbones of this architecture, facilitating the integration of private or domain-specific data into the learning process. By seamlessly connecting various data sources through data connectors, RAG overcomes the challenge of disparate and siloed information repositories, including APIs, PDFs, SQL databases, and more. The data indexes provided by RAG solutions further streamline this process by structuring the ingested data into formats optimized for consumption by LLMs. An empirical study was conducted to evaluate the effectiveness of this VR and LLM architecture. Twenty participants, divided into Experimental and Control groups, were selected to assess the impact on their learning process. The Experimental group utilized the immersive VR application, which allowed interactive engagement with the educational environment, while the Control group followed traditional learning methods. The study revealed significant improvements in learning outcomes for the Experimental group, demonstrating the potential of integrating VR and LLMs in enhancing comprehension and engagement in learning contexts. This study presents an innovative approach that capitalizes on the synergy between LLMs and immersive VR technology, opening avenues for a transformative learning experience that transcends traditional boundaries and empowers learners across a spectrum of educational landscapes. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T12:54:12Z No. of bitstreams: 1 Virtual Reality and Language Models, a New Frontier in Learning.pdf: 2275154 bytes, checksum: 2dbd03feb08da813e2b458ff33488a46 (MD5); Made available in DSpace on 2024-03-12T12:54:12Z (GMT). No. of bitstreams: 1 Virtual Reality and Language Models, a New Frontier in Learning.pdf: 2275154 bytes, checksum: 2dbd03feb08da813e2b458ff33488a46 (MD5)

Generative Artificial Intelligence in Product Design Education: Navigating Concerns of Originality and Ethics

Generative Artificial Intelligence in Product Design Education: Navigating Concerns of Originality and Ethics Bartlett, Kristin A.; Camba, Jorge D. Image-generative artificial intelligence (AI) is increasingly being used in the product design process. In this paper, we present examples of how it is being used and discuss the possibilities of how applications may evolve in the future. We discuss the legal and ethical implications of image-generative AI, including concerns about bias, hidden labor, theft from artists, lack of originality in the outputs, and lack of copyright protection. We discuss how these concerns apply to design education and provide recommendations to educators about how AI should be addressed in the design classroom. We recommend that educators introduce AI as one tool among many in the designer’s toolkit and encourage it to be used as a process tool rather than for generating final design deliverables. We also provide guidance for how educators might engage students in discussions about AI to enhance their learning. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T12:42:39Z No. of bitstreams: 1 Generative Artificial Intelligence in Product Design Education.pdf: 2541510 bytes, checksum: 5ad68dd261b50c7854f81bbb23489f26 (MD5); Made available in DSpace on 2024-03-12T12:42:39Z (GMT). No. of bitstreams: 1 Generative Artificial Intelligence in Product Design Education.pdf: 2541510 bytes, checksum: 5ad68dd261b50c7854f81bbb23489f26 (MD5)

Evaluating ChatGPT-Generated Linear Algebra Formative Assessments

Evaluating ChatGPT-Generated Linear Algebra Formative Assessments Rigaud Téllez, Nelly; Rayón Villela, Patricia; Blanco Bautista, Roberto This research explored Large Language Models potential uses on formative assessment for mathematical problem-solving process. The study provides a conceptual analysis of feedback and how the use of these models is related in the context of formative assessment for Linear Algebra problems. Particularly, the performance of a popular model known as ChatGPT in mathematical problems fails on reasoning, proofs, model construction, among others. Formative assessment is a process used by teachers and students during instruction that provides feedback to adjust ongoing teaching and learning to improve student’s achievement of intended instructional outcomes. The study analyzed and evaluated feedback provided to engineering students in their solutions, from both, instructors and ChatGPT, against fine-grained criteria of a formative feedback model that includes affective aspects. Considering preliminary outputs, and to improve performance of feedback from both agents’ instructors and ChatGPT, we developed a framework for formative assessment in mathematical problemsolving using a Large Language Model (LLM). We designed a framework to generate prompts, supported by common Linear Algebra mistakes within the context of concept development and problem-solving strategies. In this framework, the instructor acts as an agent to verify tasks in a math problem assigned to students, establishing a virtuous cycle of learning of queries supported by ChatGPT. Results revealed potentialities and challenges on how to improve feedback on graduate-level math problems, by which both educators and students adapt teaching and learning strategies. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T12:18:25Z No. of bitstreams: 1 Evaluating ChatGPT-Generated Linear Algebra Formative Assessments.pdf: 571024 bytes, checksum: c90c41bdd2f423882f07827c9f36cc78 (MD5); Made available in DSpace on 2024-03-12T12:18:25Z (GMT). No. of bitstreams: 1 Evaluating ChatGPT-Generated Linear Algebra Formative Assessments.pdf: 571024 bytes, checksum: c90c41bdd2f423882f07827c9f36cc78 (MD5)

A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method

A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method Maslim, Martinus; Wang, Hei-Chia; Putra, Cendra Devayana; Prabowo, Yulius Denny To measure the quality of student learning, teachers must conduct evaluations. One of the most efficient modes of evaluation is the short answer question. However, there can be inconsistencies in teacher-performed manual evaluations due to an excessive number of students, time demands, fatigue, etc. Consequently, teachers require a trustworthy system capable of autonomously and accurately evaluating student answers. Using hybrid transfer learning and student answer dataset, we aim to create a reliable automated short answer scoring system called Hybrid Transfer Learning for Automated Short Answer Scoring (HTL-ASAS). HTL-ASAS combines multiple tokenizers from a pretrained model with the bidirectional encoder representations from transformers. Based on our evaluation of the training model, we determined that HTL-ASAS has a higher evaluation accuracy than models used in previous studies. The accuracy of HTL-ASAS for datasets containing responses to questions pertaining to introductory information technology courses reaches 99.6%. With an accuracy close to one hundred percent, the developed model can undoubtedly serve as the foundation for a trustworthy ASAS system. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-12T09:23:17Z No. of bitstreams: 1 A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method.pdf: 1131436 bytes, checksum: ad08b7933bf4c2c45c3cb3530a9f79c6 (MD5); Made available in DSpace on 2024-03-12T09:23:17Z (GMT). No. of bitstreams: 1 A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method.pdf: 1131436 bytes, checksum: ad08b7933bf4c2c45c3cb3530a9f79c6 (MD5)

Requirements for User Experience Management - A Tertiary Study

Requirements for User Experience Management - A Tertiary Study Hinderks, Andreas; Domínguez Mayo, Francisco José; Escalona, María José; Thomaschewski, Jörg Today’s users expect to be able to interact with the products they own without much effort and also want to be excited about them. The development of a positive user experience must therefore be managed. We understand management in general as a combination of a goal, a strategy, and resources. When applied to UX, user experience management consists of a UX goal, a UX strategy, and UX resources. We conducted a tertiary study and examined the current state of existing literature regarding possible requirements. We want to figure out, what requirements can be derived from the literature reviews with the focus on UX and agile development. In total, we were able to identify and analyse 16 studies. After analysing the studies in detail, we identified different requirements for UX management. In summary, we identified 13 requirements. The most frequently mentioned requirements were prototypes and UX/usability evaluation. Communication between UX professionals and developers was identified as a major improvement in the software development process. In summary, we were able to identify requirements for UX management of People/Social, Technology/Artifacts, and Process/Practice. However, we could not identify requirements for UX management that enabled the development and achievement of a UX goal. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2024-02-06T09:28:40Z No. of bitstreams: 1 ip2024_01_004.pdf: 707717 bytes, checksum: 636dc3688d95f8f658579c6594af7a52 (MD5); Made available in DSpace on 2024-02-06T09:28:40Z (GMT). No. of bitstreams: 1 ip2024_01_004.pdf: 707717 bytes, checksum: 636dc3688d95f8f658579c6594af7a52 (MD5)

An Adaptive Salp-Stochastic-Gradient-Descent- Based Convolutional LSTM With MapReduce Framework for the Prediction of Rainfall

An Adaptive Salp-Stochastic-Gradient-Descent- Based Convolutional LSTM With MapReduce Framework for the Prediction of Rainfall Manoj, S. O.; Kumar, Abhishek; Dubey, A. K.; Ananth, J. P. Rainfall prediction is considered to be an esteemed research area that impacts the day-to-day life of Indians. The predominant income source of most of the Indian population is agriculture. It helps the farmers to make the appropriate decisions pertaining to cultivation and irrigation. The primary objective of this investigation is to develop a technique for rainfall prediction utilising the MapReduce framework and the convolutional long short-term memory (ConvLSTM) method to circumvent the limitations of higher computational requirements and the inability to process a large number of data points. In this work, an adaptive salp-stochastic-gradientdescent-based ConvLSTM (adaptive S-SGD-based ConvLSTM) system has been developed to predict rainfall accurately to process the long time series data and to eliminate the vanishing problems. To optimize the hyperparameter of the convLSTM model, the S-SGD methodology proposed combine the SGD and the salp swarm algorithm (SSA). The adaptive S-SGD based ConvLSTM has been developed by integrating the adaptive concept in S-SGD. It tunes the weights of ConvLSTM optimally to achieve better prediction accuracy. Assessment measures, such as the percentage root mean square difference (PRD) and mean square error (MSE), were employed to compare the suggested method with previous approaches. The developed system demonstrates high prediction accuracy, achieving minimal values for MSE (0.0042) and PRD (0.8450). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2024-02-06T09:12:23Z No. of bitstreams: 1 ip2024_01_003_0.pdf: 2711710 bytes, checksum: a8f9f550ee68dfb95eeeff37d0dd99cd (MD5); Made available in DSpace on 2024-02-06T09:12:23Z (GMT). No. of bitstreams: 1 ip2024_01_003_0.pdf: 2711710 bytes, checksum: a8f9f550ee68dfb95eeeff37d0dd99cd (MD5)

TKU-PSO: An Efficient Particle Swarm Optimization Model for Top-K High-Utility Itemset Mining

TKU-PSO: An Efficient Particle Swarm Optimization Model for Top-K High-Utility Itemset Mining Carstensen, Simen; Chun-Wei Lin, Jerry Top-k high-utility itemset mining (top- HUIM) is a data mining procedure used to identify the most valuable patterns within transactional data. Although many algorithms are proposed for this purpose, they require substantial execution times when the search space is vast. For this reason, several meta-heuristic models have been applied in similar utility mining problems, particularly evolutionary computation (EC). These algorithms are beneficial as they can find optimal solutions without exploring the search space exhaustively. However, there are currently no evolutionary heuristics available for top-k HUIM. This paper addresses this issue by proposing an EC-based particle swarm optimization model for top-k HUIM, which we call TKU-PSO. In addition, we have developed several strategies to relieve the computational complexity throughout the algorithm. First, redundant and unnecessary candidate evaluations are avoided by utilizing explored solutions and estimating itemset utilities. Second, unpromising items are pruned during execution based on a thresholdraising concept we call minimum solution fitness. Finally, the traditional population initialization approach is revised to improve the model’s ability to find optimal solutions in huge search spaces. Our results show that TKU-PSO is faster than state-of-the-art competitors in all datasets tested. Most notably, existing algorithms could not complete certain experiments due to excessive runtimes, whereas our model discovered the correct solutions within seconds. Moreover, TKU-PSO achieved an overall accuracy of 99.8% compared to 16.5% with the current heuristic approach, while memory usage was the smallest in 2/3 of all tests. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2024-02-06T08:40:05Z No. of bitstreams: 1 ip2024_01_002_1.pdf: 1652224 bytes, checksum: bc066ea9a8c4b0578d7ab427b76390ee (MD5); Made available in DSpace on 2024-02-06T08:40:05Z (GMT). No. of bitstreams: 1 ip2024_01_002_1.pdf: 1652224 bytes, checksum: bc066ea9a8c4b0578d7ab427b76390ee (MD5)

How Does the Visualization Technique Affect the Design Process? Using Sketches, Real Products and Virtual Models to Test the User’s Emotional Response

How Does the Visualization Technique Affect the Design Process? Using Sketches, Real Products and Virtual Models to Test the User’s Emotional Response Alonso-García, María; Palacios-Ibáñez, Almudena; de-Cózar-Macías, Óscar D.; Marín-Granados, Manuel D. Testing products during the design process can help design teams anticipate user needs and predict a positive emotional response. Emerging technologies, e.g., Virtual Reality (VR), allow designers to test products in a more sophisticated manner alongside traditional approaches like sketches, photographs or physical prototypes. In this paper, we present the results of a study conducted to evaluate the feasibility of seven visualization techniques for product assessment within the framework of emotional design, suggesting that the user’s perception depends on the visualization technique used to present the product. This research provides recommendations for product evaluation using physical, virtual, or conceptual prototypes to analyze the user’s emotional response throughout 19 parameters. Our results indicate that the use of virtual environments, including VR and VR with Passive Haptics (VRPH), can facilitate user participation in the design process, although these visualization techniques may also exaggerate the emotions perceived by users. In this context, VRPH tends to overstate the tactile perception of the product. Additionally, our results reveal that both virtual and conceptual environments can amplify a user’s likelihood to purchase a product. However, the latter setting could also potentially lead to confusion among users in regards to their perception of the product’s weight, dimensions, and cost. Based on these findings, the authors encourage industrial designers to develop new methodologies to optimize design process and minimize costs. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2024-02-06T08:28:08Z No. of bitstreams: 1 ip2024_01_001.pdf: 4676066 bytes, checksum: 9e451573d743d11b000f8576f98d05cc (MD5); Made available in DSpace on 2024-02-06T08:28:08Z (GMT). No. of bitstreams: 1 ip2024_01_001.pdf: 4676066 bytes, checksum: 9e451573d743d11b000f8576f98d05cc (MD5)

GRASE: Granulometry Analysis with Semi Eager Classifier to Detect Malware

GRASE: Granulometry Analysis with Semi Eager Classifier to Detect Malware Deore, Mahendra; Tarambale, Manoj; Raja Kumar, Jambi Ratna; Sakhare, Sachin Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT). The community benefits from this large-scale network which allows monitoring and controlling of physical devices. But many times, it costs the security as MALicious softWARE (MalWare) developers try to invade the network, as for them, these devices are like a ‘backdoor’ providing them easy ‘entry’. To stop invaders from entering the network, identifying malware and its variants is of great significance for cyberspace. Traditional methods of malware detection like static and dynamic ones, detect the malware but lack against new techniques used by malware developers like obfuscation, polymorphism and encryption. A machine learning approach to detect malware, where the classifier is trained with handcrafted features, is not potent against these techniques and asks for efforts to put in for the feature engineering. The paper proposes a malware classification using a visualization methodology wherein the disassembled malware code is transformed into grey images. It presents the efficacy of Granulometry texture analysis technique for improving malware classification. Furthermore, a Semi Eager (SemiE) classifier, which is a combination of eager learning and lazy learning technique, is used to get robust classification of malware families. The outcome of the experiment is promising since the proposed technique requires less training time to learn the semantics of higher-level malicious behaviours. Identifying the malware (testing phase) is also done faster. A benchmark database like malimg and Microsoft Malware Classification challenge (BIG-2015) has been utilized to analyse the performance of the system. An overall average classification accuracy of 99.03 and 99.11% is achieved, respectively. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2024-01-02T08:33:42Z No. of bitstreams: 1 ip2023_12_002_0.pdf: 3980674 bytes, checksum: 452dad3a10f3c7ee92f7982332310e05 (MD5); Made available in DSpace on 2024-01-02T08:33:42Z (GMT). No. of bitstreams: 1 ip2023_12_002_0.pdf: 3980674 bytes, checksum: 452dad3a10f3c7ee92f7982332310e05 (MD5)

Traffic Optimization Through Waiting Prediction and Evolutive Algorithms

Traffic Optimization Through Waiting Prediction and Evolutive Algorithms García, Francisco; Hernández, Helena; Moreno-García, María N.; de Paz Santana, Juan F.; López, Vivian F.; Bajo, Javier Traffic optimization systems require optimization procedures to optimize traffic light timing settings in order to improve pedestrian and vehicle mobility. Traffic simulators allow obtaining accurate estimates of traffic behavior by applying different timing configurations, but require considerable computational time to perform validation tests. For this reason, this project proposes the development of traffic optimizations based on the estimation of vehicle waiting times through the use of different prediction techniques and the use of this estimation to subsequently apply evolutionary algorithms that allow the optimizations to be carried out. The combination of these two techniques leads to a considerable reduction in calculation time, which makes it possible to apply this system at runtime. The tests have been carried out on a real traffic junction on which different traffic volumes have been applied to analyze the performance of the system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2024-01-02T08:21:54Z No. of bitstreams: 1 ip2023_12_001_0.pdf: 1895463 bytes, checksum: 20ee704a77e5452f0f776492b7dab0c5 (MD5); Made available in DSpace on 2024-01-02T08:21:54Z (GMT). No. of bitstreams: 1 ip2023_12_001_0.pdf: 1895463 bytes, checksum: 20ee704a77e5452f0f776492b7dab0c5 (MD5)

Editor's Note

Editor's Note Verdú, Elena The International Journal of Interactive Multimedia and Artificial Intelligence – IJIMAI –provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances in Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. The present regular issue comprises different topics as generative AI, brain and main inspired computing, bird species identification, spam detection, recommendation systems, synthetic aperture radar automatic target recognition, hand gestures recognition, anomalies detection for video surveillance systems, disease detection, social networks analysis, or user experience. The collection of articles shows the wide use of deep learning techniques, although classical machine learning techniques, among others, are also present. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-12-12T14:09:23Z No. of bitstreams: 1 ijimai8_4_0.pdf: 78433 bytes, checksum: 511972015e7b5e293f8991193fac549e (MD5); Made available in DSpace on 2023-12-12T14:09:23Z (GMT). No. of bitstreams: 1 ijimai8_4_0.pdf: 78433 bytes, checksum: 511972015e7b5e293f8991193fac549e (MD5)

Tests of Usability Guidelines About Response to User Actions. Importance, Compliance, and Application of the Guidelines

Tests of Usability Guidelines About Response to User Actions. Importance, Compliance, and Application of the Guidelines Alonso-Virgós, Lucía; Pascual-Espada, Jordán; Rossi, Gustavo Usability is a quality that a web page can have due to its simple use. Many recommendations aim to improve the web user experience, but there is no standardization of them. This study is part of a saga, which aims to order existing recommendations and guidelines by analyzing the behavior of 20 Information Technology (IT) developers. This publication analyzes the set of guidelines that determine "user responses" when they interact with a website. It is intended to group these guidelines and obtain data on the application of each of them. The test is carried out with 20 web developers without training or experience in web usability. The objective is to know if there are "user response" guidelines that a developer with no training or usability experience applies innate. Since web developers are also users, it is believed that there may be innate behavior that is not necessarily learned. The purposes of the work are: 1) Enumerate the most forgotten recommendations by web developers. This can help to think about the importance of offering specific training in this field. 2) Know the most important recommendations and guidelines, according to the web developers themselves. The investigation is carried out as follows: First, IT engineers were asked to develop a website; Second, user tests were performed and the most neglected and most applied guidelines were evaluated. The level of compliance was also analyzed, as developers lack experience in web usability and could be applying a guideline, but not correctly; Third, web developers are interviewed to find out what guidelines they consider necessary. The results are intended to help us understand if a web developer without training or experience in web usability can innately apply guidelines on "user responses". The objective of the study is to determine that there are guidelines that are applied intuitively and others that are not, and to know the reason for each situation. The results determine that the guidelines considered essential and those that are most applied innately have something in common. The results reveal that the essential guidelines and those that are most commonly implemented inherently share certain commonalities. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-12-12T14:04:13Z No. of bitstreams: 1 ijimai8_4_19_0.pdf: 1544973 bytes, checksum: 7376bf3b12bf68a2c95d912cbcc589ec (MD5); Made available in DSpace on 2023-12-12T14:04:13Z (GMT). No. of bitstreams: 1 ijimai8_4_19_0.pdf: 1544973 bytes, checksum: 7376bf3b12bf68a2c95d912cbcc589ec (MD5)

A Review of Bias and Fairness in Artificial Intelligence

A Review of Bias and Fairness in Artificial Intelligence González-Sendino, Rubén; Serrano, Emilio; Bajo, Javier; Novais, Paulo Automating decision systems has led to hidden biases in the use of artificial intelligence (AI). Consequently, explaining these decisions and identifying responsibilities has become a challenge. As a result, a new field of research on algorithmic fairness has emerged. In this area, detecting biases and mitigating them is essential to ensure fair and discrimination-free decisions. This paper contributes with: (1) a categorization of biases and how these are associated with different phases of an AI model’s development (including the data-generation phase); (2) a revision of fairness metrics to audit the data and AI models trained with them (considering agnostic models when focusing on fairness); and, (3) a novel taxonomy of the procedures to mitigate biases in the different phases of an AI model’s development (pre-processing, training, and post-processing) with the addition of transversal actions that help to produce fairer models. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-12-11T13:20:45Z No. of bitstreams: 1 ip2023_11_001.pdf: 1082876 bytes, checksum: 4f7737e73ded1b47a4fbf7fdb60cd437 (MD5); Made available in DSpace on 2023-12-11T13:20:45Z (GMT). No. of bitstreams: 1 ip2023_11_001.pdf: 1082876 bytes, checksum: 4f7737e73ded1b47a4fbf7fdb60cd437 (MD5)

Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation

Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation Martínez-Comesaña, Miguel; Martínez-Torres, Javier; Eguía-Oller, Pablo; López-Gómez, Javier Optimising the use of the photovoltaic (PV) energy is essential to reduce fossil fuel emissions by increasing the use of solar power generation. In recent years, research has focused on physical simulations or artifical intelligence models attempting to increase the accuracy of PV generation predictions. The use of simulated data as pre-training for deep learning models has increased in different fields. The reasons are the higher efficiency in the subsequent training with real data and the possibility of not having real data available. This work presents a methodology, based on an deep learning model optimised with specific techniques and pre-trained with synthetic data, to estimate the generation of a PV system. A case study of a photovoltaic installation with 296 PV panels located in northwest Spain is presented. The results show that the model with proper pre-training trains six to seven times faster than a model without pre-training and three to four times faster than a model pre-trained with non-accurate simulated data. In terms of accuracy and considering a homogeneous training process, all models obtained average relative errors around 12%, except the model with incorrect pre-training which performs worse. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-12-11T12:39:29Z No. of bitstreams: 1 ip2023_11_002.pdf: 3321570 bytes, checksum: 9b631febbc75d5a2914ef233fb5f9013 (MD5); Made available in DSpace on 2023-12-11T12:39:29Z (GMT). No. of bitstreams: 1 ip2023_11_002.pdf: 3321570 bytes, checksum: 9b631febbc75d5a2914ef233fb5f9013 (MD5)

Measuring the Difference Between Pictures From Controlled and Uncontrolled Sources to Promote a Destination. A Deep Learning Approach

Measuring the Difference Between Pictures From Controlled and Uncontrolled Sources to Promote a Destination. A Deep Learning Approach Diaz-Pacheco, Angel; Álvarez-Carmona, Miguel A.; Rodríguez-González, Ansel Y.; Carlos, Hugo; Aranda, Ramón Promoting a destination is a major task for Destination Marketing Organizations (DMOs). Although DMOs control, to some extent, the information presented to travelers (controlled sources), there are other different sources of information (uncontrolled sources) that could project an unfavorable image of the destination. Measuring differences between information sources would help design strategies to mitigate negative factors. In this way, we propose a deep learning-based approach to automatically measure the changes between images from controlled and uncontrolled information sources. Our approach exempts experts from the time-consuming task of assessing enormous quantities of pictures to track changes. To our best knowledge, this work is the first work that focuses on this issue using technological paradigms. Notwithstanding this, our approach paves novel pathways to acquire strategic insights that can be harnessed for the augmentation of destination development, the refinement of recommendation systems, the analysis of online travel reviews, and myriad other pertinent domains. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-12-11T12:26:20Z No. of bitstreams: 1 ip2023_10_003_0.pdf: 4406116 bytes, checksum: 08e69141ea39a9c47231ca9ac0609575 (MD5); Made available in DSpace on 2023-12-11T12:26:20Z (GMT). No. of bitstreams: 1 ip2023_10_003_0.pdf: 4406116 bytes, checksum: 08e69141ea39a9c47231ca9ac0609575 (MD5)

Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets

Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets Bobadilla, Jesús; Gutiérrez, Abraham The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datasets. We have tested the GANRS method by creating multiple synthetic datasets from three different real datasets taken as a source. Experiments include variations in the number of users in the synthetic datasets, as well as a different number of samples. We have also selected six state-of-the-art collaborative filtering deep learning models to test both their comparative performance and the GANRS method. The results show a consistent behavior of the generated datasets compared to the source ones; particularly, in the obtained values and trends of the precision and recall quality measures. The tested deep learning models have also performed as expected on all synthetic datasets, making it possible to compare the results with those obtained from the real source data. Future work is proposed, including different cold start scenarios, unbalanced data, and demographic fairness. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-11-02T17:30:16Z No. of bitstreams: 1 ip2023_10_002.pdf: 5390765 bytes, checksum: 4edb2ac46e74d982a71f964a97162377 (MD5); Made available in DSpace on 2023-11-02T17:30:16Z (GMT). No. of bitstreams: 1 ip2023_10_002.pdf: 5390765 bytes, checksum: 4edb2ac46e74d982a71f964a97162377 (MD5)

S-Divergence-Based Internal Clustering Validation Index

S-Divergence-Based Internal Clustering Validation Index Kumar Sharma, Krishna; Seal, Ayan; Yazidi, Anis; Krejcar, Ondrej A clustering validation index (CVI) is employed to evaluate an algorithm’s clustering results. Generally, CVI statistics can be split into three classes, namely internal, external, and relative cluster validations. Most of the existing internal CVIs were designed based on compactness (CM) and separation (SM). The distance between cluster centers is calculated by SM, whereas the CM measures the variance of the cluster. However, the SM between groups is not always captured accurately in highly overlapping classes. In this article, we devise a novel internal CVI that can be regarded as a complementary measure to the landscape of available internal CVIs. Initially, a database’s clusters are modeled as a non-parametric density function estimated using kernel density estimation. Then the S-divergence (SD) and S-distance are introduced for measuring the SM and the CM, respectively. The SD is defined based on the concept of Hermitian positive definite matrices applied to density functions. The proposed internal CVI (PM) is the ratio of CM to SM. The PM outperforms the legacy measures presented in the literature on both superficial and realistic databases in various scenarios, according to empirical results from four popular clustering algorithms, including fuzzy k-means, spectral clustering, density peak clustering, and density-based spatial clustering applied to noisy data. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-11-02T17:22:50Z No. of bitstreams: 1 ip2023_10_001.pdf: 4247803 bytes, checksum: 6cc0f78e54ba6ad1f97721e81afc2aa4 (MD5); Made available in DSpace on 2023-11-02T17:22:50Z (GMT). No. of bitstreams: 1 ip2023_10_001.pdf: 4247803 bytes, checksum: 6cc0f78e54ba6ad1f97721e81afc2aa4 (MD5)

Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality

Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality Izquierdo-Domenech, Juan; Linares-Pellicer, Jordi; Ferri-Molla, Isabel Augmented reality (AR) has become a powerful tool for assisting operators in complex environments, such as shop floors, laboratories, and industrial settings. By displaying synthetic visual elements anchored in real environments and providing information for specific tasks, AR helps to improve efficiency and accuracy. However, a common bottleneck in these environments is introducing all necessary information, which often requires predefined structured formats and needs more ability for multimodal and Natural Language (NL) interaction. This work proposes a new method for dynamically documenting complex environments using AR in a multimodal, non-structured, and interactive manner. Our method employs Large Language Models (LLMs) to allow experts to describe elements from the real environment in NL and select corresponding AR elements in a dynamic and iterative process. This enables a more natural and flexible way of introducing information, allowing experts to describe the environment in their own words rather than being constrained by a predetermined structure. Any operator can then ask about any aspect of the environment in NL to receive a response and visual guidance from the AR system, thus allowing for a more natural and flexible way of introducing and retrieving information. These capabilities ultimately improve the effectiveness and efficiency of tasks in complex environments. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-10-02T15:57:32Z No. of bitstreams: 1 ip2023_09_002.pdf: 1947156 bytes, checksum: 5cbf3c0aa9d4ca318f0f478bfedc6825 (MD5); Made available in DSpace on 2023-10-02T15:57:32Z (GMT). No. of bitstreams: 1 ip2023_09_002.pdf: 1947156 bytes, checksum: 5cbf3c0aa9d4ca318f0f478bfedc6825 (MD5)

The Game Theory in Quantum Computers: A Review

The Game Theory in Quantum Computers: A Review Pérez-Antón, Raquel; López-Sánchez, José Ignacio; Corbi, Alberto Game theory has been studied extensively in recent centuries as a set of formal mathematical strategies for optimal decision making. This discipline improved its efficiency with the arrival, in the 20th century, of digital computer science. However, the computational limitations related to exponential time type problems in digital processors, triggered the search for more efficient alternatives. One of these choices is quantum computing. Certainly, quantum processors seem to be able to solve some of these complex problems, at least in theory. For this reason, in recent times, many research works have emerged related to the field of quantum game theory. In this paper we review the main studies about the subject, including operational requirements and implementation details. In addition, we describe various quantum games, their design strategy, and the used supporting tools. We also present the still open debate linked to the interpretation of the transformations of classical algorithms in fundamental game theory to their quantum version, with special attention to the Nash equilibrium. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-10-02T15:44:05Z No. of bitstreams: 1 ip2023_09_001.pdf: 1472228 bytes, checksum: 40c31750ef4e8ddfa372dabcd1fe17a8 (MD5); Made available in DSpace on 2023-10-02T15:44:05Z (GMT). No. of bitstreams: 1 ip2023_09_001.pdf: 1472228 bytes, checksum: 40c31750ef4e8ddfa372dabcd1fe17a8 (MD5)

Editor’s Note

Editor’s Note Alonso, Ricardo S.; Chamoso, Pablo; Rodríguez-González, Sara; Novais, Paulo Research in Agents and Multiagent Systems has matured significantly in recent years, representing one of the main branches of Artificial Intelligence and currently there are numerous effective applications of these technologies combined with Deep Learning, Computer Vision or Natural Language Processing, including areas such as healthcare and Ambient Intelligence, smart cities and mobility, Industry 4.0, educational technology, and fintech, among many others. In this regard, the International Conference on Practical Applications of Agents and Multi-Agent System (PAAMS) provides an international forum to present and discuss the latest scientific advances and their effective applications in different sectors, evaluate the impact of the approach and facilitate technology transfer among different stakeholders. Currently, a series of co-located events specialized in different areas of research are held simultaneously with PAAMS, these being the International Congress on Blockchain and Applications (BLOCKCHAIN), the International Conference on Distributed Computing and Artificial Intelligence (DCAI), the International Conference on Decision Economics (DECON), the International Symposium on Ambient Intelligence (ISAmI), the International Conference on Methodologies and Intelligent Systems for Technology Enhanced Learning (MIS4TEL), and the International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB). In this regard, the present Special Issue includes a selection of extended papers presented at the 20th International Conference PAAMS 22 and its co-located events and held in L’Aquila (Italy), July 13-15, 2022. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-09-06T08:32:18Z No. of bitstreams: 1 ijimai8_3_0_0.pdf: 57556 bytes, checksum: f0b02c6bc1df1b55b4db509d8c856e50 (MD5); Made available in DSpace on 2023-09-06T08:32:18Z (GMT). No. of bitstreams: 1 ijimai8_3_0_0.pdf: 57556 bytes, checksum: f0b02c6bc1df1b55b4db509d8c856e50 (MD5)

Violence Detection in Audio: Evaluating the Effectiveness of Deep Learning Models and Data Augmentation

Violence Detection in Audio: Evaluating the Effectiveness of Deep Learning Models and Data Augmentation Durães, Dalila; Veloso, Bruno; Novais, Paulo Human nature is inherently intertwined with violence, impacting the lives of numerous individuals. Various forms of violence pervade our society, with physical violence being the most prevalent in our daily lives. The study of human actions has gained significant attention in recent years, with audio (captured by microphones) and video (captured by cameras) being the primary means to record instances of violence. While video requires substantial processing capacity and hardware-software performance, audio presents itself as a viable alternative, offering several advantages beyond these technical considerations. Therefore, it is crucial to represent audio data in a manner conducive to accurate classification. In the context of violence in a car, specific datasets dedicated to this domain are not readily available. As a result, we had to create a custom dataset tailored to this particular scenario. The purpose of curating this dataset was to assess whether it could enhance the detection of violence in car-related situations. Due to the imbalanced nature of the dataset, data augmentation techniques were implemented. Existing literature reveals that Deep Learning (DL) algorithms can effectively classify audio, with a commonly used approach involving the conversion of audio into a mel spectrogram image. Based on the results obtained for that dataset, the EfficientNetB1 neural network demonstrated the highest accuracy (95.06%) in detecting violence in audios, closely followed by EfficientNetB0 (94.19%). Conversely, MobileNetV2 proved to be less capable in classifying instances of violence. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-09-06T08:26:38Z No. of bitstreams: 1 ijimai8_3_7.pdf: 714559 bytes, checksum: a445d93fee207713f024d19b6bf6ab13 (MD5); Made available in DSpace on 2023-09-06T08:26:38Z (GMT). No. of bitstreams: 1 ijimai8_3_7.pdf: 714559 bytes, checksum: a445d93fee207713f024d19b6bf6ab13 (MD5)

An Investigation Into Different Text Representations to Train an Artificial Immune Network for Clustering Texts

An Investigation Into Different Text Representations to Train an Artificial Immune Network for Clustering Texts Ferraria, Matheus A.; Ferraria, Vinicius A.; de Castro, Leandro N. Extracting knowledge from text data is a complex task that is usually performed by first structuring the texts and then applying machine learning algorithms, or by using specific deep architectures capable of dealing directly with the raw text data. The traditional approach to structure texts is called Bag of Words (BoW) and consists of transforming each word in a document into a dimension (variable) in the structured data. Another approach uses grammatical classes to categorize the words and, thus, limit the dimension of the structured data to the number of grammatical categories. Another form of structuring text data for analysis is by using a distributed representation of words, sentences, or documents with methods like Word2Vec, Doc2Vec, and SBERT. This paper investigates four classes of text structuring methods to prepare documents for being clustered by an artificial immune system called aiNet. The goal is to assess the influence of each structuring method in the quality of the clustering obtained by the system and how methods that belong to the same type of representation differ from each other, for example both LIWC and MRC are considered grammarbased models but each one of them uses completely different dictionaries to generate its representation. By using internal clustering measures, our results showed that vector space models, on average, presented the best results for the datasets chosen, followed closely by the state of the art SBERT model, and MRC had the overall worst performance. We could also observe a consistency in the number of clusters generated by each representation and for each dataset, having SBERT as the model that presented a number of clusters closer to the original number of classes in the data. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-09-06T08:20:25Z No. of bitstreams: 1 ijimai8_3_5.pdf: 353722 bytes, checksum: 84e1c9e34aff07d64e231c000192cdfd (MD5); Made available in DSpace on 2023-09-06T08:20:25Z (GMT). No. of bitstreams: 1 ijimai8_3_5.pdf: 353722 bytes, checksum: 84e1c9e34aff07d64e231c000192cdfd (MD5)

Pollutant Time Series Analysis for Improving Air-Quality in Smart Cities

Pollutant Time Series Analysis for Improving Air-Quality in Smart Cities López-Blanco, Raúl; Chaveinte García, Miguel; Alonso, Ricardo S.; Prieto, Javier; Corchado, Juan M. The evolution towards Smart Cities is the process that many urban centers are following in their quest for efficiency, resource optimization and sustainable growth. This step forward in the continuous improvement of cities is closely linked to the quality of life they want to offer their citizens. One of the key issues that can have the greatest impact on the quality of life of all city dwellers is the quality of the air they breathe, which can lead to illnesses caused by pollutants in the air. The application of new technologies, such as the Internet of Things, Big Data and Artificial Intelligence, makes it possible to obtain increasingly abundant and accurate data on what is happening in cities, providing more information to take informed action based on scientific data. This article studies the evolution of pollutants in the main cities of Castilla y León, using Generative Additive Models (GAM), which have proven to be the most efficient for making predictions with detailed historical data and which have very strong seasonalities. The results of this study conclude that during the COVID-19 pandemic containment period, there was an overall reduction in the concentration of pollutants. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-09-06T07:56:12Z No. of bitstreams: 1 ijimai8_3_9.pdf: 2271859 bytes, checksum: d134b99201a4bd287f347a2fe723b4ea (MD5); Made available in DSpace on 2023-09-06T07:56:12Z (GMT). No. of bitstreams: 1 ijimai8_3_9.pdf: 2271859 bytes, checksum: d134b99201a4bd287f347a2fe723b4ea (MD5)

Consensus-Based Learning for MAS: Definition, Implementation and Integration in IVEs

Consensus-Based Learning for MAS: Definition, Implementation and Integration in IVEs Carrascosa, C.; Enguix, F.; Rebollo, M.; Rincon, J. One of the main advancements in distributed learning may be the idea behind Google’s Federated Learning (FL) algorithm. It trains copies of artificial neural networks (ANN) in a distributed way and recombines the weights and biases obtained in a central server. Each unit maintains the privacy of the information since the training datasets are not shared. This idea perfectly fits a Multi-Agent System, where the units learning and sharing the model are agents. FL is a centralized approach, where a server is in charge of receiving, averaging and distributing back the models to the different units making the learning process. In this work, we propose a truly distributed learning process where all the agents have the same role in the system. We suggest using a consensus-based learning algorithm that we call Co-Learning. This process uses a consensus process to share the ANN models each agent learns using its private data and calculates the aggregated model. Co-Learning, as a consensus-based algorithm, calculates the average of the ANN models shared by the agents with their local neighbors. This iterative process converges to the averaged ANN model as a central server does. Apart from the definition of the Co-Learning algorithm, the paper presents its integration in SPADE agents, along with a framework called FIVE allowing to develop Intelligent Virtual Environments for SPADE agents. This framework has been used to test the execution of SPADE agents using Co-Learning algorithm in a simulation of an orange orchard field. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-09-06T07:34:30Z No. of bitstreams: 1 ijimai8_3_2.pdf: 958756 bytes, checksum: 8d28398827dd5615483a56b7b414301b (MD5); Made available in DSpace on 2023-09-06T07:34:30Z (GMT). No. of bitstreams: 1 ijimai8_3_2.pdf: 958756 bytes, checksum: 8d28398827dd5615483a56b7b414301b (MD5)

Development of an Intelligent Classifier Model for Denial of Service Attack Detection

Development of an Intelligent Classifier Model for Denial of Service Attack Detection Michelena, Álvaro; Aveleira-Mata, Jose; Jove, Esteban; Alaiz-Moretón, Héctor; Quintián, Héctor; Calvo-Rolle, José Luis The prevalence of Internet of Things (IoT) systems deployment is increasing across various domains, from residential to industrial settings. These systems are typically characterized by their modest computationa requirements and use of lightweight communication protocols, such as MQTT. However, the rising adoption of IoT technology has also led to the emergence of novel attacks, increasing the susceptibility of these systems to compromise. Among the different attacks that can affect the main IoT protocols are Denial of Service attacks (DoS). In this scenario, this paper evaluates the performance of six supervised classification techniques (Decision Trees, Multi-layer Perceptron, Random Forest, Support Vector Machine, Fisher Linear Discriminant and Bernoulli and Gaussian Naive Bayes) combined with the Principal Component Analysis (PCA) feature extraction method for detecting DoS attacks in MQTT networks. For this purpose, a real dataset containing all the traffic generated in the network and many attacks executed has been used. The results obtained with several models have achieved performances above 99% AUC. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-09-06T07:20:37Z No. of bitstreams: 1 ijimai8_3_3.pdf: 460755 bytes, checksum: 7a24d6bbdd35f105e746145de760bdc5 (MD5); Made available in DSpace on 2023-09-06T07:20:37Z (GMT). No. of bitstreams: 1 ijimai8_3_3.pdf: 460755 bytes, checksum: 7a24d6bbdd35f105e746145de760bdc5 (MD5)

Automatic Cell Counting With YOLOv5: A Fluorescence Microscopy Approach

Automatic Cell Counting With YOLOv5: A Fluorescence Microscopy Approach López Flórez, Sebastián; González-Briones, Alfonso; Hernández, Guillermo; Ramos, Carlos; de la Prieta, Fernando Counting cells in a Neubauer chamber on microbiological culture plates is a laborious task that depends on technical experience. As a result, efforts have been made to advance computer vision-based approaches, increasing efficiency and reliability through quantitative analysis of microorganisms and calculation of their characteristics, biomass concentration, and biological activity. However, variability that still persists in these processes poses a challenge that is yet to be overcome. In this work, we propose a solution adopting a YOLOv5 network model for automatic cell recognition and counting in a case study for laboratory cell detection using images from a CytoSMART Exact FL microscope. In this context, a dataset of 21 expert-labeled cell images was created, along with an extra Sperm DetectionV dataset of 1024 images for transfer learning. The dataset was trained using the pretrained YOLOv5 algorithm with the Sperm DetectionV database. A laboratory test was also performed to confirm result’s viability. Compared to YOLOv4, the current YOLOv5 model had accuracy, precision, recall, and F1 scores of 92%, 84%, 91%, and 87%, respectively. The YOLOv5 algorithm was also used for cell counting and compared to the current segmentation-based U-Net and OpenCV model that has been implemented. In conclusion, the proposed model successfully recognizes and counts the different types of cells present in the laboratory. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-09-06T07:08:19Z No. of bitstreams: 1 ijimai8_3_6.pdf: 697392 bytes, checksum: c1f8f47261458de62606dcb4d737418c (MD5); Made available in DSpace on 2023-09-06T07:08:19Z (GMT). No. of bitstreams: 1 ijimai8_3_6.pdf: 697392 bytes, checksum: c1f8f47261458de62606dcb4d737418c (MD5)

A Survey on Demand-Responsive Transportation for Rural and Interurban Mobility

A Survey on Demand-Responsive Transportation for Rural and Interurban Mobility Martí, Pasqual; Jordán, Jaume; González Arrieta, María Angélica; Julian, Vicente Rural areas have been marginalized when it comes to flexible, quality transportation research. This review article brings together papers that discuss, analyze, model, or experiment with demand-responsive transportation systems applied to rural settlements and interurban transportation, discussing their general feasibility as well as the most successful configurations. For that, demand-responsive transportation is characterized and the techniques used for modeling and optimization are described. Then, a classification of the relevant publications is presented, splitting the contributions into analytical and experimental works. The results of the classification lead to a discussion that states open issues within the topic: replacement of public transportation with demandresponsive solutions, disconnection between theoretical and experimental works, user-centered design and its impact on adoption rate, and a lack of innovation regarding artificial intelligence implementation on the proposed systems. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-09-05T16:15:21Z No. of bitstreams: 1 ijimai8_3_4.pdf: 412656 bytes, checksum: f18ea08635e89eb0ce496ee4f982c51f (MD5); Made available in DSpace on 2023-09-05T16:15:21Z (GMT). No. of bitstreams: 1 ijimai8_3_4.pdf: 412656 bytes, checksum: f18ea08635e89eb0ce496ee4f982c51f (MD5)

Using Large Language Models to Shape Social Robots’ Speech

Using Large Language Models to Shape Social Robots’ Speech Sevilla-Salcedo, Javier; Fernádez-Rodicio, Enrique; Martín-Galván, Laura; Castro-González, Álvaro; Castillo, José C.; Salichs, Miguel A. Social robots are making their way into our lives in different scenarios in which humans and robots need to communicate. In these scenarios, verbal communication is an essential element of human-robot interaction. However, in most cases, social robots’ utterances are based on predefined texts, which can cause users to perceive the robots as repetitive and boring. Achieving natural and friendly communication is important for avoiding this scenario. To this end, we propose to apply state-of- the-art natural language generation models to provide our social robots with more diverse speech. In particular, we have implemented and evaluated two mechanisms: a paraphrasing module that transforms the robot’s utterances while keeping their original meaning, and a module to generate speech about a certain topic that adapts the content of this speech to the robot’s conversation partner. The results show that these models have great potential when applied to our social robots, but several limitations must be considered. These include the computational cost of the solutions presented, the latency that some of these models can introduce in the interaction, the use of proprietary models, or the lack of a subjective evaluation that complements the results of the tests conducted. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-09-04T16:02:13Z No. of bitstreams: 1 ijimai8_3_1.pdf: 440548 bytes, checksum: 5eb947a869e7c9bb1f258fde803c3198 (MD5); Made available in DSpace on 2023-09-04T16:02:13Z (GMT). No. of bitstreams: 1 ijimai8_3_1.pdf: 440548 bytes, checksum: 5eb947a869e7c9bb1f258fde803c3198 (MD5)

Problem Detection in the Edge of IoT Applications

Problem Detection in the Edge of IoT Applications Bernabé-Sánchez, Iván; Fernández, Alberto; Billhardt, Holger; Ossowski, Sascha Due to technological advances, Internet of Things (IoT) systems are becoming increasingly complex. They are characterized by being multi-device and geographically distributed, which increases the possibility of errors of different types. In such systems, errors can occur anywhere at any time and fault tolerance becomes an essential characteristic to make them robust and reliable. This paper presents a framework to manage and detect errors and malfunctions of the devices that compose an IoT system. The proposed solution approach takes into account both, simple devices such as sensors or actuators, as well as computationally intensive devices which are distributed geographically. It uses knowledge graphs to model the devices, the system’s topology, the software deployed on each device and the relationships between the different elements. The proposed framework retrieves information from log messages and processes this information automatically to detect anomalous situations or malfunctions that may affect the IoT system. This work also presents the ECO ontology to organize the IoT system information. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-09-04T15:53:52Z No. of bitstreams: 1 ijimai8_3_8_0.pdf: 896555 bytes, checksum: 147dbd5992157eb21e49bc88dde04a02 (MD5); Made available in DSpace on 2023-09-04T15:53:52Z (GMT). No. of bitstreams: 1 ijimai8_3_8_0.pdf: 896555 bytes, checksum: 147dbd5992157eb21e49bc88dde04a02 (MD5)

Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique

Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique Lakshmi, H. R.; Borra, Surekha With increasing copyright violation cases, watermarking of digital images is a very popular solution for securing online media content. Since some sensitive applications require image recovery after watermark extraction, reversible watermarking is widely preferred. This article introduces a Modified Quadratic Difference Expansion (MQDE) and fractal encryption-based reversible watermarking for securing the copyrights of images. First, fractal encryption is applied to watermarks using Tromino's L-shaped theorem to improve security. In addition, Cuckoo Search-Grey Wolf Optimization (CSGWO) is enforced on the cover image to optimize block allocation for inserting an encrypted watermark such that it greatly increases its invisibility. While the developed MQDE technique helps to improve coverage and visual quality, the novel data-driven distortion control unit ensures optimal performance. The suggested approach provides the highest level of protection when retrieving the secret image and original cover image without losing the essential information, apart from improving transparency and capacity without much tradeoff. The simulation results of this approach are superior to existing methods in terms of embedding capacity. With an average PSNR of 67 dB, the method shows good imperceptibility in comparison to other schemes. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-08-31T10:18:07Z No. of bitstreams: 1 ip2023_08_002.pdf: 4918772 bytes, checksum: d4644403b3cc2f380a852da7aac8b994 (MD5); Made available in DSpace on 2023-08-31T10:18:07Z (GMT). No. of bitstreams: 1 ip2023_08_002.pdf: 4918772 bytes, checksum: d4644403b3cc2f380a852da7aac8b994 (MD5)

The Application of Deep Learning for Classification of Alzheimer's Disease Stages by Magnetic Resonance Imaging Data

The Application of Deep Learning for Classification of Alzheimer's Disease Stages by Magnetic Resonance Imaging Data Irfan, Muhammad; Shahrestani, Seyed; ElKhodr, Mahmoud Detecting Alzheimer’s disease (AD) in its early stages is essential for effective management, and screening for Mild Cognitive Impairment (MCI) is common practice. Among many deep learning techniques applied to assess brain structural changes, Magnetic Resonance Imaging (MRI) and Convolutional Neural Networks (CNN) have grabbed research attention because of their excellent efficiency in automated feature learning of a variety of multilayer perceptron. In this study, various CNNs are trained to predict AD on three different views of MRI images, including Sagittal, Transverse, and Coronal views. This research use T1-Weighted MRI data of 3 years composed of 2182 NIFTI files. Each NIFTI file presents a single patient's Sagittal, Transverse, and Coronal views. T1-Weighted MRI images from the ADNI database are first preprocessed to achieve better representation. After MRI preprocessing, large slice numbers require a substantial computational cost during CNN training. To reduce the slice numbers for each view, this research proposes an intelligent probabilistic approach to select slice numbers such that the total computational cost per MRI is minimized. With hyperparameter tuning, batch normalization, and intelligent slice selection and cropping, an accuracy of 90.05% achieve with the Transverse, 82.4% with Sagittal, and 78.5% with Coronal view, respectively. Moreover, the views are stacked together and an accuracy of 92.21% is achived for the combined views. In addition, results are compared with other studies to show the performance of the proposed approach for AD detection. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-08-28T12:26:24Z No. of bitstreams: 1 ip2023_07_009.pdf: 2326497 bytes, checksum: 9461bad16202051f6035b98f55c7c36f (MD5); Made available in DSpace on 2023-08-28T12:26:24Z (GMT). No. of bitstreams: 1 ip2023_07_009.pdf: 2326497 bytes, checksum: 9461bad16202051f6035b98f55c7c36f (MD5)

What Do We Mean by GenAI? A Systematic Mapping of The Evolution, Trends, and Techniques Involved in Generative AI

What Do We Mean by GenAI? A Systematic Mapping of The Evolution, Trends, and Techniques Involved in Generative AI García-Peñalvo, Francisco; Vázquez-Ingelmo, Andrea Artificial Intelligence has become a focal point of interest across various sectors due to its ability to generate creative and realistic outputs. A specific subset, generative artificial intelligence, has seen significant growth, particularly in late 2022. Tools like ChatGPT, Dall-E, or Midjourney have democratized access to Large Language Models, enabling the creation of human-like content. However, the concept 'Generative Artificial Intelligence lacks a universally accepted definition, leading to potential misunderstandings. While a model that produces any output can be technically seen as generative, the Artificial Intelligent research community often reserves the term for complex models that generate high-quality, human-like material. This paper presents a literature mapping of AI-driven content generation, analyzing 631 solutions published over the last five years to better understand and characterize the Generative Artificial Intelligence landscape. Our findings suggest a dichotomy in the understanding and application of the term "Generative AI". While the broader public often interprets "Generative AI" as AI-driven creation of tangible content, the AI research community mainly discusses generative implementations with an emphasis on the models in use, without explicitly categorizing their work under the term "Generative AI". Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-08-28T12:17:56Z No. of bitstreams: 1 ip2023_07_006.pdf: 2089221 bytes, checksum: 7d853997a9bf3895a42bfd07db9c8615 (MD5); Made available in DSpace on 2023-08-28T12:17:56Z (GMT). No. of bitstreams: 1 ip2023_07_006.pdf: 2089221 bytes, checksum: 7d853997a9bf3895a42bfd07db9c8615 (MD5)

Explaining Query Answers in Probabilistic Databases

Explaining Query Answers in Probabilistic Databases Debbi, Hichem Probabilistic databases have emerged as an extension of relational databases that can handle uncertain data under possible worlds semantics. Although the problems of creating effective means of probabilistic data representation as well as probabilistic query evaluation have been addressed so widely, low attention has been given to query result explanation. While query answer explanation in relational databases tends to answer the question: why is this tuple in the query result? In probabilistic databases, we should ask an additional question: why does this tuple have such a probability? Due to the huge number of resulting worlds of probabilistic databases, query explanation in probabilistic databases is a challenging task. In this paper, we propose a causal explanation technique for conjunctive queries in probabilistic databases. Based on the notions of causality, responsibility and blame, we will be able to address explanation for tuple and attribute uncertainties in a complementary way. Through an experiment on the real-dataset of IMDB, we will see that this framework would be helpful for explaining complex queries results. Comparing to existing explanation methods, our method could be also considered as an aided-diagnosis method through computing the blame, which helps to understand the impact of uncertain attributes. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-08-28T12:06:08Z No. of bitstreams: 1 ip2023_07_005.pdf: 2108435 bytes, checksum: 13c631e121d7879270ce87308c9a49aa (MD5); Made available in DSpace on 2023-08-28T12:06:08Z (GMT). No. of bitstreams: 1 ip2023_07_005.pdf: 2108435 bytes, checksum: 13c631e121d7879270ce87308c9a49aa (MD5)

Research on Brain and Mind Inspired Intelligence

Research on Brain and Mind Inspired Intelligence Liu, Yang; Wei, Jianshe To address the problems of scientific theory, common technology and engineering application of multimedia and multimodal information computing, this paper is focused on the theoretical model, algorithm framework, and system architecture of brain and mind inspired intelligence (BMI) based on the structure mechanism simulation of the nervous system, the function architecture emulation of the cognitive system and the complex behavior imitation of the natural system. Based on information theory, system theory, cybernetics and bionics, we define related concept and hypothesis of brain and mind inspired computing (BMC) and design a model and framework for frontier BMI theory. Research shows that BMC can effectively improve the performance of semantic processing of multimedia and cross-modal information, such as target detection, classification and recognition. Based on the brain mechanism and mind architecture, a semantic-oriented multimedia neural, cognitive computing model is designed for multimedia semantic computing. Then a hierarchical cross-modal cognitive neural computing framework is proposed for cross-modal information processing. Furthermore, a cross-modal neural, cognitive computing architecture is presented for remote sensing intelligent information extraction platform and unmanned autonomous system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-08-28T12:00:15Z No. of bitstreams: 1 ip2023_07_004.pdf: 5526925 bytes, checksum: 5223497ce60b15e0c4c36b946ecfa981 (MD5); Made available in DSpace on 2023-08-28T12:00:15Z (GMT). No. of bitstreams: 1 ip2023_07_004.pdf: 5526925 bytes, checksum: 5223497ce60b15e0c4c36b946ecfa981 (MD5)

Deobfuscating Leetspeak With Deep Learning to Improve Spam Filtering

Deobfuscating Leetspeak With Deep Learning to Improve Spam Filtering Vélez de Mendizabal, Iñaki; Vidriales, Xabier; Basto-Fernandes, Vitor; Ezpeleta, Enaitz; Méndez, José Ramón; Zurutuza, Urko The evolution of anti-spam filters has forced spammers to make greater efforts to bypass filters in order to distribute content over networks. The distribution of content encoded in images or the use of Leetspeak are concrete and clear examples of techniques currently used to bypass filters. Despite the importance of dealing with these problems, the number of studies to solve them is quite small, and the reported performance is very limited. This study reviews the work done so far (very rudimentary) for Leetspeak deobfuscation and proposes a new technique based on using neural networks for decoding purposes. In addition, we distribute an image database specifically created for training Leetspeak decoding models. We have also created and made available four different corpora to analyse the performance of Leetspeak decoding schemes. Using these corpora, we have experimentally evaluated our neural network approach for decoding Leetspeak. The results obtained have shown the usefulness of the proposed model for addressing the deobfuscation of Leetspeak character sequences. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-08-28T11:41:03Z No. of bitstreams: 1 ip2023_07_003.pdf: 1374055 bytes, checksum: c27508a391c30289b9587215e328be22 (MD5); Made available in DSpace on 2023-08-28T11:41:03Z (GMT). No. of bitstreams: 1 ip2023_07_003.pdf: 1374055 bytes, checksum: c27508a391c30289b9587215e328be22 (MD5)

Improving Retrieval Performance of Case Based Reasoning Systems by Fuzzy Clustering

Improving Retrieval Performance of Case Based Reasoning Systems by Fuzzy Clustering Saadi, F.; Atmani, Baghdad; Henni, F. Case-based reasoning (CBR), which is a classical reasoning methodology, has been put to use. Its application has allowed significant progress in resolving problems related to the diagnosis, therapy, and prediction of diseases. However, this methodology has shown some complicated problems that must be resolved, including determining a representation form for the case (complexity, uncertainty, and vagueness of medical information), preventing the case base from the infinite growth of generated medical information and selecting the best retrieval technique. These limitations have pushed researchers to think about other ways of solving problems, and we are recently witnessing the integration of CBR with other techniques such as data mining. In this article, we develop a new approach integrating clustering (Fuzzy C-Means (FCM) and K-Means) in the CBR cycle. Clustering is one of the crucial challenges and has been successfully used in many areas to develop innate structures and hidden patterns for data grouping [1]. The objective of the proposed approach is to solve the limitations of CBR and improve it, particularly in the search for similar cases (retrieval step). The approach is tested with the publicly available immunotherapy dataset. The results of the experimentations show that the integration of the FCM algorithm in the retrieval step reduces the search space (the large volume of information), resolves the problem of the vagueness of medical information, speeds up the calculation and response time, and increases the search efficiency, which further improves the performance of the retrieval step and, consequently, the CBR system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-08-28T11:27:41Z No. of bitstreams: 1 ip2023_07_002.pdf: 1057175 bytes, checksum: 1c18333d633c197eb724c2cb2574d39b (MD5); Made available in DSpace on 2023-08-28T11:27:41Z (GMT). No. of bitstreams: 1 ip2023_07_002.pdf: 1057175 bytes, checksum: 1c18333d633c197eb724c2cb2574d39b (MD5)

IoT Detection System for Mildew Disease in Roses Using Neural Networks and Image Analysis

IoT Detection System for Mildew Disease in Roses Using Neural Networks and Image Analysis Torres, Laura; Romero, Luis; Aguirre, Edgar; Ferro Escobar, Roberto Artificial intelligence presents different approaches, one of these is the use of neural network algorithms, a particular context is the farming sector and these algorithms support the detection of diseases in flowers, this work presents a system to detect downy mildew disease in roses through the analysis of images through neural networks and the correlation of environmental variables through an experiment in a controlled environment, for which an IoT platform was developed that integrated an artificial intelligence module. For the verification of the model, three different models of neural networks in a controlled greenhouse were experimentally compared and a proposed model was obtained for the training and validation sets of two categories of healthy roses and diseased roses with 89% training and 11% recovery. validation and it was determined that the relative humidity variable can influence the development and appearance of Downy Mildew disease when its value is above 85% for a prolonged period. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-08-28T08:56:57Z No. of bitstreams: 1 ip2023_07_001_0.pdf: 4767566 bytes, checksum: 0b015bacd69adb6ae18095b9b1b8b605 (MD5); Made available in DSpace on 2023-08-28T08:56:57Z (GMT). No. of bitstreams: 1 ip2023_07_001_0.pdf: 4767566 bytes, checksum: 0b015bacd69adb6ae18095b9b1b8b605 (MD5)

Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction

Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction Su, Zhan; Yu, Ruiyun; Zou, Shihao; Guo, Bingyang; Cheng, Li Human-Object Interaction (HOI) detection focuses on human-centered visual relationship detection, which is a challenging task due to the complexity and diversity of image content. Unlike most recent HOI detection works that only rely on paired instance-level information in the union range, our proposed Spatial-aware Multilevel Parsing Network (SMPNet) uses a multi-level information detection strategy, including instance-level visual features of detected human-object pair, part-level related features of the human body, and scene-level features extracted by the graph neural network. After fusing the three levels of features, the HOI relationship is predicted. We validate our method on two public datasets, V-COCO and HICO-DET. Compared with prior works, our proposed method achieves the state-of-the-art results on both datasets in terms of mAProle, which demonstrates the effectiveness of our proposed multi-level information detection strategy. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-07-11T13:51:43Z No. of bitstreams: 1 ip2023_06_004.pdf: 8063452 bytes, checksum: 52340a7c1fb803e53fbd3505555ae8c8 (MD5); Made available in DSpace on 2023-07-11T13:51:43Z (GMT). No. of bitstreams: 1 ip2023_06_004.pdf: 8063452 bytes, checksum: 52340a7c1fb803e53fbd3505555ae8c8 (MD5)

Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review

Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review Suárez-Cetrulo, Andrés L.; Quintana, David; Cervantes, Alejandro Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach. It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-07-11T11:48:04Z No. of bitstreams: 1 ip2023_06_003.pdf: 997078 bytes, checksum: fd331c4e733574393c764b1b317d2e13 (MD5); Made available in DSpace on 2023-07-11T11:48:04Z (GMT). No. of bitstreams: 1 ip2023_06_003.pdf: 997078 bytes, checksum: fd331c4e733574393c764b1b317d2e13 (MD5)

Improvement of Academic Analytics Processes Through the Identification of the Main Variables Affecting Early Dropout of First-Year Students in Technical Degrees. A Case Study

Improvement of Academic Analytics Processes Through the Identification of the Main Variables Affecting Early Dropout of First-Year Students in Technical Degrees. A Case Study Llauró, A.; Fonseca, David; Villegas, E.; Aláez, M.; Romero, S. The field of research on the phenomenon of university dropout and the factors that promote it is of the utmost relevance, especially in the current context of the Covid-19 pandemic. Students who have started degrees in the last two years have completed their university studies in periods of lockdown and unlike traditional education, this has often involved taking online classes. In this scenario, the students' motivation and the way they are able to cope with the difficulties of the first year of a university course are very relevant, especially in technical degrees. Previous studies show that a large number of undergraduate students drop out prematurely. In order to act to reduce dropout rates, schools, especially technical schools, should be able to map the entry profile of students and identify the factors that promote early dropout. This paper focuses on identifying, categorizing and evaluating a number of indicators according to the perception of tutors and the field of study, based on the application of quantitative and qualitative techniques. The results support the approach taken, as they show how tutors can identify students at risk of dropping out at the beginning of the course and act proactively to monitor and motivate them. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-07-11T11:19:29Z No. of bitstreams: 1 ip2023_06_002.pdf: 513302 bytes, checksum: 3b2ddb78e187bd1926c84bd4f22b25f2 (MD5); Made available in DSpace on 2023-07-11T11:19:29Z (GMT). No. of bitstreams: 1 ip2023_06_002.pdf: 513302 bytes, checksum: 3b2ddb78e187bd1926c84bd4f22b25f2 (MD5)

An Improved Deep Learning Model for Electricity Price Forecasting

An Improved Deep Learning Model for Electricity Price Forecasting Iqbal, Rashed; Mokhlis, Hazlie; Mohd Khairuddin, Anis Salwa; Azam Muhammad, Munir Accurate electricity price forecasting (EPF) is important for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Besides that, EPF becomes critically important for effective planning and efficient operation of a power system due to deregulation of electricity industry. However, accurate EPF is very challenging due to complex nonlinearity in the time series-based electricity prices. Hence, this work proposed two-fold contributions which are (1) effective time series preprocessing module to ensure feasible time-series data is fitted in the deep learning model, and (2) an improved long short-term memory (LSTM) model by incorporating linear scaled hyperbolic tangent (LiSHT) layer in the EPF. In this work, the time series pre-processing module adopted linear trend of the correlated features of electricity price series and the time series are tested by using Augmented Dickey Fuller (ADF) test method. In addition, the time series are transformed using boxcox transformation method in order to satisfy the stationarity property. Then, an improved LSTM prediction module is proposed to forecast electricity prices where LiSHT layer is adopted to optimize the parameters of the heterogeneous LSTM. This study is performed using the Australian electricity market price, load and renewable energy supply data. The experimental results obtained show that the proposed EPF framework performed better compared to previous techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-07-11T10:59:08Z No. of bitstreams: 1 ip2023_06_001.pdf: 2496568 bytes, checksum: abb16105141372c6a491ec064dbf9a26 (MD5); Made available in DSpace on 2023-07-11T10:59:08Z (GMT). No. of bitstreams: 1 ip2023_06_001.pdf: 2496568 bytes, checksum: abb16105141372c6a491ec064dbf9a26 (MD5)

Editor’s Note

Editor’s Note Gaona-García, Paulo Alonso Artificial Intelligence (AI) represents one of the fastest growing areas of knowledge, sectors and fields of action globally. This growth has allowed to mark different positions, where the most favorable ones are oriented to its unquestionable contribution to facilitate decision making in various fields of society, as well as other sectors that mark a strong position for its use to be carried out in a regulated and measured way due to the scope and risks to which we are exposed. For this reason, rigorous methods are increasingly required for the design and development of AI-based computational models; methods that involve strict mechanisms for their validation, as well as the analysis of possible risks and scope that they may have on the field of application where they are being exposed. This type of aspects would definitely mark a valuable and relevant milestone to define several paths within which we can find two: 1) if it is definitely necessary to set limits on the use of AI by establishing increasingly sophisticated regulatory frameworks on various areas involving data protection and regulated use of the same, and 2) to remove all barriers so that it can be exploited openly in all its dimensions in any area of our society. Hence the importance of analysing the different risks and threats that AI may present within the particular context in which it is being applied. Based on this panorama, this regular edition of the “International Journal Interactive Multimedia and Artificial Intelligence” presents a series of papers where proposals are oriented to different fields and sectors, which make use of diverse approaches, methods, models and AI-based systems that allow us to have a generalized idea of how these challenges are being addressed in some fields of our society. In particular, this regular issue collects research topics focusing on addressing the problems of evolving recommender systems, classification models, decision support systems, system modelling, data analytics, optimization algorithms, image retrieval, deep neural networks, social network analysis, and the relevance of the design of User Experience (UX) proposals. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-06-05T14:28:13Z No. of bitstreams: 1 ijimai8_2_0_1.pdf: 95746 bytes, checksum: c911ddb3a3adcb6f9dd5511649802c70 (MD5); Made available in DSpace on 2023-06-05T14:28:13Z (GMT). No. of bitstreams: 1 ijimai8_2_0_1.pdf: 95746 bytes, checksum: c911ddb3a3adcb6f9dd5511649802c70 (MD5)

ResNet18 Supported Inspection of Tuberculosis in Chest Radiographs With Integrated Deep, LBP, and DWT Features

ResNet18 Supported Inspection of Tuberculosis in Chest Radiographs With Integrated Deep, LBP, and DWT Features Rajinikanth, Venkatesan; Kadry, Seifedine; Moreno-Ger, Pablo The lung is a vital organ in human physiology and disease in lung causes various health issues. The acute disease in lung is a medical emergency and hence several methods are developed and implemented to detect the lung abnormality. Tuberculosis (TB) is one of the common lung disease and premature diagnosis and treatment is necessary to cure the disease with appropriate medication. Clinical level assessment of TB is commonly performed with chest radiographs (X-ray) and the recorded images are then examined to identify TB and its harshness. This research proposes a TB detection framework using integrated optimal deep and handcrafted features. The different stages of this work include (i) X-ray collection and processing, (ii) Pretrained Deep-Learning (PDL) scheme-based feature mining, (iii) Feature extraction with Local Binary Pattern (LBP) and Discrete Wavelet Transform (DWT), (iv) Feature optimization with Firefly-Algorithm, (v) Feature ranking and serial concatenation, and (vi) Classification by means of a 5-fold cross confirmation. The result of this study validates that, the ResNet18 scheme helps to achieve a better accuracy with SoftMax (95.2%) classifier and Decision Tree Classifier (99%) with deep and concatenated features, respectively. Further, overall performance of Decision Tree is better compared to other classifiers. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-06-05T14:02:05Z No. of bitstreams: 1 ijimai8_2_4.pdf: 3550633 bytes, checksum: 4967a3d490556c67b7a1f4f2ef738197 (MD5); Made available in DSpace on 2023-06-05T14:02:05Z (GMT). No. of bitstreams: 1 ijimai8_2_4.pdf: 3550633 bytes, checksum: 4967a3d490556c67b7a1f4f2ef738197 (MD5)

Digit Recognition Using Composite Features With Decision Tree Strategy

Digit Recognition Using Composite Features With Decision Tree Strategy Chen, Chung-Hsing; Huang, Ko-Wei At present, check transactions are one of the most common forms of money transfer in the market. The information for check exchange is printed using magnetic ink character recognition (MICR), widely used in the banking industry, primarily for processing check transactions. However, the magnetic ink card reader is specialized and expensive, resulting in general accounting departments or bookkeepers using manual data registration instead. An organization that deals with parts or corporate services might have to process 300 to 400 checks each day, which would require a considerable amount of labor to perform the registration process. The cost of a single-sided scanner is only 1/10 of the MICR; hence, using image recognition technology is an economical solution. In this study, we aim to use multiple features for character recognition of E13B, comprising ten numbers and four symbols. For the numeric part, we used statistical features such as image density features, geometric features, and simple decision trees for classification. The symbols of E13B are composed of three distinct rectangles, classified according to their size and relative position. Using the same sample set, MLP, LetNet-5, Alexnet, and hybrid CNN-SVM were used to train the numerical part of the artificial intelligence network as the experimental control group to verify the accuracy and speed of the proposed method. The results of this study were used to verify the performance and usability of the proposed method. Our proposed method obtained all test samples correctly, with a recognition rate close to 100%. A prediction time of less than one millisecond per character, with an average value of 0.03 ms, was achieved, over 50 times faster than state-of-the-art methods. The accuracy rate is also better than all comparative state-of-the-art methods. The proposed method was also applied to an embedded device to ensure the CPU would be used for verification instead of a high-end GPU. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-06-05T13:55:48Z No. of bitstreams: 1 ijimai8_2_10.pdf: 2043799 bytes, checksum: a6c642303d81bfc5179a8053e5e7e086 (MD5); Made available in DSpace on 2023-06-05T13:55:48Z (GMT). No. of bitstreams: 1 ijimai8_2_10.pdf: 2043799 bytes, checksum: a6c642303d81bfc5179a8053e5e7e086 (MD5)

Efficient Gated Convolutional Recurrent Neural Networks for Real-Time Speech Enhancement

Efficient Gated Convolutional Recurrent Neural Networks for Real-Time Speech Enhancement Fazal-E -Wahab; Ye, Zhongfu; Saleem, Nasir; Ali, Hamza Deep learning (DL) networks have grown into powerful alternatives for speech enhancement and have achieved excellent results by improving speech quality, intelligibility, and background noise suppression. Due to high computational load, most of the DL models for speech enhancement are difficult to implement for realtime processing. It is challenging to formulate resource efficient and compact networks. In order to address this problem, we propose a resource efficient convolutional recurrent network to learn the complex ratio mask for real-time speech enhancement. Convolutional encoder-decoder and gated recurrent units (GRUs) are integrated into the Convolutional recurrent network architecture, thereby formulating a causal system appropriate for real-time speech processing. Parallel GRU grouping and efficient skipped connection techniques are engaged to achieve a compact network. In the proposed network, the causal encoder-decoder is composed of five convolutional (Conv2D) and deconvolutional (Deconv2D) layers. Leaky linear rectified unit (ReLU) is applied to all layers apart from the output layer where softplus activation to confine the network output to positive is utilized. Furthermore, batch normalization is adopted after every convolution (or deconvolution) and prior to activation. In the proposed network, different noise types and speakers can be used in training and testing. With the LibriSpeech dataset, the experiments show that the proposed real-time approach leads to improved objective perceptual quality and intelligibility with much fewer trainable parameters than existing LSTM and GRU models. The proposed model obtained an average of 83.53% STOI scores and 2.52 PESQ scores, respectively. The quality and intelligibility are improved by 31.61% and 17.18% respectively over noisy speech. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-06-01T10:22:12Z No. of bitstreams: 1 ip2023_05_007.pdf: 2253358 bytes, checksum: 2f2676659a89cc17ef33fee27b97df02 (MD5); Made available in DSpace on 2023-06-01T10:22:12Z (GMT). No. of bitstreams: 1 ip2023_05_007.pdf: 2253358 bytes, checksum: 2f2676659a89cc17ef33fee27b97df02 (MD5)

ConvGRU-CNN: Spatiotemporal Deep Learning for Real-World Anomaly Detection in Video Surveillance System

ConvGRU-CNN: Spatiotemporal Deep Learning for Real-World Anomaly Detection in Video Surveillance System Qasim Gandapur, Maryam; Verdú, Elena Video surveillance for real-world anomaly detection and prevention using deep learning is an important and difficult research area. It is imperative to detect and prevent anomalies to develop a nonviolent society. Realworld video surveillance cameras automate the detection of anomaly activities and enable the law enforcement systems for taking steps toward public safety. However, a human-monitored surveillance system is vulnerable to oversight anomaly activity. In this paper, an automated deep learning model is proposed in order to detect and prevent anomaly activities. The real-world video surveillance system is designed by implementing the ResNet-50, a Convolutional Neural Network (CNN) model, to extract the high-level features from input streams whereas temporal features are extracted by the Convolutional GRU (ConvGRU) from the ResNet-50 extracted features in the time-series dataset. The proposed deep learning video surveillance model (named ConvGRUCNN) can efficiently detect anomaly activities. The UCF-Crime dataset is used to evaluate the proposed deep learning model. We classified normal and abnormal activities, thereby showing the ability of ConvGRU-CNN to find a correct category for each abnormal activity. With the UCF-Crime dataset for the video surveillance-based anomaly detection, ConvGRU-CNN achieved 82.22% accuracy. In addition, the proposed model outperformed the related deep learning models. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-06-01T10:12:22Z No. of bitstreams: 1 ip2023_05_006_0.pdf: 2055534 bytes, checksum: 070dd17271be2ce11fcd266d68791423 (MD5); Made available in DSpace on 2023-06-01T10:12:22Z (GMT). No. of bitstreams: 1 ip2023_05_006_0.pdf: 2055534 bytes, checksum: 070dd17271be2ce11fcd266d68791423 (MD5)

A Benchmark for the UEQ+ Framework: Construction of a Simple Tool to Quickly Interpret UEQ+ KPIs

A Benchmark for the UEQ+ Framework: Construction of a Simple Tool to Quickly Interpret UEQ+ KPIs Meiners, Anna-Lena; Schrepp, Martin; Hinderks, Andreas; Thomaschewski, Jörg Questionnaires are a highly efficient method to compare the user experience (UX) of different interactive products or versions of a single product. Concretely, they allow us to evaluate the UX easily and to compare different products with a numeric UX score. However, often only one UX score from a single evaluated product is available. Without a comparison to other measurements, it is difficult to interpret an individual score, e.g. to decide whether a product’s UX is good enough to compete in the market. Many questionnaires offer benchmarks to support researchers in these cases. A benchmark is the result of a larger set of product evaluations performed with the same questionnaire. The score obtained from a single product evaluation can be compared to the scores from this benchmark data set to quickly interpret the results. In this paper, the first benchmark for the UEQ+ (User Experience Questionnaire +) is presented, which was created using 3.290 UEQ+ responses for 26 successful software products. The UEQ+ is a modular framework that contains a high number of validated user experience scales that can be combined to form a UX questionnaire. Currently, no benchmark is available for this framework, making the benchmark constructed in this paper a valuable interpretation tool for UEQ+ questionnaires. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-06-01T10:03:31Z No. of bitstreams: 1 ip2023_05_003.pdf: 956451 bytes, checksum: 1ebeb9b7b4361cfbbd2175e1e172f038 (MD5); Made available in DSpace on 2023-06-01T10:03:31Z (GMT). No. of bitstreams: 1 ip2023_05_003.pdf: 956451 bytes, checksum: 1ebeb9b7b4361cfbbd2175e1e172f038 (MD5)

Supporting Skill Assessment in Learning Experiences Based on Serious Games Through Process Mining Techniques

Supporting Skill Assessment in Learning Experiences Based on Serious Games Through Process Mining Techniques Caballero-Hernández, Juan Antonio; Palomo-Duarte, Manuel; Dodero, Juan Manuel; Gaševic, Dragan Learning experiences based on serious games are employed in multiple contexts. Players carry out multiple interactions during the gameplay to solve the different challenges faced. Those interactions can be registered in logs as large data sets providing the assessment process with objective information about the skills employed. Most assessment methods in learning experiences based on serious games rely on manual approaches, which do not scalewell when the amount of data increases. We propose an automated method to analyse students’ interactions and assess their skills in learning experiences based on serious games. The method takes into account not only the final model obtained by the student, but also the process followed to obtain it, extracted from game logs. The assessment method groups students according to their in-game errors and ingame outcomes. Then, the models for the most and the least successful students are discovered using process mining techniques. Similarities in their behaviour are analysed through conformance checking techniques to compare all the students with the most successful ones. Finally, the similarities found are quantified to build a classification of the students’ assessments. We have employed this method with Computer Science students playing a serious game to solve design problems in a course on databases. The findings show that process mining techniques can palliate the limitations of skill assessment methods in game-based learning experiences. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-06-01T09:53:03Z No. of bitstreams: 1 ip2023_05_002.pdf: 2378538 bytes, checksum: cbb82497cc2949dd439295c221f7095d (MD5); Made available in DSpace on 2023-06-01T09:53:03Z (GMT). No. of bitstreams: 1 ip2023_05_002.pdf: 2378538 bytes, checksum: cbb82497cc2949dd439295c221f7095d (MD5)

Graffiti Identification System Using Low-Cost Sensors

Graffiti Identification System Using Low-Cost Sensors García García, Miguel; González Arrieta, María Angélica; Rodríguez González, Sara; Márquez-Sánchez, Sergio; Da Silva Ramos, Carlos Fernando This article introduces the possibility of studying graffiti using a colorimeter developed with Arduino hardware technology according to the Do It Yourself (DIY) philosophy. Through the obtained Red Green Blue (RGB) data it is intended to study and compare the information extracted from each of the graffiti present on different walls. The same color can be found in different parts of a single graffiti, but also in other graffiti that could a priori be of different authorship. Nevertheless, graffiti may be related, and it may be possible to group graffiti artists and "gangs" that work together. The methodology followed for the construction of the colorimeter and its real application in a practical case are described in four case studies. The case studies describe how graffiti were identified and recognized and they provide a comparison of the collected color samples. The results show the added value of the colorimeter in the graffiti recognition process, demonstrating its usefulness on a functional level. Finally, the contributions of this research are outlined, and an analysis is carried out of the changes to be made to the proposed method in the future, for improved graffiti color identification. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-06-01T09:42:02Z No. of bitstreams: 1 ip2023_05_001.pdf: 4129896 bytes, checksum: 1327ead89f0c7692e534ae14e61b9f4b (MD5); Made available in DSpace on 2023-06-01T09:42:02Z (GMT). No. of bitstreams: 1 ip2023_05_001.pdf: 4129896 bytes, checksum: 1327ead89f0c7692e534ae14e61b9f4b (MD5)

Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems

Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems Bobadilla, Jesús; Dueñas-Lerín, Jorge; Ortega, Fernando; Gutiérrez, Abraham Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unordered lists, novelty, and diversity. Results show each convenient matrix factorization model attending to their simplicity, the required prediction quality, the necessary recommendation quality, the desired recommendation novelty and diversity, the need to explain recommendations, the adequacy of assigning semantic interpretations to hidden factors, the advisability of recommending to groups of users, and the need to obtain reliability values. To ensure the reproducibility of the experiments, an open framework has been used, and the implementation code is provided. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-05-03T10:32:26Z No. of bitstreams: 1 ip2023_04_008.pdf: 6298241 bytes, checksum: 78a2b02200b2d8058f71baecdc8062ba (MD5); Made available in DSpace on 2023-05-03T10:32:26Z (GMT). No. of bitstreams: 1 ip2023_04_008.pdf: 6298241 bytes, checksum: 78a2b02200b2d8058f71baecdc8062ba (MD5)

Exploring ChatGPT's Potential for Consultation, Recommendations and Report Diagnosis: Gastric Cancer and Gastroscopy Reports’ Case

Exploring ChatGPT's Potential for Consultation, Recommendations and Report Diagnosis: Gastric Cancer and Gastroscopy Reports’ Case Zhou, Jiaming; Li, Tengyue; Fong, Simon James; Dey, Nilanjan; González-Crespo, Rubén Artificial intelligence (AI) has shown its effectiveness in helping clinical users meet evolving challenges. Recently, ChatGPT, a newly launched AI chatbot with exceptional text comprehension capabilities, has triggered a global wave of AI popularization and application in seeking answers through human‒machine dialogues. Gastric cancer, as a globally prevalent disease, has a five-year survival rate of up to 90% when detected early and treated promptly. This research aims to explore ChatGPT's potential in disseminating gastric cancer knowledge, providing consultation recommendations, and interpreting endoscopy reports. Through experimentation, the GPT-4 model of ChatGPT achieved an appropriateness of 91.3% and a consistency of 95.7% in a gastric cancer knowledge test. Furthermore, GPT-4 has demonstrated considerable potential in consultation recommendations and endoscopy report analysis. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-05-03T10:25:57Z No. of bitstreams: 1 ip2023_04_007.pdf: 918647 bytes, checksum: 4646c89b726a20dbc836aa8df0455beb (MD5); Made available in DSpace on 2023-05-03T10:25:57Z (GMT). No. of bitstreams: 1 ip2023_04_007.pdf: 918647 bytes, checksum: 4646c89b726a20dbc836aa8df0455beb (MD5)

Adaptation of Applications to Compare Development Frameworks in Deep Learning for Decentralized Android Applications

Adaptation of Applications to Compare Development Frameworks in Deep Learning for Decentralized Android Applications Sainz-de-Abajo, Beatriz; Laso, Sergio; Garcia-Alonso, Jose Not all frameworks used in machine learning and deep learning integrate with Android, which requires some prerequisites. The primary objective of this paper is to present the results of the analysis and a comparison of deep learning development frameworks, which can be adapted into fully decentralized Android apps from a cloud server. As a work methodology, we develop and/or modify the test applications that these frameworks offer us a priori in such a way that it allows an equitable comparison of the analysed characteristics of interest. These parameters are related to attributes that a user would consider, such as (1) percentage of success; (2) battery consumption; and (3) power consumption of the processor. After analysing numerical results, the proposed framework that best behaves in relation to the analysed characteristics for the development of an Android application is TensorFlow, which obtained the best score against Caffe2 and Snapdragon NPE in the percentage of correct answers, battery consumption, and device CPU power consumption. Data consumption was not considered because we focus on decentralized cloud storage applications in this study. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-05-03T10:16:58Z No. of bitstreams: 1 ip2023_04_006.pdf: 2388930 bytes, checksum: 7da9701560ea4c79115755aca69cea4c (MD5); Made available in DSpace on 2023-05-03T10:16:58Z (GMT). No. of bitstreams: 1 ip2023_04_006.pdf: 2388930 bytes, checksum: 7da9701560ea4c79115755aca69cea4c (MD5)

Reliability of IBM’s Public Quantum Computers

Reliability of IBM’s Public Quantum Computers Pérez-Antón, Raquel; Corbi, Alberto; López-Sánchez, José Ignacio; Burgos, Daniel One of the challenges of the current ecosystem of quantum computers (QC) is the stabilization of the coherence associated with the entanglement of the states of their inner qubits. In this empirical study, we monitor the reliability of IBM’s public-access QCs network on a daily basis. Each of these state-of-the-art machines has a totally different qubit association, and this entails that for a given (same) input program, they may output a different set of probabilities for the assembly of results (including both the right and the wrong ones). Although we focus on the computing structure provided by the “Big Blue” company, our survey can be easily transferred to other currently available quantum mainframes. In more detail, we probe these quantum processors with an ad hoc designed computationally demanding quaternary search algorithm. As stated, this quantum program is executed every 24 hours (for nearly 100 days) and its goal is to put to the limit the operational capacity of this novel and genuine type of equipment. Next, we perform a comparative analysis of the obtained results according to the singularities of each computer and over the total number of executions. In addition, we subsequently apply (for 50 days) an improvement filtering to perform noise mitigation on the results obtained proposed by IBM. The Yorktown 5-qubit computer reaches noise filtering of up to 33% in one day, that is, a 90% confidence level is reached in the expected results. From our continuous and long-term tests, we derive that room still exists regarding the improvement of quantum calculators in order to guarantee enough confidence in the returned outcomes. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-05-03T10:09:15Z No. of bitstreams: 1 ip2023_04_005.pdf: 1799939 bytes, checksum: dfac3fefecc5e4a8a6ba36ec4e5cc70f (MD5); Made available in DSpace on 2023-05-03T10:09:15Z (GMT). No. of bitstreams: 1 ip2023_04_005.pdf: 1799939 bytes, checksum: dfac3fefecc5e4a8a6ba36ec4e5cc70f (MD5)

Aligning Figurative Paintings With Their Sources for Semantic Interpretation

Aligning Figurative Paintings With Their Sources for Semantic Interpretation Aslan, Sinem; Steels, Luc This paper reports steps in probing the artistic methods of figurative painters through computational algorithms. We explore a comparative method that investigates the relation between the source of a painting, typically a photograph or an earlier painting, and the painting itself. A first crucial step in this process is to find the source and to crop, standardize and align it to the painting so that a comparison becomes possible. The next step is to apply different low-level algorithms to construct difference maps for color, edges, texture, brightness, etc. From this basis, various subsequent operations become possible to detect and compare features of the image, such as facial action units and the emotions they signify. This paper demonstrates a pipeline we have built and tested using paintings by a renowned contemporary painter Luc Tuymans. We focus in this paper particularly on the alignment process, on edge difference maps, and on the utility of the comparative method for bringing out the semantic significance of a painting. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-05-03T09:31:36Z No. of bitstreams: 1 ip2023_04_004_0.pdf: 5966615 bytes, checksum: 10749d690dd51e22ada8b5c8bc621930 (MD5); Made available in DSpace on 2023-05-03T09:31:36Z (GMT). No. of bitstreams: 1 ip2023_04_004_0.pdf: 5966615 bytes, checksum: 10749d690dd51e22ada8b5c8bc621930 (MD5)

Analysis of Gender Differences in Facial Expression Recognition Based on Deep Learning Using Explainable Artificial Intelligence

Analysis of Gender Differences in Facial Expression Recognition Based on Deep Learning Using Explainable Artificial Intelligence Manresa-Yee, Cristina; Ramis, Silvia; Buades, José M. Potential uses of automated Facial Expression Recognition (FER) cover a wide range of applications such as customer behavior analysis, healthcare applications or providing personalized services. Data for machine learning play a fundamental role, therefore, understanding the relevancy of the data in the outcomes is of utmost importance. In this work we present a study on how gender influences the learning of a FER system. We analyze with Explainable Artificial intelligence (XAI) techniques how gender contributes to the learning and assess which facial expressions are more similar regarding face regions that impact on the classification. Results show that there exist common regions in some expressions both for females and males with different intensities (e.g. happiness); however, there are other expressions like disgust, where important face regions differ. The insights of this work will help improving FER systems and understand the source of any inequality. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-05-03T09:22:35Z No. of bitstreams: 1 ip2023_04_003.pdf: 2400476 bytes, checksum: 29a450353bf34f3a9da5ddb4e01f8a22 (MD5); Made available in DSpace on 2023-05-03T09:22:35Z (GMT). No. of bitstreams: 1 ip2023_04_003.pdf: 2400476 bytes, checksum: 29a450353bf34f3a9da5ddb4e01f8a22 (MD5)

PeopleNet: A Novel People Counting Framework for Head-Mounted Moving Camera Videos

PeopleNet: A Novel People Counting Framework for Head-Mounted Moving Camera Videos Tomar, A.; Kumar, S.; Pant, B. Traditional crowd counting (optical flow or feature matching) techniques have been upgraded to deep learning (DL) models due to their lack of automatic feature extraction and low-precision outcomes. Most of these models were tested on surveillance scene crowd datasets captured by stationary shooting equipment. It is very challenging to perform people counting from the videos shot with a head-mounted moving camera; this is mainly due to mixing the temporal information of the moving crowd with the induced camera motion. This study proposed a transfer learning-based PeopleNet model to tackle this significant problem. For this, we have made some significant changes to the standard VGG16 model, by disabling top convolutional blocks and replacing its standard fully connected layers with some new fully connected and dense layers. The strong transfer learning capability of the VGG16 network yields in-depth insights of the PeopleNet into the good quality of density maps resulting in highly accurate crowd estimation. The performance of the proposed model has been tested over a self-generated image database prepared from moving camera video clips, as there is no public and benchmark dataset for this work. The proposed framework has given promising results on various crowd categories such as dense, sparse, average, etc. To ensure versatility, we have done self and cross-evaluation on various crowd counting models and datasets, which proves the importance of the PeopleNet model in adverse defense of society. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-05-03T09:09:31Z No. of bitstreams: 1 ip2023_04_002_.pdf: 6031442 bytes, checksum: fd5da7b99c7eabd3a996764acecebd7f (MD5); Made available in DSpace on 2023-05-03T09:09:31Z (GMT). No. of bitstreams: 1 ip2023_04_002_.pdf: 6031442 bytes, checksum: fd5da7b99c7eabd3a996764acecebd7f (MD5)

Development of a Shared UX Vision Based on UX Factors Ascertained Through Attribution

Development of a Shared UX Vision Based on UX Factors Ascertained Through Attribution Winter, Dominique; Hausmann, Carolin; Hinderks, Andreas; Thomaschewski, Jörg User experience (UX) is an important quality in differentiating products. For a product team, it is a challenge to develop a good positive user experience. A common UX vision for the product team supports the team in making goal-oriented decisions regarding the user experience. This paper presents an approach to developing a shared UX vision. This UX vision is developed by the product team while a collaborative session. To validate our approach, we conducted a first validation study. In this study, we conducted a collaborative session with two groups and a total of 37 participants. The group of participants comprised product managers, UX designers and comparable professional profiles. At the end of the collaborative session, participants had to fill out a questionnaire. Through questions and observations, we identified ten good practices and four bad practices in the application of our approach to developing a UX vision. The top 3 good practices mentioned by the participants include the definition of decision-making procedures (G1), determining the UX vision with the team (G2), and using general factors of the UX as a basis (G3). The top 3 bad practices are: providing too little time for the development of the UX vision (B1), not providing clear cluster designations (B2) and working without user data (B3). The results show that the present approach for developing a UX vision helps to promote a shared understanding of the intended UX in a quickly and simply way. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-05-03T08:51:40Z No. of bitstreams: 1 ip2023_04_001.pdf: 1637462 bytes, checksum: f2830f59be2d0b581d0f5e756ad54183 (MD5); Made available in DSpace on 2023-05-03T08:51:41Z (GMT). No. of bitstreams: 1 ip2023_04_001.pdf: 1637462 bytes, checksum: f2830f59be2d0b581d0f5e756ad54183 (MD5)

Use of Data Mining for Intelligent Evaluation of Imputation Methods

Use of Data Mining for Intelligent Evaluation of Imputation Methods la Red, David L.; Primorac, Carlos R. In real-world situations, researchers frequently face the difficulty of missing values (MV), i.e., values not observed in a data set. Data imputation techniques allow the estimation of MV using different algorithms, by means of which important data can be imputed for a particular instance. Most of the literature in this field deals with different imputation methods. However, few studies deal with a comparative evaluation of the different methods as to provide more appropriate guidelines for the selection of the method to be applied to impute data for specific situations. The objective of this work is to show a methodology for evaluating the performance of imputation methods by means of new metrics derived from data mining processes, using quality metrics of data mining models. We started from the complete dataset that was amputated with different amputation mechanisms to generate 63 datasets with MV; these were imputed using Median, k-NN, k-Means and Hot-Deck imputation methods. The performance of the imputation methods was evaluated using new metrics derived from quality metrics of the data mining processes, performed with the original full file and with the imputed files. This evaluation is not based on measuring the error when imputing (usual operation), but on considering the similarity of the values of the quality metrics of the data mining processes obtained with the original file and with the imputed files. The results show that –globally considered and according to the new proposed metric, the imputation methods that showed the best performance were k-NN and k-Means. An additional advantage of the proposed methodology is that it provides predictive data mining models that can be used a posteriori. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-04-05T08:31:53Z No. of bitstreams: 1 ip2023_03_002.pdf: 3509410 bytes, checksum: 4784064166409fee926d2e983625eab2 (MD5); Made available in DSpace on 2023-04-05T08:31:54Z (GMT). No. of bitstreams: 1 ip2023_03_002.pdf: 3509410 bytes, checksum: 4784064166409fee926d2e983625eab2 (MD5)

Longitudinal Segmented Analysis of Internet Usage and Well-Being Among Older Adults

Longitudinal Segmented Analysis of Internet Usage and Well-Being Among Older Adults Cervantes, Alejandro; Quintana, David; Saez, Yago; Isasi, Pedro The connection between digital literacy and the three core dimensions of psychological well-being is not yet well understood, and the evidence is controversial. We analyzed a sample of 2,314 individuals, aged 50 years and older, that participated in the English Longitudinal Study of Aging. Participants were clustered according to drivers of psychological well-being using Self-Organizing Maps. The resulting groups were subsequently studied separately using generalized estimating equations fitted on 2-year lagged repeated measures using three scales to capture the dimensions of well-being and Markov models. The clustering analysis suggested the existence of four different groups of participants. Statistical models found differences in the connection between internet use and psychological well-being depending on the group. The Markov models showed a clear association between internet use and the potential for transition among groups of the population characterized, among other things, by higher levels of psychological well-being. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-15T14:19:08Z No. of bitstreams: 1 ip2022_05_002.pdf: 742179 bytes, checksum: 9e2c29ca131f0bcbd368e0c12f8e11cd (MD5); Made available in DSpace on 2023-03-15T14:19:08Z (GMT). No. of bitstreams: 1 ip2022_05_002.pdf: 742179 bytes, checksum: 9e2c29ca131f0bcbd368e0c12f8e11cd (MD5)

On the Importance of UX Quality Aspects for Different Product Categories

On the Importance of UX Quality Aspects for Different Product Categories Schrepp, Martin; Kollmorgen, Jessica; Meiners, Anna-Lena; Hinderks, Andreas; Winter, Dominique; Santoso, Harry B.; Thomaschewski, Jörg User experience (UX) is a holistic concept. We conceptualize UX as a set of semantically distinct quality aspects. These quality aspects relate subjectively perceived properties of the user interaction with a product to the psychological needs of users. Not all possible UX quality aspects are equally important for all products. The main use case of a product can determine the relative importance of UX aspects for the overall impression of the UX. In this paper, the authors present several studies that investigate this dependency between the product category and the importance of several well-known UX aspects. A method to measure the importance of such UX aspects is presented. In addition, the authors show that the observed importance ratings are stable, i.e., reproducible, and hardly influenced by demographic factors or cultural background. Thus, the ratings reported in our studies can be reused by UX professionals to find out which aspects of UX they should concentrate on in product design and evaluation. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-15T10:21:46Z No. of bitstreams: 1 ip2023_03_001.pdf: 1323641 bytes, checksum: 56fa34ed7b6a0677146aedec4cd55891 (MD5); Made available in DSpace on 2023-03-15T10:21:46Z (GMT). No. of bitstreams: 1 ip2023_03_001.pdf: 1323641 bytes, checksum: 56fa34ed7b6a0677146aedec4cd55891 (MD5)

Rhetorical Pattern Finding

Rhetorical Pattern Finding Gómez, Francisco; Tizón Díaz, Manuel; Arronte Alvarez, Aitor; Padilla, Victor In this paper, we research rhetorical patterns from a musicological and computational standpoint. First, a theoretical examination of what constitutes a rhetorical pattern is conducted. Out of that examination, which includes primary sources and the study of the main composers, a formal definition of rhetorical patterns is proposed. Among the rhetorical figures, a set of imitative rhetorical figures is selected for our study, namely, epizeuxis, palilogy, synonymia, and polyptoton. Next, we design a computational model of the selected rhetorical patterns to automatically find those patterns in a corpus consisting of masses by Renaissance composer Tomás Luis de Victoria. In order to have a ground truth with which to test out our model, a group of experts manually annotated the rhetorical patterns. To deal with the problem of reaching a consensus on the annotations, a four-round Delphi method was followed by the annotators. The rhetorical patterns found by the annotators and by the algorithm are compared and their differences discussed. The algorithm reports almost all the patterns annotated by the experts and some additional patterns. The algorithm reports almost all the patterns annotated by the experts (recall: 98.11%) and some additional patterns (precision: 71.73%). These patterns correspond to rhetorical patterns within other rhetorical patterns, which were overlooked by the annotators on the basis of their contextual knowledge. These results pose issues as to how to integrate that contextual knowledge into the computational model. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-15T10:09:25Z No. of bitstreams: 1 ip2022_10_002.pdf: 1408728 bytes, checksum: 5f3989a04ead9602a8cbf4325fca66c2 (MD5); Made available in DSpace on 2023-03-15T10:09:25Z (GMT). No. of bitstreams: 1 ip2022_10_002.pdf: 1408728 bytes, checksum: 5f3989a04ead9602a8cbf4325fca66c2 (MD5)

A Hybrid Secure Cloud Platform Maintenance Based on Improved Attribute-Based Encryption Strategies

A Hybrid Secure Cloud Platform Maintenance Based on Improved Attribute-Based Encryption Strategies Kumar, Abhishek; Kumar, Swarn Avinash; Dutt, Vishal; Dubey, A. K.; Narang, Sushil In the modern era, Cloud Platforms are the most needed port to maintain documents remotely with proper security norms. The concept of cloud environments is similar to the network channel. Still, the Cloud is considered the refined form of network, in which the data can easily be stored into the server without any range restrictions. The data maintained into the remote server needs a high-security feature, and the processing power of data should be high to retrieve the data back from the respective server. In the past, there were several security schemes available to protect the remote cloud server reasonably. However, the attack possibilities over the cloud platform remain; only all the researchers continuously work on this platform without any delay. This paper introduces a hybrid data security scheme called the Improved Attribute-Based Encryption Scheme (IABES). This IABES combines two powerful data security algorithms: Advanced Encryption Standard (AES) and Attribute-Based Encryption (ABE) algorithm. These two algorithms are combined to provide massive support to the proposed approach of data maintenance over the remote cloud server with high-end security norms. This hybrid data security algorithm assures the data cannot be attacked over the server by the attacker or intruder in any case because of its robustness. The essential generation process generates a credential for the users. It cannot be identified or visible to anyone as well as the generated certificates cannot be extracted even if the corresponding user forgets the credentials. The only way to get back the certification is resetting the credential. The obtained results prove the accuracy level of the proposed cypher security schemes compared with the regular cloud security management scheme, and the proposed algorithm essential generation process is unique. No one can guess or acquire it. Even the person may be the service provider or server administrator. For all, the proposed system assures data maintenance over the cloud platform with a high level of security and robustness in Quality of Service. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-15T09:41:36Z No. of bitstreams: 1 ip2021_11_004.pdf: 1309547 bytes, checksum: 411470b57830a77ebfaabbf6704f3cda (MD5); Made available in DSpace on 2023-03-15T09:41:36Z (GMT). No. of bitstreams: 1 ip2021_11_004.pdf: 1309547 bytes, checksum: 411470b57830a77ebfaabbf6704f3cda (MD5)

A Survey on Data-Driven Evaluation of Competencies and Capabilities Across Multimedia Environments

A Survey on Data-Driven Evaluation of Competencies and Capabilities Across Multimedia Environments Strukova, Sofia; Ruipérez-Valiente, José A.; Gómez Mármol, Félix The rapid evolution of technology directly impacts the skills and jobs needed in the next decade. Users can, intentionally or unintentionally, develop different skills by creating, interacting with, and consuming the content from online environments and portals where informal learning can emerge. These environments generate large amounts of data; therefore, big data can have a significant impact on education. Moreover, the educational landscape has been shifting from a focus on contents to a focus on competencies and capabilities that will prepare our society for an unknown future during the 21st century. Therefore, the main goal of this literature survey is to examine diverse technology-mediated environments that can generate rich data sets through the users’ interaction and where data can be used to explicitly or implicitly perform a data-driven evaluation of different competencies and capabilities. We thoroughly and comprehensively surveyed the state of the art to identify and analyse digital environments, the data they are producing and the capabilities they can measure and/or develop. Our survey revealed four key multimedia environments that include sites for content sharing & consumption, video games, online learning and social networks that fulfilled our goal. Moreover, different methods were used to measure a large array of diverse capabilities such as expertise, language proficiency and soft skills. Our results prove the potential of the data from diverse digital environments to support the development of lifelong and lifewide 21st-century capabilities for the future society. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-15T09:22:47Z No. of bitstreams: 1 ip2022_10_004_0.pdf: 1157780 bytes, checksum: 5e9ac2341a6bba489dc90f7fe1b5b692 (MD5); Made available in DSpace on 2023-03-15T09:22:47Z (GMT). No. of bitstreams: 1 ip2022_10_004_0.pdf: 1157780 bytes, checksum: 5e9ac2341a6bba489dc90f7fe1b5b692 (MD5)

Modulating the Gameplay Challenge Through Simple Visual Computing Elements: A Cube Puzzle Case Study

Modulating the Gameplay Challenge Through Simple Visual Computing Elements: A Cube Puzzle Case Study Ribelles, Jose; Lopez, Angeles; Traver, V. Javier Positive player’s experiences greatly rely on a balanced gameplay where the game difficulty is related to player’s skill. Towards this goal, the gameplay can be modulated to make it easier or harder. In this work, a modulating mechanism based on visual computing is explored. The main hypothesis is that simple visual modifications of some elements in the game can have a significant impact on the game experience. This concept, which is essentially unexplored in the literature, has been experimentally tested with a web-based cube puzzle game where participants played either the original game or the visually modified game. The analysis is based on players’ behavior, performance, and replies to a questionnaire upon game completion. The results provide evidence on the effectiveness of visual computing on gameplay modulation. We believe the findings are relevant to game researchers and developers because they highlight how a core gameplay can be easily modified with relatively simple ingredients, at least for some game genres. Interestingly, the insights gained from this study also open the door to automate the game adaptation based on observed player’s interaction. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-15T09:14:27Z No. of bitstreams: 1 ip2022_05_001.pdf: 5360016 bytes, checksum: cad2037fa92328670eb0032ba3e802f1 (MD5); Made available in DSpace on 2023-03-15T09:14:27Z (GMT). No. of bitstreams: 1 ip2022_05_001.pdf: 5360016 bytes, checksum: cad2037fa92328670eb0032ba3e802f1 (MD5)

Drug Target Interaction Prediction Using Machine Learning Techniques – A Review

Drug Target Interaction Prediction Using Machine Learning Techniques – A Review Suruliandi, A.; Idhaya, T.; Raja, S. P. Drug discovery is a key process, given the rising and ubiquitous demand for medication to stay in good shape right through the course of one’s life. Drugs are small molecules that inhibit or activate the function of a protein, offering patients a host of therapeutic benefits. Drug design is the inventive process of finding new medication, based on targets or proteins. Identifying new drugs is a process that involves time and money. This is where computer-aided drug design helps cut time and costs. Drug design needs drug targets that are a protein and a drug compound, with which the interaction between a drug and a target is established. Interaction, in this context, refers to the process of discovering protein binding sites, which are protein pockets that bind with drugs. Pockets are regions on a protein macromolecule that bind to drug molecules. Researchers have been at work trying to determine new Drug Target Interactions (DTI) that predict whether or not a given drug molecule will bind to a target. Machine learning (ML) techniques help establish the interaction between drugs and their targets, using computer-aided drug design. This paper aims to explore ML techniques better for DTI prediction and boost future research. Qualitative and quantitative analyses of ML techniques show that several have been applied to predict DTIs, employing a range of classifiers. Though DTI prediction improves with negative drug target pairs (DTP), the lack of true negative DTPs has led to the use a particular dataset of drugs and targets. Using dynamic DTPs improves DTI prediction. Little attention has so far been paid to developing a new classifier for DTI classification, and there is, unquestionably, a need for better ones. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-15T08:58:46Z No. of bitstreams: 1 ip2022_11_002_0.pdf: 920478 bytes, checksum: 045180cf6b9ca6c626f93afc48d23879 (MD5); Made available in DSpace on 2023-03-15T08:58:46Z (GMT). No. of bitstreams: 1 ip2022_11_002_0.pdf: 920478 bytes, checksum: 045180cf6b9ca6c626f93afc48d23879 (MD5)

OBOE: an Explainable Text Classification Framework

OBOE: an Explainable Text Classification Framework del Águila Escobar, Raúl A.; Suárez-Figueroa, Mari Carmen; Fernández-López, Mariano Explainable Artificial Intelligence (XAI) has recently gained visibility as one of the main topics of Artificial Intelligence research due to, among others, the need to provide a meaningful justification of the reasons behind the decision of black-box algorithms. Current approaches are based on model agnostic or ad-hoc solutions and, although there are frameworks that define workflows to generate meaningful explanations, a text classification framework that provides such explanations considering the different ingredients involved in the classification process (data, model, explanations, and users) is still missing. With the intention of covering this research gap, in this paper we present a text classification framework called OBOE (explanatiOns Based On concEpts), in which such ingredients play an active role to open the black-box. OBOE defines different components whose implementation can be customized and, thus, explanations are adapted to specific contexts. We also provide a tailored implementation to show the customization capability of OBOE. Additionally, we performed (a) a validation of the implemented framework to evaluate the performance using different corpora and (b) a user-based evaluation of the explanations provided by OBOE. The latter evaluation shows that the explanations generated in natural language express the reason for the classification results in a way that is comprehensible to non-technical users. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-15T08:40:46Z No. of bitstreams: 1 ip2022_11_01.pdf: 1457451 bytes, checksum: 5fd3f5aeda8a86102d1df7f228ab1c4a (MD5); Made available in DSpace on 2023-03-15T08:40:46Z (GMT). No. of bitstreams: 1 ip2022_11_01.pdf: 1457451 bytes, checksum: 5fd3f5aeda8a86102d1df7f228ab1c4a (MD5)

Brain Tumor Classification Using a Pre-Trained Auxiliary Classifying Style-Based Generative Adversarial Network

Brain Tumor Classification Using a Pre-Trained Auxiliary Classifying Style-Based Generative Adversarial Network Kumaar, M. Akshay; Samiayya, Duraimurugan; Rajinikanth, Venkatesan; Raj Vincent P M, Durai; Kadry, Seifedine Computer Vision's applications and their use cases in the medical field have grown vastly in the past decade. The algorithms involved in these critical applications have helped doctors and surgeons perform procedures on patients more precisely with minimal side effects. However, obtaining medical data for developing large scale generalizable and intelligent algorithms is challenging in the real world as multiple socio-economic, administrative, and demographic factors impact it. Furthermore, training machine learning algorithms with a small amount of data can lead to less accuracy and performance bias, resulting in incorrect diagnosis and treatment, which can cause severe side effects or even casualties. Generative Adversarial Networks (GAN) have recently proven to be an effective data synthesis and augmentation technique for training deep learning-based image classifiers. This research proposes a novel approach that uses a Style-based Generative Adversarial Network for conditional synthesis and auxiliary classification of Brain Tumors by pre-training. The Discriminator of the pre-trained GAN is fine-tuned with extensive data augmentation techniques to improve the classification accuracy when the training data is small. The proposed method was validated with an open-source MRI dataset which consists of three types of tumors - Glioma, Meningioma, and Pituitary. The proposed system achieved 99.51% test accuracy, 99.52% precision score, and 99.50% recall score, significantly higher than other approaches. Since the framework can be made adaptive using transfer learning, this method also benefits new and small datasets of similar distributions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-14T11:08:15Z No. of bitstreams: 1 ip2023_02_008_0.pdf: 2381727 bytes, checksum: 316091571fa76e03f42a6c6be5902ef2 (MD5); Made available in DSpace on 2023-03-14T11:08:15Z (GMT). No. of bitstreams: 1 ip2023_02_008_0.pdf: 2381727 bytes, checksum: 316091571fa76e03f42a6c6be5902ef2 (MD5)

A Greedy Randomized Adaptive Search With Probabilistic Learning for solving the Uncapacitated Plant Cycle Location Problem

A Greedy Randomized Adaptive Search With Probabilistic Learning for solving the Uncapacitated Plant Cycle Location Problem López-Plata, Israel; Expósito-Izquierdo, Christopher; Lalla-Ruiz, Eduardo; Melián-Batista, Belén; Moreno-Vega, J. Marcos In this paper, we address the Uncapacitated Plant Cycle Location Problem. It is a location-routing problem aimed at determining a subset of locations to set up plants dedicated to serving customers. We propose a mathematical formulation to model the problem. The high computational burden required by the formulation when tackling large scenarios encourages us to develop a Greedy Randomized Adaptive Search Procedure with Probabilistic Learning Model. Its rationale is to divide the problem into two interconnected sub-problems. The computational results indicate the high performance of our proposal in terms of the quality of reported solutions and computational time. Specifically, we have overcome the best approach from the literature on a wide range of scenarios. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-14T10:51:43Z No. of bitstreams: 1 ip2022_04_003.pdf: 1912008 bytes, checksum: 3a6b10eb9b2c0334e124757b3c6f94bb (MD5); Made available in DSpace on 2023-03-14T10:51:43Z (GMT). No. of bitstreams: 1 ip2022_04_003.pdf: 1912008 bytes, checksum: 3a6b10eb9b2c0334e124757b3c6f94bb (MD5)

OntoInfoG++: A Knowledge Fusion Semantic Approach for Infographics Recommendation

OntoInfoG++: A Knowledge Fusion Semantic Approach for Infographics Recommendation Deepak, Gerard; Vibakar, Adithya; Santhanavijayan, A. As humans tend to improvise and learn on a constant basis, the need for visualizing and recommending knowledge is increasing. Since the World Wide Web is exploded with a lot of multimedia content and with a growing amount of research papers on the Web, there is a potential need for inferential multimedia like the infographics which can lead to an ultimate new level of learning from most viable information sources on the Web. The potential growth and future of technology have called for the need of a Web 3.0 compliant infographic recommendation system in order to be able to visualize, design and develop aesthetically. The trend of the Web has asked for better infographic recommendations in the attempt of technological exploration. This paper proposes the OntoInfoG++ which is a knowledge centric recommendation approach for Infographics that encompasses the amalgamation of metadata derived from multiple heterogeneous sources and the crowd sourced ontologies to recommend infographics based on the topic of interest of the user. The user- clicks are taken into consideration along with an Ontology which is modeled using the titles and the keywords extracted from the dataset comprising of research papers. The approach models user topic of interest from the Query Words, Current User-Clicks, and from standard Knowledge Stores like the BibSonomy, DBpedia, Wikidata, LOD Cloud, and crowd sourced Ontologies. The semantic alignment is achieved using three distinct measures namely the Horn’s index, EnAPMI measure and information entropy. The resultant infographic recommendation has been achieved by computing the semantic similarity between enriched topics of interest and infographic labels and arrange the recommended infographics in the increasing order of their semantic similarity to yield a chronological order for the meaningful arrangement of infographics. The OntoInfoG++ has achieved an overall F-measure of 97.27 % which is the best-in-class F-measure for an infographic recommendation system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-14T10:34:38Z No. of bitstreams: 1 ip2021_12_005.pdf: 1688814 bytes, checksum: 798e8a63663c6db51e90b023e2be4df0 (MD5); Made available in DSpace on 2023-03-14T10:34:38Z (GMT). No. of bitstreams: 1 ip2021_12_005.pdf: 1688814 bytes, checksum: 798e8a63663c6db51e90b023e2be4df0 (MD5)

Attentive Flexible Translation Embedding in Top-N Sparse Sequential Recommendations

Attentive Flexible Translation Embedding in Top-N Sparse Sequential Recommendations Seo, Min-Ji; Kim, Myung-Ho Sequential recommendation aims to predict the user’s next action based on personal action sequences. The major challenge in this task is how to achieve high performance recommendation under the data sparsity problem. Translation-based recommendations, which learn distance metrics to capture interactions between users and items in sequential recommendations, are a promising method to overcome this issue. However, a disadvantage of translation-based recommendations is that they capture long-term preferences of the user and complex item transitions. In this paper, we propose attentive flexible translation for recommendations (AFTRec) to tackle data sparsity problem by capturing a user’s dynamic preferences and complex interactions between items in user’s purchasing behaviors. In particular, we first encode semantic information of an item related to user’s purchasing behaviors as the user-specific item translation vectors. We also design a transition graph and encode complex item transitions as correlation-specific item translation vectors. Finally, we adopt a flexible distance metric that considers directions with respect to the translation vectors in the same space for predicting the next item. To evaluate the performance of our method, we conducted experiments on four sparse datasets and one dense dataset with different domains. The experimental results demonstrate that our proposed AFTRec outperforms the state-of-the-art baselines in terms of normalized discounted cumulative gain and hit rate on sparse datasets. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-14T10:28:46Z No. of bitstreams: 1 ip2022_10_007.pdf: 2429918 bytes, checksum: 4407cce040a1d5838b26a588739dd81c (MD5); Made available in DSpace on 2023-03-14T10:28:46Z (GMT). No. of bitstreams: 1 ip2022_10_007.pdf: 2429918 bytes, checksum: 4407cce040a1d5838b26a588739dd81c (MD5)

An Empirical Evaluation of Machine Learning Techniques for Crop Prediction

An Empirical Evaluation of Machine Learning Techniques for Crop Prediction Mariammal, G.; Suruliandi, A.; Raja, S. P.; Poongothai, E. Agriculture is the primary source driving the economic growth of every country worldwide. Crop prediction, which is critical to agriculture, depends on the soil and environment. Nutrient levels differ from area to area and greatly influence in crop cultivation. Earlier, the tasks of crop forecast and cultivation were undertaken by farmers themselves. Today, however, crop prediction is determined by climatic variations. This is where machine learning algorithms step in to identify the most relevant crop for cultivation. This research undertakes an empirical analysis using the bagging, random forest, support vector machine, decision tree, Naïve Bayes and k-nearest neighbor classifiers to predict the most appropriate cultivable crop for certain areas, based on environment and soil traits. Further, the suitability of the classifiers is examined using a GitHub prisoners’ dataset. The experimental results of all the classification techniques were assessed to show that the ensemble outclassed the rest with respect to every performance metric. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-14T10:18:38Z No. of bitstreams: 1 ip2022_12_004.pdf: 829089 bytes, checksum: 1a22e16ffed40d297c55796f0fecf0b9 (MD5); Made available in DSpace on 2023-03-14T10:18:38Z (GMT). No. of bitstreams: 1 ip2022_12_004.pdf: 829089 bytes, checksum: 1a22e16ffed40d297c55796f0fecf0b9 (MD5)

A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm

A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm Qasim Awla, Hoshang; Wahhab Kareem, Shahab; Salih Mohammed, Amin Bayesian networks are analytical models that may represent probabilistic dependent connections among variables and are useful in machine learning for generating knowledge structure. Due to the vastness of the solution space, learning Bayesian network (BN) structures from data is an NP-hard problem. The score and search technique is one Bayesian Network structure learning strategy. In Bayesian network structure learning the Falcon Optimization Algorithm (FOA) is presented and evaluated by the authors. Inserting, Reversing, Moving, and Deleting, are used in the method to create the FOA for finding the best structural solution. The FOA algorithm is based on the falcon's searching technique during drought conditions. The suggested technique is compared to the score metric function of Pigeon Inspired search algorithm, Greedy Search, and Antlion optimization search algorithm. The performance of these techniques in terms of confusion matrices was further evaluated by the authors using a variety of benchmark data sets. The Falcon optimization algorithm outperforms the previous algorithms and generates higher scores and accuracy values, as evidenced by the results of our experiments. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-14T10:05:41Z No. of bitstreams: 1 ip2023_01_004.pdf: 1954539 bytes, checksum: a8f6a836f3c25d18979bf2fa465f2a14 (MD5); Made available in DSpace on 2023-03-14T10:05:41Z (GMT). No. of bitstreams: 1 ip2023_01_004.pdf: 1954539 bytes, checksum: a8f6a836f3c25d18979bf2fa465f2a14 (MD5)

Tourism-Related Placeness Feature Extraction from Social Media Data Using Machine Learning Models

Tourism-Related Placeness Feature Extraction from Social Media Data Using Machine Learning Models Muñoz, Pedro; Doñaque, E.; Larrañaga, A.; Martínez Torres, Javier The study of placeness has been focus for researchers trying to understand the impact of locations on their surroundings and tourism, the loss of it by globalization and modernization and its effect on tourism, or the characterization of the activities that take place in them. Identifying places that have a high level of placeness can become very valuable when studying social trends and mobility in relation to the space in which the study takes place. Moreover, places can be enriched with dimensions such as the demographics of the individuals visiting such places and the activities the carry in them thanks to social media and modern machine learning and data mining methods. Such information can prove to be useful in fields such as urban planning or tourism as a base for analysis and decision-making or the discovery of new social hotspots or sites rich in cultural heritage. This manuscript will focus on the methodology to obtain such information, for which data from Instagram is used to feed a set of classification models that will mine demographics from the users based on graphic and textual data from their profiles, gain insight on what they were doing in each of their posts and try to classify that information into any of the categories discovered in this article. The goal of this methodology is to obtain, from social media data, characteristics of visitors to locations as a discovery tool for the tourism industry. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-14T09:46:13Z No. of bitstreams: 1 ip2022_12_003.pdf: 1149017 bytes, checksum: f6c14a3431fb1cf416990f4d8443e4bb (MD5); Made available in DSpace on 2023-03-14T09:46:13Z (GMT). No. of bitstreams: 1 ip2022_12_003.pdf: 1149017 bytes, checksum: f6c14a3431fb1cf416990f4d8443e4bb (MD5)

Deep Transfer Learning-Based Automated Identification of Bird Song

Deep Transfer Learning-Based Automated Identification of Bird Song Das, Nabanita; Padhy, Neelamadhab; Dey, Nilanjan; Bhattacharya, Sudipta; Tavares, Joao Manuel R. S. Bird species identification is becoming increasingly crucial for avian biodiversity conservation and assisting ornithologists in quantifying the presence of birds in a given area. Convolutional Neural Networks (CNNs) are advanced deep learning algorithms that have proven to perform well in speech classification. However, developing an accurate deep learning classifier requires a large amount of data. Such a large amount of data on endemic or endangered creatures is frequently difficult to gathered. Also, in some other fields, such as bioinformatics and robotics, the high cost of data collection and expensive annotation limit their progress, so large, well-annotated data creating a set is also difficult. A transfer learning method can alleviate overfitting concerns in a deep learning model. This feature serves as the inspiration for transfer learning, which was created to deal with situations where the data are distributed across a variety of functional domains. In this study, the ability of deep transfer models such as VGG16, VGG19 and InceptionV3 to effectively extract and discriminate speech signals from different species of birds with high prediction accuracy is explored. The obtained accuracies using VGG16, VGG19 and InceptionV3 were equal to 78, 61.9 and 85%, respectively, which are very promising. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-14T09:30:41Z No. of bitstreams: 1 ip2023_01_003_1.pdf: 3796248 bytes, checksum: 3384f7e7c2b7a69190885f76c549e697 (MD5); Made available in DSpace on 2023-03-14T09:30:41Z (GMT). No. of bitstreams: 1 ip2023_01_003_1.pdf: 3796248 bytes, checksum: 3384f7e7c2b7a69190885f76c549e697 (MD5)

Resource and Process Management With a Decision Model Based on Fuzzy Logic

Resource and Process Management With a Decision Model Based on Fuzzy Logic Fornerón Martínez, J. T.; Agostini, F.; la Red, David L. The allocation of the resources to be shared in the context of a distributed processing system needs to be coordinated through the mutual exclusion mechanism, which will decide the order in which the shared resources will be allocated to those processes that require them. This paper proposes an aggregation operator, which can be used by a module that manages the shared resources, whose function is to assign the resources to the processes according to their requirements (shared resources) and the status of the distributed nodes in which the processes operate (computational load), by using 2-tuple associated to linguistic labels. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-14T09:17:36Z No. of bitstreams: 1 ip2023_02_009.pdf: 2277350 bytes, checksum: aca528a795efb6cf7d0b6eff1a46ce6d (MD5); Made available in DSpace on 2023-03-14T09:17:36Z (GMT). No. of bitstreams: 1 ip2023_02_009.pdf: 2277350 bytes, checksum: aca528a795efb6cf7d0b6eff1a46ce6d (MD5)

An Efficient Fake News Detection System Using Contextualized Embeddings and Recurrent Neural Network

An Efficient Fake News Detection System Using Contextualized Embeddings and Recurrent Neural Network Ali Reshi, Junaid; Ali, Rashid Fake news is detrimental for society and individuals. Since the information dissipation through online media is too quick, an efficient system is needed to detect and counter the propagation of fake news on social media. Many studies have been performed in last few years to detect fake news on social media. This study focusses on the efficient detection of fake news on social media, through a Natural Language Processing based approach, using deep learning. For the detection of fake news, textual data have been analyzed in unidirectional way using sequential neural networks, or in bi-directional way using transformer architectures like Bidirectional Encoder Representations from Transformers (BERT). This paper proposes ConFaDe - a deep learning based fake news detection system that utilizes contextual embeddings generated from a transformer-based model. The model uses Masked Language Modelling and Replaced Token Detection in its pre-training to capture contextual and semantic information in the text. The proposed system outperforms the previously set benchmarks for fake news detection; including state-of-the-art approaches on a real-world fake news dataset, when evaluated using a set of standard performance metrics with an accuracy of 99.9 % and F1 macro of 99.9%. In contrast to the existing state-of-the-art model, the proposed system uses 90 percent less network parameters and is 75 percent lesser in size. Consequently, ConFaDe requires fewer hardware resources and less training time, and yet outperforms the existing fake news detection techniques, a step forward in the direction of Green Artificial Intelligence. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-13T12:06:50Z No. of bitstreams: 1 ip2023_02_007_1.pdf: 1828984 bytes, checksum: 10970f3c192f73e3f2f4b1b0f7068095 (MD5); Made available in DSpace on 2023-03-13T12:06:50Z (GMT). No. of bitstreams: 1 ip2023_02_007_1.pdf: 1828984 bytes, checksum: 10970f3c192f73e3f2f4b1b0f7068095 (MD5)

Quantitative Measures for Medical Fundus and Mammography Images Enhancement

Quantitative Measures for Medical Fundus and Mammography Images Enhancement Intriago-Pazmiño, Monserrate; Ibarra-Fiallo, Julio; Guzmán-Castillo, Adán; Alonso-Calvo, Raúl; Crespo, José Enhancing the visibility of medical images is part of the initial or preprocessing phase within a computer vision system. This image preparation is essential for subsequent system tasks such as segmentation or classification. Therefore, quantitative validation of medical image preprocessing is crucial. In this work, four metrics are studied: Contrast Improvement Index (CII), Enhancement Measurement Estimation (EME), Entropy EME (EMEE), and Entropy. The objective is to find the best parameters for each metric. The study is performed on five medical image datasets, three retinal fundus sets (DRIVE, ROPFI, HRF-POORQ), and two mammography image sets (MIAS, DDSM). Metrics are calculated using a binary mask image to discard the background. Using the fundus and mask datasets, the best results were obtained with the EMEE and EMEE metrics, which achieved mean improvements of up to 186% and 75%, respectively. For mammography datasets and using masks of the region of interest, the two metrics with the highest percentage improvement were CII and EMEE, which obtained means of up to 396% and 129%, respectively. Based on the experimental results provided, we can conclude that EMEE, EME, and CII metrics can achieve better enhancement assessment in this type of medical imaging. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-13T11:45:48Z No. of bitstreams: 1 ip2022_12_002_0.pdf: 5667122 bytes, checksum: f9c96ad1df94e8ae5b1a983bd37fe2c1 (MD5); Made available in DSpace on 2023-03-13T11:45:48Z (GMT). No. of bitstreams: 1 ip2022_12_002_0.pdf: 5667122 bytes, checksum: f9c96ad1df94e8ae5b1a983bd37fe2c1 (MD5)

Synthetic Aperture Radar Automatic Target Recognition Based on a Simple Attention Mechanism

Synthetic Aperture Radar Automatic Target Recognition Based on a Simple Attention Mechanism Ukwuoma, Chiagoziem Chima; Zhiguang, Qin; Tienin, Bole W.; Yussif, Sophyani B.; Ejiyi, Chukwuebuka Joseph; Urama, Gilbert C.; Ukwuoma, Chibueze D.; Chikwendu, Ijeoma Amuche A simple but effective channel attention module is proposed for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). The channel attention technique has shown recent success in improving Deep Convolutional Neural Networks (CNN). The resolution of SAR images does not surpass optical images thus information flow of SAR images becomes relatively poor when the network depth is raised blindly leading to a serious gradients explosion/vanishing. To resolve the issue of SAR image recognition efficiency and ambiguity trade-off, we proposed a simple Channel Attention module into the ResNet Architecture as our network backbone, which utilizes few parameters yet results in a performance gain. Our simple attention module, which follows the implementation of Efficient Channel Attention, shows that avoiding dimensionality reduction is essential for learning as well as an appropriate cross-channel interaction can preserve performance and decrease model complexity. We also explored the One Policy Learning Rate on the ResNet-50 architecture and compared it with the proposed attention based ResNet-50 architecture. A thorough analysis of the MSTAR Dataset demonstrates the efficacy of the suggested strategy over the most recent findings. With the Attention-based model and the One Policy Learning Rate-based architecture, we were able to obtain recognition rate of 100% and 99.8%, respectively. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-13T11:10:00Z No. of bitstreams: 1 ip2023_02_004.pdf: 4381897 bytes, checksum: 240caf320d19eb5d4de43c7dd61fb2ea (MD5); Made available in DSpace on 2023-03-13T11:10:00Z (GMT). No. of bitstreams: 1 ip2023_02_004.pdf: 4381897 bytes, checksum: 240caf320d19eb5d4de43c7dd61fb2ea (MD5)

Emotion-Aware Monitoring of Users’ Reaction With a Multi-Perspective Analysis of Long- and Short-Term Topics on Twitter

Emotion-Aware Monitoring of Users’ Reaction With a Multi-Perspective Analysis of Long- and Short-Term Topics on Twitter Cavaliere, Danilo; Fenza, Giuseppe; Loia, Vincenzo; Nota, Francesco Social networks, such as Twitter, play like a disinformation spread booster giving the chance to individuals and organizations to influence users’ beliefs on purpose through tweets causing destabilization effects to the community. As a consequence, there is a need for solutions to analyse users’ reactions to topics debated in the community. To this purpose, state-of-the-art methods focus on selecting the most debated topics over time, ignoring less-frequent-discussed topics. In this paper, a framework for users’ reaction and topic analysis is introduced. First the method extracts topics as frequent itemsets of named entities from tweets collected, hence the support over time and RoBERTa-based sentiment analysis are applied to assess the current topic spread and the emotional impact, then a time-grid-based approach allows a granule-level analysis of the collected features that can be exploited for predicting future users’ reactions towards topics. Finally, a three-perspective score function is introduced to build comparative ranked lists of the most relevant topics according to topic sentiment, importance and spread. Experiences demonstrate the potential of the framework on IEEE COVID-19 Tweets Dataset. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-13T11:00:19Z No. of bitstreams: 1 ip2023_02_003.pdf: 1790012 bytes, checksum: 313fafa79d29a1bd740e0945384dcf6e (MD5); Made available in DSpace on 2023-03-13T11:00:19Z (GMT). No. of bitstreams: 1 ip2023_02_003.pdf: 1790012 bytes, checksum: 313fafa79d29a1bd740e0945384dcf6e (MD5)

Real World Anomalous Scene Detection and Classification using Multilayer Deep Neural Networks

Real World Anomalous Scene Detection and Classification using Multilayer Deep Neural Networks Jan, Atif; Khan, Gul Muhammad Surveillance videos record malicious events in a locality utilizing various machine learning algorithms for detection. Deep-learning algorithms being the most prominent AI algorithms are data-hungry as well as computationally expensive. These algorithms perform better when trained over a diverse and huge set of examples. These modern AI methods have a dire need of utilizing human intelligence to pamper the problem in such a way as to reduce the ultimate effort in terms of computational cost. In this research work, a novel methodology termed Bag of Focus (BoF) based training methodology has been proposed. BoF is based on the concept of selecting motion-intensive blocks in a long video, for training different deep neural networks (DNN's). The methodology reduced the computational overhead by 90% (ten times) in comparison to when full-length videos are entertained. It has been observed that training networks using BoF are equally effective in terms of performance for the same network trained over the full-length dataset. In this research work, firstly, a fine-grained annotated dataset including instance and activity information has been developed for real-world volume crimes. Secondly, a BoF-based methodology has been introduced for effective training of the state-of-the-art 3D, and 2D Convolutional Neural Networks (CNNs). Lastly, a comparison between the state-of-the-art networks have been presented for malicious event recognition in videos. It has been observed that 2D CNN even with lesser parameters achieved a promising classification accuracy of 98.7% and Area under the curve (AUC) of 99.7%. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-13T10:46:39Z No. of bitstreams: 1 ip2021_10_010_0.pdf: 3198406 bytes, checksum: 0f55f0dc5ce2de966e69cb4804ae19b1 (MD5); Made available in DSpace on 2023-03-13T10:46:39Z (GMT). No. of bitstreams: 1 ip2021_10_010_0.pdf: 3198406 bytes, checksum: 0f55f0dc5ce2de966e69cb4804ae19b1 (MD5)

A Hybrid Parallel Classification Model for the Diagnosis of Chronic Kidney Disease

A Hybrid Parallel Classification Model for the Diagnosis of Chronic Kidney Disease Singh, Vijendra; Jain, Divya Chronic Kidney Disease (CKD) has become a prevalent disease nowadays, affecting people globally around the world. Accurate prediction of CKD progression over time is essential for reducing its associated mortality and morbidity rates. This paper proposes a fast, novel hybrid approach to diagnose Chronic Renal Disease. The proposed approach is based on the optimization of SVM classifier with the hybridized dimensionality reduction approach to identify the most informative parameters for CKD diagnosis. It handles the selection of features through two steps. The first one is a filter-based approach using ReliefF method to assign weights and ranks to each feature of the dataset. The second step is the dimensionality reduction of the best-selected subset by means of PCA, a feature extraction technique. For faster execution of datasets, simultaneous execution on multiple processors is employed. The proposed model achieved the highest prediction accuracy of 92.5% on the clinical CKD dataset compared to existing methods - ‘CFS+SVM’ (60.45%), ‘ReliefF + SVM’ (86%), ‘MIFS + SVM’ (56.72%), ‘ReliefF + CFS + SVM’ (54.37). The proposed work is also examined on the benchmarked Chronic Kidney Disease Dataset and achieved classification accuracy of 98.5% compared to the accuracy with other methods -‘CFS+SVM’ (92.7%), ‘ReliefF + SVM’ (89.6%), ‘MIFS + SVM’ (94.7%). The experimental outcomes positively demonstrate that the proposed hybridized model is effective in undertaking medical data classification tasks and is, therefore, a promising tool for the diagnosis of CKD patients. The proposed approach is statistically validated with the Friedman test with significant results compared to other techniques. The proposed approach also executes in the least time with improved prediction accuracy and competes with and even outperforms other methods in the literature. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-13T10:30:50Z No. of bitstreams: 1 ip2021_10_008.pdf: 1663611 bytes, checksum: 010c00dc7292ae899ce2d0844508a619 (MD5); Made available in DSpace on 2023-03-13T10:30:50Z (GMT). No. of bitstreams: 1 ip2021_10_008.pdf: 1663611 bytes, checksum: 010c00dc7292ae899ce2d0844508a619 (MD5)

RGBeat: A Recoloring Algorithm for Deutan and Protan Dichromats

RGBeat: A Recoloring Algorithm for Deutan and Protan Dichromats Ribeiro, Madalena; Gomes, Abel Deutan and protan dichromats only see exactly two hues in the HSV color space, 240-blue (240o) and 60-yellow (60 o). Consequently, they see both reds and greens as yellows; therefore, they cannot distinguish reds from greens very well. Thus, their color space is 2D and results from the intersection between the HSV color cone and the 60º-240º plane. The RGBeat recoloring algorithm’s main contribution here is that it is the first recoloring algorithm that enhances the color perception of deutan and protan dichromats but without compromising the lifelong color perceptual learning. Also, as far as we know, this is the first HTML5-compliant web recoloring approach for dichromat people that considers both text and image recoloring in an integrated manner. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-13T10:13:54Z No. of bitstreams: 1 ip2022_01_003_0.pdf: 14595713 bytes, checksum: 66c1760f71dd542e2c028df01beab30b (MD5); Made available in DSpace on 2023-03-13T10:13:54Z (GMT). No. of bitstreams: 1 ip2022_01_003_0.pdf: 14595713 bytes, checksum: 66c1760f71dd542e2c028df01beab30b (MD5)

RIADA: A Machine-Learning Based Infrastructure for Recognising the Emotions of Spotify Songs

RIADA: A Machine-Learning Based Infrastructure for Recognising the Emotions of Spotify Songs Álvarez, P.; García de Quirós, J.; Baldassarri, S. The music emotions can help to improve the personalization of services and contents offered by music streaming providers. Many research works based on the use of machine learning techniques have addressed the problem of recognising the music emotions during the last years. Nevertheless, the results obtained are only applied on small-size music repositories and do not consider what the users feel when they listen to the songs. These issues prevent the existing proposals to be integrated into the personalization mechanisms of the online music providers. In this paper, we present the RIADA infrastructure which is composed by a set of systems able to annotate emotionally the catalog of songs offered by Spotify based on the users’ perception. RIADA works with the Spotify playlist miner and data services to build emotion recognition models that can solve the open challenges previously mentioned. Machine learning algorithms, music information retrieval techniques, architectures for parallelization of applications and cloud computing have been combined to develop a complex result of engineering able to integrate the music emotions into the Spotify-based applications. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-10T15:13:55Z No. of bitstreams: 1 ip2022_04_02.pdf: 1334801 bytes, checksum: 5657c9b5a9277b31891d505065b116b1 (MD5); Made available in DSpace on 2023-03-10T15:13:55Z (GMT). No. of bitstreams: 1 ip2022_04_02.pdf: 1334801 bytes, checksum: 5657c9b5a9277b31891d505065b116b1 (MD5)

Cosine Similarity Based Hierarchical Skeleton and Cross Indexing for Large Scale Image Retrieval Using Mapreduce Framework

Cosine Similarity Based Hierarchical Skeleton and Cross Indexing for Large Scale Image Retrieval Using Mapreduce Framework Qianwen, Zhong The imaging data in various fields like industries, institutions, medical, and so on has grown exponentially in recent years. An innovative software solution is required for the efficient management of image data. The MapReduce framework is used for large-scale image data processing. Various cross-indexing techniques are developed to transform the image into binary sequences but retrieving the image from the reducer on the feature vector results in a major challenge. Image retrieval using large-scale image databases attained major attention, where cross-indexing plays a key role in the research community. Therefore, in this research, a new method for image retrieval, named Cosine Similarity-based hierarchical skeleton and cross-indexing, is proposed to perform the retrieval process in the MapReduce framework effectively. The feature vector of the images is converted to binary sequences. The Most Significant Bit (MSB) of the binary code is used to store the images in the mapper using the cross-indexing model. The image retrieval process is achieved through the reducer based on the tanimoto similarity measure. The binary sequence for the query image is calculated based on the feature vector. The MSB bit of the binary code is matched with the MSB code of the images in the mapper to achieve the retrieval process. The proposed method effectively achieved better performance through the cross-indexing model with the usage of the feature vector. The performance of the proposed method is compared with the existing techniques using the UK bench dataset. The proposed method attains the values of 0.784, 0.729, 0.75, 31.23, 17.84secfor F1-score, precision, recall, computational cost, and computational time with the query set-1 by considering four mappers. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-10T15:08:19Z No. of bitstreams: 1 ip2023_01_008.pdf: 2790409 bytes, checksum: bb52e6d3cb166c7d1e3675ea8a327baa (MD5); Made available in DSpace on 2023-03-10T15:08:19Z (GMT). No. of bitstreams: 1 ip2023_01_008.pdf: 2790409 bytes, checksum: bb52e6d3cb166c7d1e3675ea8a327baa (MD5)

Multi-Agent and Fuzzy Inference-Based Framework for Traffic Light Optimization

Multi-Agent and Fuzzy Inference-Based Framework for Traffic Light Optimization Ikidid, Abdelouafi; Abdelaziz, El Fazziki; Sadgal, Mohammed Despite the fact that agent technologies have widely gained popularity in distributed systems, their potential for advanced management of vehicle traffic has not been sufficiently explored. This paper presents a traffic simulation framework based on agent technology and fuzzy logic. The objective of this framework is to act on the phase layouts represented by its sequences and length to maximize throughput and fluidize traffic at an isolated intersection and for the whole multi-intersection network, through both inter- and intra-intersection collaboration and coordination. The optimizing of signal layouts is done in real time, and it is not only based on local stream factors but also on traffic stream conditions in surrounding intersections. The system profits from agent communication and collaboration as well as coordination features, along with decentralized organization, to decompose the traffic control optimization into subproblems and enable the distributed resolution. Thus, the separate parts can be resolved rapidly by parallel tasking. It also uses fuzzy technology to handle the uncertainty of traffic conditions. An instance of the proposed framework was validated and designed in the ANYLOGIC simulator. Instantiation results and analysis denote that the designed system can significantly develop the efficiency at an individual intersection as well as in the multi-intersection network. It reduces the average travel delay and the time spent in the network compared to multi-agent-based adaptative signal control systems. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-10T15:01:38Z No. of bitstreams: 1 ip2021_12_002.pdf: 1280024 bytes, checksum: af272fdf10087c5ff553bc37f4787e81 (MD5); Made available in DSpace on 2023-03-10T15:01:38Z (GMT). No. of bitstreams: 1 ip2021_12_002.pdf: 1280024 bytes, checksum: af272fdf10087c5ff553bc37f4787e81 (MD5)

Deep Learning Assisted Medical Insurance Data Analytics With Multimedia System

Deep Learning Assisted Medical Insurance Data Analytics With Multimedia System Zhang, Cheng; Vinodhini, B.; Muthu, Bala Anand Big Data presents considerable challenges to deep learning for transforming complex, high-dimensional, and heterogeneous biomedical data into health care data. Various kinds of data are analyzed in recent biomedical research that includes e-health records, medical imaging, text, and IoT sensor data, which are complex, badly labeled, heterogeneous, and usually unstructured. Conventional statistical learning and data mining methods usually require first to extract features to acquire more robust and effective variables from those data. These features help build clustering or prediction models. New useful paradigms are provided by the latest advancements based on deep learning technologies for obtaining end-to-end learning techniques from complex data. The abstractions of data are represented using the multiple layers of deep learning for building computational models. Clinician performance is augmented by the prospective of deep learning models in medical imaging interpretation, and automated segmentation is used to reduce the time for the diagnosis. This work presents a convolution neural network-based deep learning infrastructure that performs medical imaging data analysis in various pipeline stages, including data-loading, data-augmentation, network architectures, loss functions, and evaluation metrics. Our proposed deep learning approach supports both 2D as well as 3D medical image analysis. We evaluate the proposed system's performance using metrics like sensitivity, specificity, accuracy, and precision over the clinical data with and without augmentation. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-10T13:56:53Z No. of bitstreams: 1 ip2023_01_009.pdf: 5077977 bytes, checksum: 92b7117bfbea0e044a01e228b9a51ae4 (MD5); Made available in DSpace on 2023-03-10T13:56:53Z (GMT). No. of bitstreams: 1 ip2023_01_009.pdf: 5077977 bytes, checksum: 92b7117bfbea0e044a01e228b9a51ae4 (MD5)

HDDSS: An Enhanced Heart Disease Decision Support System using RFE-ABGNB Algorithm

HDDSS: An Enhanced Heart Disease Decision Support System using RFE-ABGNB Algorithm Dhilsath Fathima, M.; Justin Samuel, S.; Raja, S. P. Heart disease is the leading cause of mortality globally. Heart disease refers to a range of disorders that affect the heart and blood vessels. The risks of developing heart disease become minimized if heart disease is detected early. Previous studies have suggested many heart disease decision-support systems based on machine learning (ML) algorithms. However, the lower prediction accuracy is the main issue in these heart disease decisionsupport systems. The proposed work developed a heart disease decision-support system (HDDSS) that can predict whether or not a person has heart disease. The main goal of this research work is to use the RFEABGNB to improve HDDSS prediction accuracy. The Cleveland heart disease dataset is used for training and validating the proposed HDDSS. The two significant stages of HDDSS are the feature election stage and the classification modeling stage. The recursive feature elimination (RFE) technique is used in the first stage of HDDSS to select the relevant features of the heart disease dataset. In the second stage of HDDSS, the proposed Adaptive boosted Gaussian Naïve Bayes (ABGNB) algorithm has been used to construct a classification model for training and validating a heart disease decision-support system. An output of HDDSS is analyzed using various classification output measures. According to the results obtained, our proposed method attained a predictive performance of 92.87 percent. This HDDSS model would perform well when compared to other heart disease decision-support systems found in the literature. According to our experimental analysis, the RFE-ABGNB focused heart disease decision-support system is more appropriate for a heart disease prediction. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-10T13:47:13Z No. of bitstreams: 1 ip2021_10_003.pdf: 1485176 bytes, checksum: 6b7f0f1040d71b4c29b19d0c283d2772 (MD5); Made available in DSpace on 2023-03-10T13:47:13Z (GMT). No. of bitstreams: 1 ip2021_10_003.pdf: 1485176 bytes, checksum: 6b7f0f1040d71b4c29b19d0c283d2772 (MD5)

Results of a Study to Improve the Spanish Version of the User Experience Questionnaire (UEQ)

Results of a Study to Improve the Spanish Version of the User Experience Questionnaire (UEQ) Hernández-Campos, Mónica; Thomaschewski, Jörg; Law, Yuen C. This paper analyses changes in some items of the User Experience Questionnaire (UEQ) for use in the context of Costa Rican culture. Although a Spanish version of the UEQ was created in 2012, we use a double-translation and reconciliation model for detecting the more appropriate words for Costa Rican culture. These resulted in 7 new items that were added to the original Spanish version. In total, the resulting UEQ had 33 items. 161 participants took part in a study that examined both the original items and the new ones. Static analyses (Cronbach's Alpha, mean, variance, and confidence interval) were performed to measure the differences of the scales of the original items and the new UEQ variant with the Costa Rican words. Finally, confidence intervals of the individual items and Cronbach’s Alpha coefficient average of the affected scales were analysed. The results show, contrary to initial expectations, that the Costa Rican word version is neither better nor worse than the original Spanish version. However, this shows that the UEQ is very robust to some changes in the items. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-10T13:35:35Z No. of bitstreams: 1 ip2022_11_003.pdf: 514919 bytes, checksum: 2bc2e2adaafe10ad962fdde1426f1df3 (MD5); Made available in DSpace on 2023-03-10T13:35:35Z (GMT). No. of bitstreams: 1 ip2022_11_003.pdf: 514919 bytes, checksum: 2bc2e2adaafe10ad962fdde1426f1df3 (MD5)

Local Model-Agnostic Explanations for Black-box Recommender Systems Using Interaction Graphs and Link Prediction Techniques

Local Model-Agnostic Explanations for Black-box Recommender Systems Using Interaction Graphs and Link Prediction Techniques Caro-Martínez, Marta; Jiménez-Díaz, Guillermo; Recio-García, Juan A. Explanations in recommender systems are a requirement to improve users’ trust and experience. Traditionally, explanations in recommender systems are derived from their internal data regarding ratings, item features, and user profiles. However, this information is not available in black-box recommender systems that lack sufficient data transparency. This current work proposes a local model-agnostic, explanation-by-example method for recommender systems based on knowledge graphs to leverage this knowledge requirement. It only requires information about the interactions between users and items. Through the proper transformation of these knowledge graphs into item-based and user-based structures, link prediction techniques are applied to find similarities between the nodes and to identify explanatory items for the user’s recommendation. Experimental evaluation demonstrates that these knowledge graphs are more effective than classical content-based explanation approaches but have lower information requirements, making them more suitable for black-box recommender systems. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-10T13:25:30Z No. of bitstreams: 1 ip2021_12_001_0.pdf: 1615281 bytes, checksum: f98ee39ee64a909c80a9c3160689213e (MD5); Made available in DSpace on 2023-03-10T13:25:30Z (GMT). No. of bitstreams: 1 ip2021_12_001_0.pdf: 1615281 bytes, checksum: f98ee39ee64a909c80a9c3160689213e (MD5)

A Platform for Swimming Pool Detection and Legal Verification Using a Multi-Agent System and Remote Image Sensing

A Platform for Swimming Pool Detection and Legal Verification Using a Multi-Agent System and Remote Image Sensing Sánchez San Blas, Héctor; Carmona Balea, Antía; Sales, A.; Augusto Silva, Luís; Villarrubia González, Gabriel Spain is the second country in Europe with the most swimming pools. However, the legal literature estimates that 20% of swimming pools are not declared or irregular.The administration has a corps of people who manually analyze satellite or drone images to detect illegal or irregular structures. This method is costly in terms of effort and time, and it is also a method based on the subjectivity of the person carrying it out. This proposal aims to design a platform that allows the automatic detection of irregular pools. Using geographic information tools (GIS) based on orthophotography, combined with advanced machine learning techniques for object detection, allows this work. Furthermore, using a multi-agent architecture allows the system to be modular, with the possibility of the different parts of the system working together, balancing the workload. The proposed system has been validated by testing it in different towns in Spain. The system has shown promisin results in performing this task, with an F1-Score of 97.1%. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-09T16:11:46Z No. of bitstreams: 1 ip2023_01_002.pdf: 15157584 bytes, checksum: a865b94d7d0ba34b25ee70e7ae98f5c5 (MD5); Made available in DSpace on 2023-03-09T16:11:46Z (GMT). No. of bitstreams: 1 ip2023_01_002.pdf: 15157584 bytes, checksum: a865b94d7d0ba34b25ee70e7ae98f5c5 (MD5)

KoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals

KoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals García-Peñalvo, Francisco; Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; Sampedro-Gómez, Jesús; Sánchez-Puente, Antonio; Vicente-Palacios, Víctor; Dorado-Díaz, P. Ignacio; Sánchez, Pedro L. Machine Learning (ML) has extended its use in several domains to support complex analyses of data. The medical field, in which significant quantities of data are continuously generated, is one of the domains that can benefit from the application of ML pipelines to solve specific problems such as diagnosis, classification, disease detection, segmentation, assessment of organ functions, etc. However, while health professionals are experts in their domain, they can lack programming and theoretical skills regarding ML applications. Therefore, it is necessary to train health professionals in using these paradigms to get the most out of the application of ML algorithms to their data. In this work, we present a platform to assist non-expert users in defining ML pipelines in the health domain. The system’s design focuses on providing an educational experience to understand how ML algorithms work and how to interpret their outcomes and on fostering a flexible architecture to allow the evolution of the available components, algorithms, and heuristics. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-09T15:55:31Z No. of bitstreams: 1 ip2023_01_006.pdf: 1776887 bytes, checksum: 6df5ba28ca606edf3547654db4c11ff0 (MD5); Made available in DSpace on 2023-03-09T15:55:31Z (GMT). No. of bitstreams: 1 ip2023_01_006.pdf: 1776887 bytes, checksum: 6df5ba28ca606edf3547654db4c11ff0 (MD5)

A Feature Selection Approach Based on Archimedes’ Optimization Algorithm for Optimal Data Classification

A Feature Selection Approach Based on Archimedes’ Optimization Algorithm for Optimal Data Classification Khrissi, Lahbib; El Akkad, Nabil; Satori, Hassan; Satori, Khalid Feature selection is an active research area in data mining and machine learning, especially with the increase in the amount of numerical data. FS is a search strategy to find the best subset of features among a large number of subsets of features. Thus, FS is applied in most modern applications and in various domains, which requires the search for a powerful FS technique to process and classify high-dimensional data. In this paper, we propose a new technique for dimension reduction in feature selection. This approach is based on a recent metaheuristic called Archimedes’ Optimization Algorithm (AOA) to select an optimal subset of features to improve the classification accuracy. The idea of the AOA is based on the steps of Archimedes' principle in physics. It explains the behavior of the force exerted when an object is partially or fully immersed in a fluid. AOA optimization maintains a balance between exploration and exploitation, keeping a population of solutions and studying a large area to find the best overall solution. In this study, AOA is exploited as a search technique to find an optimal feature subset that reduces the number of features to maximize classification accuracy. The K-nearest neighbor (K-NN) classifier was used to evaluate the classification performance of selected feature subsets. To demonstrate the superiority of the proposed method, 16 benchmark datasets from the UCI repository are used and also compared by well-known and recently introduced meta-heuristics in this context, such as: sine-cosine algorithm (SCA), whale optimization algorithm (WOA), butterfly optimization algorithm (BAO), and butterfly flame optimization algorithm (MFO). The results prove the effectiveness of the proposed algorithm over the other algorithms based on several performance measures used in this paper. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-09T15:45:11Z No. of bitstreams: 1 ip2023_01_005.pdf: 1715840 bytes, checksum: db554ba969c6df88b4c141b7b1499932 (MD5); Made available in DSpace on 2023-03-09T15:45:11Z (GMT). No. of bitstreams: 1 ip2023_01_005.pdf: 1715840 bytes, checksum: db554ba969c6df88b4c141b7b1499932 (MD5)

Validity and Intra Rater Reliability of a New Device for Tongue Force Measurement

Validity and Intra Rater Reliability of a New Device for Tongue Force Measurement Diaz-Saez, Marta Carlota; Beltran-Alacreu, Hector; Gil-Castillo, Javier; Navarro-Fernández, Gonzalo; Cebrian Carretero, Jose Luis; Gil-Martínez, Alfonso Background. The tongue is made up of multiple muscles both extrinsic and intrinsic. The hyoid, jaw and maxillary complex contain the tongue, which hangs between these structures forming an important biomechanical system. This organ has to work in coordination with craniofacial structures to ensure normal orofacial functioning. There are different devices on the market for tongue force measurement. However, they are not accessible for patients due to their size and very high prices. Likewise, other devices have not yet carried out validity and reliability studies. The purpose of this study was to validate a new device proving that it is accurate compared to the algometer. Moreover, the study wanted to determine the intra-rater reliability of a protocol to assess the maximum tongue force in asymptomatic subjects. Material and methods. This is an observational-longitudinal study with repeated measurements. A prototype device was developed specifically for this study to measure tongue force through force-sensitive resistor sensors. The prototype system was equipped with a device to perform and transmit the measurement and a C++ programming software in the computer to take data from the session. Different formulas were made to calibrate the system. For validity, the force measured by the prototype and the algometer was compared. For intra-rater reliability, 29 asymptomatic Spanish subjects were recruited, and a standardized protocol was carried out for the tests. Results. Experiments to assess validity showed a strong correlation (r>0.97) and an excellent reliability (ICC>0.90) between devices.On the other hand, the intra-rater reliability analysis showed an excellent ICC (0.93) with a 95% CI of 0.86 to 0.97 and a MDC90 of 6.26N. Conclusion. We demonstrated good validity values and high intra-rater reliability for the prototype device for the maximum tongue force. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-09T15:14:12Z No. of bitstreams: 1 ip2022_02_001.pdf: 1108123 bytes, checksum: 83add9a60fbe02db172335dc2a1497f1 (MD5); Made available in DSpace on 2023-03-09T15:14:12Z (GMT). No. of bitstreams: 1 ip2022_02_001.pdf: 1108123 bytes, checksum: 83add9a60fbe02db172335dc2a1497f1 (MD5)

Chatbot-Based Learning Platform for SQL Training

Chatbot-Based Learning Platform for SQL Training Balderas, Antonio; Baena-Pérez, Rubén; Person, Tatiana; Mota, José Miguel; Ruiz-Rube, Iván Learning the SQL language for working with relational databases is a fundamental subject for future computer engineers. However, in distance learning contexts or unexpected situations like the COVID-19 pandemic, where students had to follow lectures remotely, they may find it hard to learn. Chatbots are software applications that aim to have conversations with people to help them solve problems or provide support in a specific domain. This paper proposes a chatbot-based learning platform to assist students in learning SQL. A case study has been conducted to evaluate the proposal, with undergraduate computer engineering students using the learning platform to perform SQL queries while being assisted by the chatbot. The results show evidence that students who used the chatbot performed better on the final SQL exam than those who did not. In addition, the research shows positive evidence of the benefits of using such learning platforms to support SQL teaching and learning for both students and lecturers: students use a platform that helps them self-regulate their learning process, while lecturers get interesting metrics on student performance. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-09T15:01:20Z No. of bitstreams: 1 ip2022_05_003.pdf: 1402528 bytes, checksum: fa631e1bc6e7d15b9f36bd1e2d12c703 (MD5); Made available in DSpace on 2023-03-09T15:01:20Z (GMT). No. of bitstreams: 1 ip2022_05_003.pdf: 1402528 bytes, checksum: fa631e1bc6e7d15b9f36bd1e2d12c703 (MD5)

Mapping the Situation of Educational Technologies in the Spanish University System Using Social Network Analysis and Visualization

Mapping the Situation of Educational Technologies in the Spanish University System Using Social Network Analysis and Visualization Vargas Quesada, B.; Zarco, Carmen; Cordón, Oscar Educational Technologies (EdTech) are based on the use of Information and Communication Technologies (ICT) to improve the quality of teaching and learning. EdTech is experiencing great development at different educational levels worldwide, especially since the appearance of Covid-19. The recent publication of a study by the ICT Sectorial of CRUE Universidades Españolas, the Spanish University Association, is the first report on the implementation of such technologies within Spain´s University System. This paper presents two different maps based on the data from that report. Together, they illustrate the penetration of different types of EdTech in our university system and shed light on the strategic interest behind their adoption. Our goal is to produce self-explanatory maps that can be easily and directly interpreted. The first map reflects wide granularity in terms of the global importance of technologies, while the second points to relevant conclusions given the spatial position of Spain´s universities, and the size of the nodes that represent them (directly related with their strategic interests on EdTech), as well as with the local relationships existing among them (identifying similarities on those strategic interests). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-09T08:55:53Z No. of bitstreams: 1 ip2021_09_04.pdf: 1539656 bytes, checksum: 2f1c40a9c7eae6019e6ebdf25943ac5d (MD5); Made available in DSpace on 2023-03-09T08:55:53Z (GMT). No. of bitstreams: 1 ip2021_09_04.pdf: 1539656 bytes, checksum: 2f1c40a9c7eae6019e6ebdf25943ac5d (MD5)

Point Cloud Deep Learning Solution for Hand Gesture Recognition

Point Cloud Deep Learning Solution for Hand Gesture Recognition Osimani, César; Ojeda-Castelo, Juan Jesus; Piedra-Fernandez, Jose A. In the last couple of years, there has been an increasing need for Human-Computer Interaction (HCI) systems that do not require touching the devices to control them, such as ATMs, self service kiosks in airports, terminals in public offices, among others. The use of hand gestures offers a natural alternative to achieve control without touching the devices. This paper presents a solution that allows the recognition of hand gestures by analyzing three-dimensional landmarks using deep learning. These landmarks are extracted by using a model created with machine learning techniques from a single standard RGB camera in order to define the skeleton of the hand with 21 landmarks distributed as follows: one on the wrist and four on each finger. This study proposes a deep neural network that was trained with 9 gestures receiving as input the 21 points of the hand. One of the main contributions, that considerably improves the performance, is a first layer of normalization and transformation of the landmarks. In our experimental analysis, we reach an accuracy of 99.87% recognizing of 9 hand gestures. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-09T08:40:35Z No. of bitstreams: 1 ip2023_01_001.pdf: 3726735 bytes, checksum: 59ea71a7db13110cd8657efe5823c1ba (MD5); Made available in DSpace on 2023-03-09T08:40:35Z (GMT). No. of bitstreams: 1 ip2023_01_001.pdf: 3726735 bytes, checksum: 59ea71a7db13110cd8657efe5823c1ba (MD5)

Editor’s Note

Editor’s Note Yang, Jiachen; Song, Houbing; Khurram Khan, Muhammad With the rapid development of information and communication technologies, artificial intelligence and IoTs, more and more advanced technologies, such as machine learning, reinforcement learning, neural networks and fuzzy systems, have been introduced into industrial practices. The application of advanced technologies has greatly promoted the process of industrial revolution. However, there is big gap between controlled simulation and real evolving environment, which results in the unsatisfactory performance of the typical algorithms in practical environments. For example, in Underwater IoTs, a dynamic and uncertain marine environment can cause equipment damage, resulting in huge financial losses. Therefore, improving the robustness and adaptability of algorithms and systems, and proposing new solutions in practical applications to meet the requirements of self-developing, self-organizing, and evolving systems is essential to promote intelligent industrial applications. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-08T14:33:40Z No. of bitstreams: 1 ijimai8_1_0_0.pdf: 60247 bytes, checksum: 479d5af4a15b484b21299a0d0b09d351 (MD5); Made available in DSpace on 2023-03-08T14:33:40Z (GMT). No. of bitstreams: 1 ijimai8_1_0_0.pdf: 60247 bytes, checksum: 479d5af4a15b484b21299a0d0b09d351 (MD5)

An Efficient Probabilistic Methodology to Evaluate Web Sources as Data Source for Warehousing

An Efficient Probabilistic Methodology to Evaluate Web Sources as Data Source for Warehousing Sharan Sinha, Hariom; Kumar Choudhary, Saket; Kumar Solanki, Vijender Internet is the largest source of data and the requirement of data analytics have fueled the data warehouse to switch from structured conventional Data Warehouse to complex Web Data Warehouse. The dynamic and complex nature of web poses various types of complexities during synthesis of web data into a conventional warehouse. Multi-Criteria-Decision Making (MCDM) is a prominent mechanism to select the best data for storing into the data-warehouse. In this article, a method, based on the probabilistic analysis of SAW and TOPSIS methods, has been proposed to select web data sources as data sources for web data warehouse. This method deals more efficiently with the dynamic and complex nature of web. Here, the result of the selection employs the analysis of both the methods (SAW and TOPSIS) to evaluate the probability of selection of respective score (1-9) for each feature. With these probability values, the probability of selection of the next web sources has been be determined. Moreover, using the same probability values, mean score and standard deviation of the scores of respective features of selected web sources have been deduced, which are further used to fix the standard score of each feature for selection of web sources. The standard score is a parameter of the proposed Mean-Standard-Deviation (MSD) method to check the suitability of web sources individually, whereas others do the same on comparative basis. The proposed method cuts down the cost of the repetitive comparison operation, once after computation of the Standard score using Mean and Standard deviation of each individual feature. Here, the respective value of the standard score of each feature is only compared with the score of each respective feature of the next web sources, so it reduces the cost of computation and selects the web sources faster as well. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-08T14:29:34Z No. of bitstreams: 1 ijimai8_1_9_0.pdf: 1385402 bytes, checksum: e91de83950e00b8d900611eb43d65c24 (MD5); Made available in DSpace on 2023-03-08T14:29:34Z (GMT). No. of bitstreams: 1 ijimai8_1_9_0.pdf: 1385402 bytes, checksum: e91de83950e00b8d900611eb43d65c24 (MD5)

A Spatio-Temporal Attention Graph Convolutional Networks for Sea Surface Temperature Prediction

A Spatio-Temporal Attention Graph Convolutional Networks for Sea Surface Temperature Prediction Chen, Desheng; Wen, Jiabao; Lv, Caiyun Sea surface temperature (SST) is an important index to detect ocean changes, predict SST anomalies, and prevent natural disasters caused by abnormal changes, dynamic variation of which have a profound impact on the whole marine ecosystem and the dynamic changes of climate. In order to better capture the dynamic changes of ocean temperature, it’s vitally essential to predict the SST in the future. A new spatio-temporal attention graph convolutional network (STAGCN) for SST prediction was proposed in this paper which can capture spatial dependence and temporal correlation in the way of integrating gated recurrent unit (GRU) model with graph convolutional network (GCN) and introduced attention mechanism. The STAGCN model adopts the GCN model to learn the topological structure between ocean location points for extracting the spatial characteristics from the ocean position nodes network. Besides, capturing temporal correlation by learning dynamic variation of SST time series data, a GRU model is introduced into the STAGCN model to deal with the prediction problem about long time series, the input of which is the SST data with spatial characteristics. To capture the significance of SST information at different times and increase the accuracy of SST forecast, the attention mechanism was used to obtain the spatial and temporal characteristics globally. In this study, the proposed STAGCN model was trained and tested on the East China Sea. Experiments with different prediction lengths show that the model can capture the spatio-temporal correlation of regional-scale sea surface temperature series and almost uniformly outperforms other classical models under different sea areas and different prediction levels, in which the root mean square error is reduced by about 0.2 compared with the LSTM model. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-08T14:10:36Z No. of bitstreams: 1 ijimai8_1_6.pdf: 3816814 bytes, checksum: c979aaa13f7cc3fab1abd8850ed7409b (MD5); Made available in DSpace on 2023-03-08T14:10:36Z (GMT). No. of bitstreams: 1 ijimai8_1_6.pdf: 3816814 bytes, checksum: c979aaa13f7cc3fab1abd8850ed7409b (MD5)

Using the Statistical Machine Learning Models ARIMA and SARIMA to Measure the Impact of Covid-19 on Official Provincial Sales of Cigarettes in Spain

Using the Statistical Machine Learning Models ARIMA and SARIMA to Measure the Impact of Covid-19 on Official Provincial Sales of Cigarettes in Spain Andueza, Andoni; Del Arco-Osuna, Miguel Ángel; Fornés, Bernat; González-Crespo, Rubén; Martín-Álvarez, Juan Manuel From a public health perspective, tobacco use is addictive by nature and triggers several cancers, cardiovascular and respiratory diseases, reproductive disorders, and many other adverse health effects leading to many deaths. In this context, the need to eradicate tobacco-related health problems and the increasingly complex environments of tobacco research require sophisticated analytical methods to handle large amounts of data and perform highly specialized tasks. In this study, time series models are used: autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) to forecast the impact of COVID-19 on sales of cigarette in Spanish provinces. To find the optimal solution, initial combinations of model parameters automatically selected the ARIMA model, followed by finding the optimized model parameters based on the best fit between the predictions and the test data. The analytical tools Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to assess the reliability of the models. The evaluation metrics that are used as criteria to select the best model are: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), mean percentage error (MPE), mean error (ME) and mean absolute standardized error (MASE). The results show that the national average impact is slight. However, in border provinces with France or with a high influx of tourists, a strong impact of COVID-19 on tobacco sales has been observed. In addition, the least impact has been observed in border provinces with Gibraltar. Policymakers need to make the right decisions about the tobacco price differentials that are observed between neighboring European countries when there is constant and abundant cross-border human transit. To keep smoking under control, all countries must make harmonized decisions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-07T14:25:59Z No. of bitstreams: 1 ijimai8_1_7.pdf: 3695376 bytes, checksum: ad125ea27f7e74c81ad2d02f5b972af0 (MD5); Made available in DSpace on 2023-03-07T14:25:59Z (GMT). No. of bitstreams: 1 ijimai8_1_7.pdf: 3695376 bytes, checksum: ad125ea27f7e74c81ad2d02f5b972af0 (MD5)

COVID-19 Disease Prediction Using Weighted Ensemble Transfer Learning

COVID-19 Disease Prediction Using Weighted Ensemble Transfer Learning Kumar Roy, Pradeep; Singh, Ashish Health experts use advanced technological equipment to find complex diseases and diagnose them. Medical imaging nowadays is popular for detecting abnormalities in human bodies. This research discusses using the Internet of Medical Things in the COVID-19 crisis perspective. COVID-19 disease created an unforgettable remark on human memory. It is something like never happened before, and people do not expect it in the future. Medical experts are continuously working on getting a solution for this deadly disease. This pandemic warns the healthcare system to find an alternative solution to monitor the infected person remotely. Internet of Medical Things can be helpful in a pandemic scenario. This paper suggested a ensemble transfer learning framework predict COVID-19 infection. The model used the weighted transfer learning concept and predicted the COVID- 19 infected people with an F1-score of 0.997 for the best case on the test dataset. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-07T14:15:11Z No. of bitstreams: 1 ijimai8_1_2.pdf: 3010491 bytes, checksum: 662915d100ed2fbf3de8e7dac2c15780 (MD5); Made available in DSpace on 2023-03-07T14:15:11Z (GMT). No. of bitstreams: 1 ijimai8_1_2.pdf: 3010491 bytes, checksum: 662915d100ed2fbf3de8e7dac2c15780 (MD5)

Sentiment Analysis and Classification of Hotel Opinions in Twitter With the Transformer Architecture

Sentiment Analysis and Classification of Hotel Opinions in Twitter With the Transformer Architecture Arroni, Sergio; Galán, Yerai; Guzmán-Guzmán, Xiomarah; Nuñez-Valdez, Edward Rolando; Gómez, Alberto Sentiment analysis is of great importance to parties who are interested is analyzing the public opinion in social networks. In recent years, deep learning, and particularly, the attention-based architecture, has taken over the field, to the point where most research in Natural Language Processing (NLP) has been shifted towards the development of bigger and bigger attention-based transformer models. However, those models are developed to be all-purpose NLP models, so for a concrete smaller problem, a reduced and specifically studied model can perform better. We propose a simpler attention-based model that makes use of the transformer architecture to predict the sentiment expressed in tweets about hotels in Las Vegas. With their relative predicted performance, we compare the similarity of our ranking to the actual ranking in TripAdvisor to those obtained by more rudimentary sentiment analysis approaches, outperforming them with a 0.64121 Spearman correlation coefficient. We also compare our performance to DistilBERT, obtaining faster and more accurate results and proving that a model designed for a particular problem can perform better than models with several millions of trainable parameters. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-07T13:58:12Z No. of bitstreams: 1 ijimai8_1_5.pdf: 1019610 bytes, checksum: 0d504615f95590cf76ca3443c1d0c2a3 (MD5); Made available in DSpace on 2023-03-07T13:58:13Z (GMT). No. of bitstreams: 1 ijimai8_1_5.pdf: 1019610 bytes, checksum: 0d504615f95590cf76ca3443c1d0c2a3 (MD5)

Blockchain Based Cloud Management Architecture for Maximum Availability

Blockchain Based Cloud Management Architecture for Maximum Availability Arias Maestro, Alberto; Sanjuán Martínez, Óscar; Teredesai, Ankur M.; García-Díaz, Vicente Contemporary cloud application and Edge computing orchestration systems rely on controller/worker design patterns to allocate, distribute, and manage resources. Standard solutions like Apache Mesos, Docker Swarm, and Kubernetes can span multiple zones at data centers, multiple global regions, and even consumer point of presence locations. Previous research has concluded that random network partitions cannot be avoided in these scenarios, leaving system designers to choose between consistency and availability, as defined by the CAP theorem. Controller/worker architectures guarantee configuration consistency via the employment of redundant storage systems, in most cases coordinated via consensus algorithms such as Paxos or Raft. These algorithms ensure information consistency against network failures while decreasing availability as network regions increase. Mainstream blockchain technology provides a solution to this compromise while decentralizing control via a fully distributed architecture coordinated through Byzantine-resistant consensus algorithms. This research proposes a blockchain-based decentralized architecture for cloud resource management systems. We analyze and compare the characteristics of the proposed architecture concerning the consistency, availability, and partition resistance of architectures that rely on Paxos/Raft distributed data stores. Our research demonstrates that the proposed blockchain-based decentralized architecture noticeably increases the system availability, including cases of network partitioning, without a significant impact on configuration consistency. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-07T13:45:41Z No. of bitstreams: 1 ijimai8_1_8.pdf: 913686 bytes, checksum: e9f4dff7f3c87f658ee8363bca9a7573 (MD5); Made available in DSpace on 2023-03-07T13:45:41Z (GMT). No. of bitstreams: 1 ijimai8_1_8.pdf: 913686 bytes, checksum: e9f4dff7f3c87f658ee8363bca9a7573 (MD5)

An Efficient Bet-GCN Approach for Link Prediction

An Efficient Bet-GCN Approach for Link Prediction Saxena, Rahul; Pankaj Patil, Spandan; Kumar Verma, Atul; Jadeja, Mahipal; Vyas, Pranshu; Bhateja, Vikrant; Chun-Wei Lin, Jerry The task of determining whether or not a link will exist between two entities, given the current position of the network, is called link prediction. The study of predicting and analyzing links between entities in a network is emerging as one of the most interesting research areas to explore. In the field of social network analysis, finding mutual friends, predicting the friendship status between two network individuals in the near future, etc., contributes significantly to a better understanding of the underlying network dynamics. The concept has many applications in biological networks, such as finding possible connections (possible interactions) between genes and predicting protein-protein interactions. Apart from these, the concept has applications in many other areas of network science. Exploration based on Graph Neural Networks (GNNs) to accomplish such tasks is another focus that is attracting a lot of attention these days. These approaches leverage the strength of the structural information of the network along with the properties of the nodes to make efficient predictions and classifications. In this work, we propose a network centrality based approach combined with Graph Convolution Networks (GCNs) to predict the connections between network nodes. We propose an idea to select training nodes for the model based on high edge betweenness centrality, which improves the prediction accuracy of the model. The study was conducted using three benchmark networks: CORA, Citeseer, and PubMed. The prediction accuracies for these networks are: 95.08%, 95.07%, and 95.3%. The performance of the model is comprehensive and comparable to the other prior art methods and studies. Moreover, the performance of the model is evaluated with 90.13% for WikiCS and 87.7% for Amazon Product network to show the generalizability of the model. The paper discusses in detail the reason for the improved predictive ability of the model both theoretically and experimentally. Our results are generalizable and our model has the potential to provide good results for link prediction tasks in any domain. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-07T13:26:55Z No. of bitstreams: 1 ijimai8_1_4.pdf: 3433075 bytes, checksum: 3bb4103136d7d2931ab87df24ce17f51 (MD5); Made available in DSpace on 2023-03-07T13:26:55Z (GMT). No. of bitstreams: 1 ijimai8_1_4.pdf: 3433075 bytes, checksum: 3bb4103136d7d2931ab87df24ce17f51 (MD5)

Dataset and Baselines for IID and OOD Image Classification Considering Data Quality and Evolving Environments

Dataset and Baselines for IID and OOD Image Classification Considering Data Quality and Evolving Environments Zhang, Zhuo; Li, Yang; Gong, Yicheng; Yang, Yue; Ma, Shukun; Guo, Xiaolan; Ercisli, Sezai At present, artificial intelligence is in a period of rapid development, and deep learning has begun to be applied in various fields. Data, as a key part of the deep learning, its efficiency and stability, will directly affect the performance of the model, so it is valued by people. In order to make the dataset efficient, many active learning methods have been proposed, the dataset containing independent identically distribution (IID) samples is reduced with excellent performance; in order to make the dataset more stable, it should be solved that the model encounters out-of-distribution (OOD) samples to improve generalization performance. However, the current active learning method design and the method of adding OOD samples lack guidance, and people do not know what samples should be selected and which OOD samples will be added to better improve the generalization performance. In this paper, we propose a dataset containing a variety of elements called a dataset with Complete Sample Elements(CSE), the labels such as rotation angle and distance in addition to the common classification labels. These labels can help people analyze the distribution characteristics of each element of an efficient dataset, thereby inspiring new active learning methods; we also construct a corresponding OOD test set, which can not only detect the generalization performance of the model, but also helps explore metrics between OOD samples and existing dataset to guide the selected method of OOD samples, so that it can improve generalization efficiently. In this paper, we explore the distribution characteristics of efficient datasets in terms of angle element, and confirm that an efficient dataset tends to contain samples with different appearance. At the same time, experiments have proved the positive influence of the addition of OOD samples on the generalization performance of dataset. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-07T13:03:45Z No. of bitstreams: 1 ijimai8_1_1.pdf: 6576201 bytes, checksum: 4ee9b392f5fdb25f490e679ed2f10c21 (MD5); Made available in DSpace on 2023-03-07T13:03:45Z (GMT). No. of bitstreams: 1 ijimai8_1_1.pdf: 6576201 bytes, checksum: 4ee9b392f5fdb25f490e679ed2f10c21 (MD5)

Human Activity Recognition From Sensorised Patient's Data in Healthcare: A Streaming Deep Learning-Based Approach

Human Activity Recognition From Sensorised Patient's Data in Healthcare: A Streaming Deep Learning-Based Approach Hurtado, Sandro; García-Nieto, José; Popov, Anton; Navas-Delgado, Ismael Physical inactivity is one of the main risk factors for mortality, and its relationship with the main chronic diseases has experienced intensive medical research. A well-known method for assessing people’s activity is the use of accelerometers implanted in wearables and mobile phones. However, a series of main critical issues arise in the healthcare context related to the limited amount of available labelled data to build a classification model. Moreover, the discrimination ability of activities is often challenging to capture since the variety of movement patterns in a particular group of patients (e.g. obesity or geriatric patients) is limited over time. Consequently, the proposed work presents a novel approach for Human Activity Recognition (HAR) in healthcare to avoid this problem. This proposal is based on semi-supervised classification with Encoder-Decoder Convolutional Neural Networks (CNNs) using a combination strategy of public labelled and private unlabelled raw sensor data. In this sense, the model will be able to take advantage of the large amount of unlabelled data available by extracting relevant characteristics in these data, which will increase the knowledge in the innermost layers. Hence, the trained model can generalize well when used in real-world use cases. Additionally, real-time patient monitoring is provided by Apache Spark streaming processing with sliding windows. For testing purposes, a real-world case study is conducted with a group of overweight patients in the healthcare system of Andalusia (Spain), classifying close to 30 TBs of accelerometer sensor-based data. The proposed HAR streaming deep-learning approach properly classifies movement patterns in real-time conditions, crucial for long-term daily patient monitoring. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-03-07T12:39:36Z No. of bitstreams: 1 ijimai8_1_3.pdf: 3518563 bytes, checksum: 6573b5bd2a0673fa99edd01c4851aa6c (MD5); Made available in DSpace on 2023-03-07T12:39:36Z (GMT). No. of bitstreams: 1 ijimai8_1_3.pdf: 3518563 bytes, checksum: 6573b5bd2a0673fa99edd01c4851aa6c (MD5)

Editor's Note

Editor's Note González-Crespo, Rubén The International Journal of Interactive Multimedia and Artificial Intelligence – IJIMAI – provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances in Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. The present regular issue includes 13 articles. The first block of articles deals with problems related to images as diverse as the artificial generation of images or the optimization of their storage and transmission through compression techniques. The applications are very diverse, including the identification of forgeries, tumors or even misplaced face masks. Another block contains only one paper on speech recognition targeted on specific users suffering from dysarthria. Other block of two articles focuses on the education field problems of automation of teachers’ certification processes or prediction of students’ academic failure. Last block of articles covers services and products, commerce, marketing and user experience issues, as well as the ethical implications of AI. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-20T11:00:57Z No. of bitstreams: 1 ijimai7_7_0.pdf: 66853 bytes, checksum: 0aba5440ad126684b1d595a028e101c5 (MD5); Made available in DSpace on 2022-12-20T11:00:57Z (GMT). No. of bitstreams: 1 ijimai7_7_0.pdf: 66853 bytes, checksum: 0aba5440ad126684b1d595a028e101c5 (MD5)

Adaptive Deep Learning Detection Model for Multi-Foggy Images

Adaptive Deep Learning Detection Model for Multi-Foggy Images Hussein Arif, Zainab; Mahmoud, Moamin; Hameed Abdulkareem, Karrar; Kadry, Seifedine; Abed Mohammed, Mazin; Nasser Al-Mhiqani, Mohammed; Al-Waisy, Alaa S.; Nedoma, Jan The fog has different features and effects within every single environment. Detection whether there is fog in the image is considered a challenge and giving the type of fog has a substantial enlightening effect on image defogging. Foggy scenes have different types such as scenes based on fog density level and scenes based on fog type. Machine learning techniques have a significant contribution to the detection of foggy scenes. However, most of the existing detection models are based on traditional machine learning models, and only a few studies have adopted deep learning models. Furthermore, most of the existing machines learning detection models are based on fog density-level scenes. However, to the best of our knowledge, there is no such detection model based on multi-fog type scenes have presented yet. Therefore, the main goal of our study is to propose an adaptive deep learning model for the detection of multi-fog types of images. Moreover, due to the lack of a publicly available dataset for inhomogeneous, homogenous, dark, and sky foggy scenes, a dataset for multi-fog scenes is presented in this study (https://github.com/Karrar-H-Abdulkareem/Multi-Fog-Dataset). Experiments were conducted in three stages. First, the data collection phase is based on eight resources to obtain the multi-fog scene dataset. Second, a classification experiment is conducted based on the ResNet-50 deep learning model to obtain detection results. Third, evaluation phase where the performance of the ResNet-50 detection model has been compared against three different models. Experimental results show that the proposed model has presented a stable classification performance for different foggy images with a 96% score for each of Classification Accuracy Rate (CAR), Recall, Precision, F1-Score which has specific theoretical and practical significance. Our proposed model is suitable as a pre-processing step and might be considered in different real-time applications. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-20T10:24:11Z No. of bitstreams: 1 ijimai7_7_3.pdf: 602150 bytes, checksum: 528455ea70d04817764aeae63ad6fb9d (MD5); Made available in DSpace on 2022-12-20T10:24:11Z (GMT). No. of bitstreams: 1 ijimai7_7_3.pdf: 602150 bytes, checksum: 528455ea70d04817764aeae63ad6fb9d (MD5)

UX Poker: Estimating the Influence of User Stories on User Experience in Early Stage of Agile Development

UX Poker: Estimating the Influence of User Stories on User Experience in Early Stage of Agile Development Hinderks, Andreas; Winter, Dominique; Domínguez Mayo, Francisco José; Escalona, María José; Thomaschewski, Jörg Agile methods are used more and more frequently to develop products by reducing development time. Requirements are typically written in user stories or epics. In this paper, a new method called UX Poker is presented. This is a method to estimate the impact of a user story on user experience before development. Thus, there is the opportunity that the product backlog can also be sorted according to the expected UX. To evaluate UX Poker, a case study was conducted with four agile teams. Besides, a workshop followed by a questionnaire was conducted with all four agile teams. The goal of being able to estimate the UX even before development was achieved. Using UX Poker to create another way to sort the product backlog can be considered achieved in this first evaluation. The results show that UX Poker can be implemented in a real- life application. Additionally, during the use of UX Poker, it was found that a shared understanding of UX began. The participants clarified in the team discussion about UX Poker what related to influence the user stories had on UX and what UX meant for their product. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-20T10:13:18Z No. of bitstreams: 1 ijimai7_7_11.pdf: 256171 bytes, checksum: 2c20e6e6daddf62c4d9ebf8cd2908e2a (MD5); Made available in DSpace on 2022-12-20T10:13:18Z (GMT). No. of bitstreams: 1 ijimai7_7_11.pdf: 256171 bytes, checksum: 2c20e6e6daddf62c4d9ebf8cd2908e2a (MD5)

Empirical Analysis of Ethical Principles Applied to Different AI Uses Cases

Empirical Analysis of Ethical Principles Applied to Different AI Uses Cases López Rivero, Alfonso José; Beato, M. Encarnación; Muñoz Martínez, César; Cortiñas Vázquez, Pedro Gonzalo In this paper, we present an empirical study on the perception of the ethical challenges of artificial intelligence groups in the classification made by the European Union (EU). The study seeks to identify the ethical principles that cause the greatest concern among the population, analyzing these characteristics among different actors. The main study analyses the difference between Information and Communications Technology (ICT) professionals and the rest of the population. Along with this study, we conducted a gender study; in addition, we studied differences between university students, classified as future professionals who can work in Artificial Intelligence, and other university students. We believe that this work is a starting point for an informed debate in the scientific community and industry on the ethical implications of artificial intelligence based on the classification of ethical principles made by the EU, which can be extrapolated to any analysis carried out on the use of data to apply them in algorithms based on Artificial Intelligence. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-20T10:04:49Z No. of bitstreams: 1 ijimai7_7_12.pdf: 395197 bytes, checksum: c1aa6db4da4fc00ea66c89e3f05f3920 (MD5); Made available in DSpace on 2022-12-20T10:04:49Z (GMT). No. of bitstreams: 1 ijimai7_7_12.pdf: 395197 bytes, checksum: c1aa6db4da4fc00ea66c89e3f05f3920 (MD5)

A Rule-Based Expert System for Teachers’ Certification in the Use of Learning Management Systems

A Rule-Based Expert System for Teachers’ Certification in the Use of Learning Management Systems Regueras, Luisa M.; Verdú, María J; de Castro, Juan-Pablo In recent years and accelerated by the arrival of the COVID-19 pandemic, Learning Management Systems (LMS) are increasingly used as a complement to university teaching. LMS provide an important number of resources and activities that teachers can freely select to complement their teaching, which means courses with different usage patterns difficult to characterize. This study proposes an expert system to automatically classify courses and certify teachers’ LMS competence from LMS logs. The proposed system uses clustering to stablish the classification scheme. From the output of this algorithm, it defines the rules used to classify courses. Data registered from a university virtual campus with 3,303 courses and two million interactive events have been used to obtain the classification scheme and rules. The system has been validated against a group of experts. Results show that it performs successfully. Therefore, it can be concluded that the system can automatically and satisfactorily evaluate and certify the teachers’ LMS competence evidenced in their courses. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-20T09:57:57Z No. of bitstreams: 1 ijimai7_7_8.pdf: 405410 bytes, checksum: 9fd1999cc93eae210c239882c736428c (MD5); Made available in DSpace on 2022-12-20T09:57:57Z (GMT). No. of bitstreams: 1 ijimai7_7_8.pdf: 405410 bytes, checksum: 9fd1999cc93eae210c239882c736428c (MD5)

Painting Authorship and Forgery Detection Challenges with AI Image Generation Algorithms: Rembrandt and 17th Century Dutch Painters as a Case Study

Painting Authorship and Forgery Detection Challenges with AI Image Generation Algorithms: Rembrandt and 17th Century Dutch Painters as a Case Study Fraile-Narvaez, Marcelo; Sagredo-Olivenza, Ismael; McGowan, Nadia Image authorship attribution presents many challenges and difficulties which have increased with the capabilities presented by synthetic image generation through different artificial intelligence algorithms available today. The hypothesis in this research considers the possibility of using artificial intelligence as a tool to detect forgeries through the usage of a deep learning algorithm. The proposed algorithm was trained using a dataset comprised of paintings by Rembrandt and other 17th century Dutch painters. Three experiments were performed with the proposed algorithm. The first was to build a classifier able to ascertain whether a painting belongs to the Rembrandt or non-Rembrandt category, depending on whether it was painted by this author or not. The second tests included other 17th century painters in four categories. Artworks could be classified as Rembrandt, Eeckhout, Leveck or other Dutch painters. The third experiment used paintings generated by Dall-e 2 and attempted to classify them using the prior categories. Experiments confirmed the hypothesis with best executions reaching accuracy rates of more than 90%. Future research with extended datasets and improved image resolution are suggested to improve the obtained results. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-16T11:36:31Z No. of bitstreams: 1 ijimai7_7_1.pdf: 357350 bytes, checksum: 54def274ce6c750fdcc43ac62677a515 (MD5); Made available in DSpace on 2022-12-16T11:36:31Z (GMT). No. of bitstreams: 1 ijimai7_7_1.pdf: 357350 bytes, checksum: 54def274ce6c750fdcc43ac62677a515 (MD5)

Brain Tumor Segmentation and Identification Using Particle Imperialist Deep Convolutional Neural Network in MRI Images

Brain Tumor Segmentation and Identification Using Particle Imperialist Deep Convolutional Neural Network in MRI Images Khemchandani, Maahi Amit; Jadhav, Shivajirao Manikra; Iyer, B. R. For the past few years, segmentation for medical applications using Magnetic Resonance (MR) images is concentrated. Segmentation of Brain tumors using MRIpaves an effective platform to plan the treatment and diagnosis of tumors. Thus, segmentation is necessary to be improved, for a novel framework. The Particle Imperialist Deep Convolutional Neural Network (PI-Deep CNN) suggested framework is intended to address the problems with segmenting and categorizing the brain tumor. Using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm, the input MRI brain image is segmented, and then features are extracted using the Scatter Local Neighborhood Structure (SLNS) descriptor. Combining the scattering transform and the Local Neighborhood Structure (LNS) descriptor yields the proposed descriptor. A suggested Particle Imperialist algorithm-trained Deep CNN is then used to achieve the tumor-level classification. Different levels of the tumor are classified by the classifier, including Normal without tumor, Abnormal, Malignant tumor, and Non-malignant tumor. The cell is identified as a tumor cell and is subjected to additional diagnostics, with the exception of the normal cells that are tumor-free. The proposed method obtained a maximum accuracy of 0.965 during the experimentation utilizing the BRATS database and performance measures. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-16T10:45:19Z No. of bitstreams: 1 ijimai7_7_4.pdf: 1266936 bytes, checksum: 9ba9c6bbe3fef052c28a25d529aad2d6 (MD5); Made available in DSpace on 2022-12-16T10:45:19Z (GMT). No. of bitstreams: 1 ijimai7_7_4.pdf: 1266936 bytes, checksum: 9ba9c6bbe3fef052c28a25d529aad2d6 (MD5)

A Fuzzy-Based Multimedia Content Retrieval Method Using Mood Tags and Their Synonyms in Social Networks

A Fuzzy-Based Multimedia Content Retrieval Method Using Mood Tags and Their Synonyms in Social Networks Moon, Chang Bae; Lee, Jong Yeol; Kim, Byeong Man The preferences of Web information purchasers are rapidly evolving. Cost-effectiveness is now becoming less regarded than cost-satisfaction, which emphasizes the purchaser’s psychological satisfaction. One method to improve a user’s cost-satisfaction in multimedia content retrieval is to utilize the mood inherent in multimedia items. An example of applications using this method is SNS (Social Network Services), which is based on folksonomy, but its applications encounter problems due to synonyms. In order to solve the problem of synonyms in our previous study, the mood of multimedia content is represented with arousal and valence (AV) in Thayer’s two-dimensional model as its internal tag. Although some problems of synonyms could now be solved, the retrieval performance of the previous study was less than that of a keyword-based method. In this paper, a new method that can solve the synonym problem is proposed, while simultaneously maintaining the same performance as the keyword-based approach. In the proposed method, a mood of multimedia content is represented with a fuzzy set of 12 moods of the Thayer model. For the analysis, the proposed method is compared with two methods, one based on AV value and the other based on keyword. The analysis results demonstrate that the proposed method is superior to the two methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-16T10:00:55Z No. of bitstreams: 1 ijimai7_7_7.pdf: 768608 bytes, checksum: d517d301ec21de2de6abdb13ee3f430c (MD5); Made available in DSpace on 2022-12-16T10:00:55Z (GMT). No. of bitstreams: 1 ijimai7_7_7.pdf: 768608 bytes, checksum: d517d301ec21de2de6abdb13ee3f430c (MD5)

Modeling Sub-Band Information Through Discrete Wavelet Transform to Improve Intelligibility Assessment of Dysarthric Speech

Modeling Sub-Band Information Through Discrete Wavelet Transform to Improve Intelligibility Assessment of Dysarthric Speech Sahu, Laxmi Priya; Pradhan, Gayadhar; Singh, Jyoti Prakash The speech signal within a sub-band varies at a fine level depending on the type, and level of dysarthria. The Mel-frequency filterbank used in the computation process of cepstral coefficients smoothed out this fine level information in the higher frequency regions due to the larger bandwidth of filters. To capture the sub-band information, in this paper, four-level discrete wavelet transform (DWT) decomposition is firstly performed to decompose the input speech signal into approximation and detail coefficients, respectively, at each level. For a particular input speech signal, five speech signals representing different sub-bands are then reconstructed using inverse DWT (IDWT). The log filterbank energies are computed by analyzing the short-term discrete Fourier transform magnitude spectra of each reconstructed speech using a 30-channel Mel-filterbank. For each analysis frame, the log filterbank energies obtained across all reconstructed speech signals are pooled together, and discrete cosine transform is performed to represent the cepstral feature, here termed as discrete wavelet transform reconstructed (DWTR)- Mel frequency cepstral coefficient (MFCC). The i-vector based dysarthric level assessment system developed on the universal access speech corpus shows that the proposed DTWRMFCC feature outperforms the conventional MFCC and several other cepstral features reported for a similar task. The usages of DWTR- MFCC improve the detection accuracy rate (DAR) of the dysarthric level assessment system in the text and the speaker-independent test case to 60.094 % from 56.646 % MFCC baseline. Further analysis of the confusion matrices shows that confusion among different dysarthric classes is quite different for MFCC and DWTR-MFCC features. Motivated by this observation, a two-stage classification approach employing discriminating power of both kinds of features is proposed to improve the overall performance of the developed dysarthric level assessment system. The two-stage classification scheme further improves the DAR to 65.813 % in the text and speaker- independent test case. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-16T08:21:37Z No. of bitstreams: 1 ijimai7_7_6.pdf: 355090 bytes, checksum: 2b3f70053a83aef40a9570661816538e (MD5); Made available in DSpace on 2022-12-16T08:21:37Z (GMT). No. of bitstreams: 1 ijimai7_7_6.pdf: 355090 bytes, checksum: 2b3f70053a83aef40a9570661816538e (MD5)

Marketing Intelligence: Boom or Bust of Service Marketing?

Marketing Intelligence: Boom or Bust of Service Marketing? Lies, Jan Marketing intelligence fosters two major developments within digital service marketing. On the one hand, a boom of services seems to have evolved, accelerated by the opportunities of marketing intelligence. It has contributed to the optimization of customer experiences, e.g., supported by mobile, personalized, and customized marketing services. On the other hand, (digital) self-services are likely to pervert the term “service”. Lifecycle marketing, including annoying marketing communication in real-time, automated price adjustment and programmatic advertising based on artificial intelligence, affects the vision of fully standardized marketing automation. Additionally, there are incentives to pollute the digital information in order to manufacture opinions. Fake news is one popular example. This leads to the (open) question if marketing intelligence means service boom or bust of marketing. This contribution aims to elaborate the boom-and-bust aspects of marketing intelligence and suggests a trade-off. The method applied in this paper will be a descriptive and conceptual literature review, through which the paradigmatic thoughts will be juxtaposed from the perspective of service. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-16T08:07:54Z No. of bitstreams: 1 ijimai7_7_13.pdf: 169541 bytes, checksum: ff93eba5b42aa5bd7aeada5b596b4573 (MD5); Made available in DSpace on 2022-12-16T08:07:54Z (GMT). No. of bitstreams: 1 ijimai7_7_13.pdf: 169541 bytes, checksum: ff93eba5b42aa5bd7aeada5b596b4573 (MD5)

Teaching through Learning Analytics: Predicting Student Learning Profiles in a Physics Course at a Higher Education Institution

Teaching through Learning Analytics: Predicting Student Learning Profiles in a Physics Course at a Higher Education Institution Rincón-Flores, Elvira G.; López-Camacho, Eunice; Mena, Juanjo; Olmos, Omar Learning Analytics (LA) is increasingly used in Education to set prediction models from artificial intelligence to determine learning profiles. This study aims to determine to what extent K-nearest neighbor and random forest algorithms could become a useful tool for improving the teaching-learning process and reducing academic failure in two Physics courses at the Technological Institute of Monterrey, México (n = 268). A quasi-experimental and mixed method approach was conducted. The main results showed significant differences between the first and second term evaluations in the two groups. One of the main findings of the study is that the predictions were not very accurate for each student in the first term evaluation. However, the predictions became more accurate as the algorithm was fed with larger datasets from the second term evaluation. This result indicates how predictive algorithms based on decision trees, can offer a close approximation to the academic performance that will occur in the class, and this information could be use along with the personal impressions coming from the teacher. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-16T07:52:47Z No. of bitstreams: 1 ijimai7_7_9.pdf: 977826 bytes, checksum: 622f301873713d52a8e6b1096e914ca3 (MD5); Made available in DSpace on 2022-12-16T07:52:47Z (GMT). No. of bitstreams: 1 ijimai7_7_9.pdf: 977826 bytes, checksum: 622f301873713d52a8e6b1096e914ca3 (MD5)

Optimizing Fast Fourier Transform (FFT) Image Compression using Intelligent Water Drop (IWD) Algorithm

Optimizing Fast Fourier Transform (FFT) Image Compression using Intelligent Water Drop (IWD) Algorithm Kaur, Surinder; Chaudhary, Gopal; Dinesh Kumar, Javalkar; Pillai, Manu S.; Gupta, Yash; Khari, Manju; García-Díaz, Vicente; Parra Fuente, Javier Digital image compression is the technique in digital image processing where special attention is provided in decreasing the number of bits required to represent a digital image. A wide range of techniques have been developed over the years, and novel approaches continue to emerge. This paper proposes a new technique for optimizing image compression using Fast Fourier Transform (FFT) and Intelligent Water Drop (IWD) algorithm. IWD-based FFT Compression is a emerging ethodology, and we expect compression findings to be much better than the methods currently being applied in the domain. This work aims to enhance the degree of compression of the image while maintaining the features that contribute most. It optimizes the FFT threshold values using swarm-based optimization technique (IWD) and compares the results in terms of Structural Similarity Index Measure (SSIM). The criterion of structural similarity of image quality is based on the premise that the human visual system is highly adapted to obtain structural information from the scene, so a measure of structural similarity provides a reasonable estimate of the perceived image quality. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-14T13:34:35Z No. of bitstreams: 1 ijimai7_7_5.pdf: 1424355 bytes, checksum: 2c436692e61ddc6c505755cec07a1376 (MD5); Made available in DSpace on 2022-12-14T13:34:35Z (GMT). No. of bitstreams: 1 ijimai7_7_5.pdf: 1424355 bytes, checksum: 2c436692e61ddc6c505755cec07a1376 (MD5)

Detection of Improperly Worn Face Masks using Deep Learning – A Preventive Measure Against the Spread of COVID-19

Detection of Improperly Worn Face Masks using Deep Learning – A Preventive Measure Against the Spread of COVID-19 Bhaik, Anubha; Singh, Vaishnavi; Gandotra, Ekta; Gupta, Deepak Coronavirus disease 2019 has had a pressing impact on people all around the world. Ceasing the spread of this infectious disease is the urgent need of the hour. A vital method of protection against the virus is wearing masks in public areas. Not merely wearing masks but wearing masks properly can ensure that the respiratory droplets do not get transmitted to other people. In this paper, we have proposed a deep learning-based model, which can be used to detect people who are not wearing their face masks properly. A convolutional neural network model based on the concept of transfer learning is trained on a self-made dataset of images and implemented with light-weighted neural network called MobileNetV2 for mobile architectures. OpenCV is used with Caffe framework to detect faces in an input frame which are further forwarded to our trained convolutional neural network for classification. The method has been implemented on various input images and classification results have been obtained for the same. The experimental results show that the proposed model achieves a testing accuracy and training accuracy of 93.58% and 92.27% respectively. Optimal results with high confidence scores and correct classification have also been achieved when the proposed model was tested on individual input images. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-14T13:18:35Z No. of bitstreams: 1 ijimai7_7_2.pdf: 700700 bytes, checksum: e8748c9b4bcea9a2e54cd3b267c2a1a6 (MD5); Made available in DSpace on 2022-12-14T13:18:35Z (GMT). No. of bitstreams: 1 ijimai7_7_2.pdf: 700700 bytes, checksum: e8748c9b4bcea9a2e54cd3b267c2a1a6 (MD5)

Balance Your Work-Life: Personal Interactive Web-Interface

Balance Your Work-Life: Personal Interactive Web-Interface Majumder, Soumi; Chowdhury, Soumalya; Dey, Nilanjan; Santosh, K. C. The term work-life balance can be described as a path to manage stresses and burnouts in the workplace. In this Covid-19 pandemic, work-from-home practice includes both personal and professional spaces as employees, more often, stay digitally connected. As a result, personal life hardly can be separated, which will potentially create imbalanced life, which creates problems regarding physical and mental health of the employees. In such unprecedented situations, we are required to maintain and/or integrate balanced work-life. A balanced work-life gives employees a stress-free environment to work and improves employees' mental and physical health conditions and relationships. In this study, we focus on maintaining a proper work-life balance through a monitoring tool, the ‘Wheel of Life.’ Considering the drastic changes in work culture (due to Covid-19, for example), we introduce an interactive interface based on ‘Wheel of life’ concept. Our interface helps tune various important factors, such as business, creative, social, love and life purpose, and provides multiple recommendations. The purpose of the study is to assist web users to balance their work-life, improve psychological well-being and quality of life in this unforeseen situation. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-14T13:10:14Z No. of bitstreams: 1 ijimai7_7_10.pdf: 671815 bytes, checksum: 0f10b3440a65c2a8d054c5a8ebe8c0bd (MD5); Made available in DSpace on 2022-12-14T13:10:14Z (GMT). No. of bitstreams: 1 ijimai7_7_10.pdf: 671815 bytes, checksum: 0f10b3440a65c2a8d054c5a8ebe8c0bd (MD5)

Editor's Note

Editor's Note de Paz Santana, Juan F.; Villarrubia González, Gabriel The international conference “Disruptive Technologies Tech Ethics and Artificial Intelligence” (DITTET) provides a forum to present and discuss the latest scientific and technical advances and their implications in the field of ethics. It also provides a forum for experts to present their latest research in disruptive technologies, promoting knowledge transfer. It provides a unique opportunity to bring together experts in different fields, academics, and professionals to exchange their experience in the development and deployment of disruptive technologies, artificial intelligence, and their ethical problems. This Special Issue contains extended versions of selected works presented at the 1st International Conference on Disruptive Technologies, Tech Ethics and Artificial Intelligence (DiTTEt 2021), held in Salamanca (Spain) in September 2021. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-13T13:06:03Z No. of bitstreams: 1 ijimai7_6_0_0.pdf: 50552 bytes, checksum: 81c326ba05451a619d4113f762da9c59 (MD5); Made available in DSpace on 2022-12-13T13:06:03Z (GMT). No. of bitstreams: 1 ijimai7_6_0_0.pdf: 50552 bytes, checksum: 81c326ba05451a619d4113f762da9c59 (MD5)

Board of Directors' Profile: A Case for Deep Learning as a Valid Methodology to Finance Research

Board of Directors' Profile: A Case for Deep Learning as a Valid Methodology to Finance Research Vaca, César; Tejerina, Fernando; Sahelices, Benjamín This paper presents a Deep Learning (DL) model for natural language processing of unstructured CVs to generate a six-dimensional profile of the professional experience of the Spanish companies' board of directors. We show the complete process starting with open data extraction and cleaning, the generation of a labeled dataset for supervised learning, the development, training and validation of a DL model capable of accurately analyzing the dataset, and, finally, a data analysis work based on the automated generation of the professional profiles of more than 6,000 directors of Spanish listed companies between 2003 and 2020. An RNN-LSTM neural network has been trained in three phases starting from a random initial state, (1) learning of basic structures of the Spanish language, (2) fine tuning for scientific texts in the field of economics and finance, and (3) regression modeling to generate a six-dimensional profile based on a generalization of sentiment classification systems. The complete training has been carried out with very low computational requirements, having a total duration of 120 hours of processing in a low-end GPU. The results obtained in the validation of the DL model show great accuracy, obtaining a value for the standard deviation of the mean error between 0.015 and 0.033. As a result, we have been able to outline with a high degree of reliability the profile of the listed Spanish companies' board of directors. We found that the predominant profile is that of directors with experience in executive or consultancy positions, followed by the financial profile. The results achieved show the potential of DL in social science research, particularly in Finance. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-13T12:59:08Z No. of bitstreams: 1 ijimai7_6_7.pdf: 1262285 bytes, checksum: 0ceb0b306c3da73d936b42f485d3c6de (MD5); Made available in DSpace on 2022-12-13T12:59:08Z (GMT). No. of bitstreams: 1 ijimai7_6_7.pdf: 1262285 bytes, checksum: 0ceb0b306c3da73d936b42f485d3c6de (MD5)

A Clustering Algorithm Based on an Ensemble of Dissimilarities: An Application in the Bioinformatics Domain

A Clustering Algorithm Based on an Ensemble of Dissimilarities: An Application in the Bioinformatics Domain Martín Merino, Manuel; López Rivero, Alfonso José; Alonso, Vidal; Vallejo, Marcelo; Ferreras, Antonio Clustering algorithms such as k-means depend heavily on choosing an appropriate distance metric that reflect accurately the object proximities. A wide range of dissimilarities may be defined that often lead to different clustering results. Choosing the best dissimilarity is an ill-posed problem and learning a general distance from the data is a complex task, particularly for high dimensional problems. Therefore, an appealing approach is to learn an ensemble of dissimilarities. In this paper, we have developed a semi-supervised clustering algorithm that learns a linear combination of dissimilarities considering incomplete knowledge in the form of pairwise constraints. The minimization of the loss function is based on a robust and efficient quadratic optimization algorithm. Besides, a regularization term is considered that controls the complexity of the distance metric learned avoiding overfitting. The algorithm has been applied to the identification of tumor samples using the gene expression profiles, where domain experts provide often incomplete knowledge in the form of pairwise constraints. We report that the algorithm proposed outperforms a standard semi-supervised clustering technique available in the literature and clustering results based on a single dissimilarity. The improvement is particularly relevant for applications with high level of noise. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-13T12:20:55Z No. of bitstreams: 1 ijimai7_6_1.pdf: 2597861 bytes, checksum: 9159982e29f2b2ab8468a339c3b4743d (MD5); Made available in DSpace on 2022-12-13T12:20:55Z (GMT). No. of bitstreams: 1 ijimai7_6_1.pdf: 2597861 bytes, checksum: 9159982e29f2b2ab8468a339c3b4743d (MD5)

Normative Affordances Through and By Technology: Technological Mediation and Human Enhancement

Normative Affordances Through and By Technology: Technological Mediation and Human Enhancement Döbler, Niklas Alexander; Bartnik, Clemens Human activity is fundamentally embedded in and constituted by technology. In this regard, technology influences not only how people experience the world, but also which possibilities for action offered by the environment (affordances) can be perceived and ultimately acted upon. As having socio-cultural and normative aspects, affordances are deeply relational to the technological human form of life. Postphenomenology describes several human-technology relations and their perception and action mediating effects. Therefore, it provides a suitable framework to examine how technology mediates the perception of affordances and leads to different behavioral outcomes. Technology can reveal hitherto hidden affordances but can also result in the manipulation and concealment of action possibilities. Both aspects can be deliberately controlled by using a particular technology and/or interfering with the technological hermeneutic process. Technological mal-functions, limitations, purposeful corruption, or human error can disrupt the hermeneutic qualities of technology and may lead to false conclusions about affordances and respective maladaptive behavioral outcomes. Technology can also be applied to humans to form “better” versions of them. One consequence of these so-called Human Enhancement technologies is the emergence of different affordances for the enhanced individual and the possible establishment of new affordances inside a form of life. Manipulating the perception and emergence of affordances through technological mediation or Human Enhancement can have severe political and ethical consequences. It is necessary to engage in an open debate about the perception and action mediating power of technology and the human reliance on them in our current and future form of life. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-13T12:14:14Z No. of bitstreams: 1 ijimai7_6_2.pdf: 532117 bytes, checksum: 6a7869b1df78391dbfdf57c14b9bdb19 (MD5); Made available in DSpace on 2022-12-13T12:14:14Z (GMT). No. of bitstreams: 1 ijimai7_6_2.pdf: 532117 bytes, checksum: 6a7869b1df78391dbfdf57c14b9bdb19 (MD5)

Integrating Emotion Recognition Tools for Developing Emotionally Intelligent Agents

Integrating Emotion Recognition Tools for Developing Emotionally Intelligent Agents Marcos-Pablos, Samuel; Lobato, Fernando; García-Peñalvo, Francisco Emotionally responsive agents that can simulate emotional intelligence increase the acceptance of users towards them, as the feeling of empathy reduces negative perceptual feedback. This has fostered research on emotional intelligence during last decades, and nowadays numerous cloud and local tools for automatic emotional recognition are available, even for inexperienced users. These tools however usually focus on the recognition of discrete emotions sensed from one communication channel, even though multimodal approaches have been shown to have advantages over unimodal approaches. Therefore, the objective of this paper is to show our approach for multimodal emotion recognition using Kalman filters for the fusion of available discrete emotion recognition tools. The proposed system has been modularly developed based on an evolutionary approach so to be integrated in our digital ecosystems, and new emotional recognition sources can be easily integrated. Obtained results show improvements over unimodal tools when recognizing naturally displayed emotions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-13T12:08:57Z No. of bitstreams: 1 ijimai7_6_8.pdf: 1705376 bytes, checksum: e9d27ecbbd80b0f5456b358fd9ea2eb6 (MD5); Made available in DSpace on 2022-12-13T12:08:57Z (GMT). No. of bitstreams: 1 ijimai7_6_8.pdf: 1705376 bytes, checksum: e9d27ecbbd80b0f5456b358fd9ea2eb6 (MD5)

An Event Mesh for Event Driven IoT Applications

An Event Mesh for Event Driven IoT Applications Berjón, Roberto; Mateos, Montserrat; Beato, M. Encarnación; Fermoso García, Ana In IoT contexts, software solutions are required to have components located in different environments: mobile, edge, fog or cloud. To design this type of application, event driven architecture (EDA) is used to develop distributed, scalable, decoupled, desynchronized and real-time components. The interconnection between the different components is done through event brokers that allow communication based on messages (events). Although the design of the components is independent of the environment in which they are deployed, this environment can determine the infrastructure to be used, for example the event brokers, so it is common to have to make modifications to the applications to adapt them to these environments, which complicates their design and maintenance. It is therefore necessary to have an event mesh that allows the connection between event brokers to simplify the development of applications. This paper presents the SCIFI-II system, an event mesh that allows the distribution of events between event brokers. Its use will allow the design of components decoupling them from the event brokers, which will facilitate their deployment in any environment. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-13T11:54:07Z No. of bitstreams: 1 ijimai7_6_6.pdf: 1950920 bytes, checksum: b752a4668767f65fedf0ef48eaa650ce (MD5); Made available in DSpace on 2022-12-13T11:54:07Z (GMT). No. of bitstreams: 1 ijimai7_6_6.pdf: 1950920 bytes, checksum: b752a4668767f65fedf0ef48eaa650ce (MD5)

Promoting Social Media Dissemination of Digital Images Through CBR-Based Tag Recommendation

Promoting Social Media Dissemination of Digital Images Through CBR-Based Tag Recommendation Martín-Gómez, Lucía; Pérez-Marcos, Javier; Cordero-Gutiérrez, Rebeca; De La Iglesia, Daniel H. Multimedia content has become an essential tool to share knowledge, sell products or disseminate messages. Some social networks use multimedia content to promote information and create social communities. In order to increase the impact of the digital content, those images or videos are labeled with different words, denominated tags. In this paper, we propose a recommender system which analyzes multimedia content and suggests tags to maximize its influence in the social community. It implements a Case-Based Reasoning architecture (CBR), which allows to learn from previous tagged content. The system has been evaluated through cross fold validation with a training and validation sets carefully constructed and extracted from Instagram. The results demonstrate that the system can suggest good options to label our image and maximize the influence of the multimedia content. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-13T11:38:52Z No. of bitstreams: 1 ijimai7_6_5.pdf: 1618020 bytes, checksum: c83bebe1b826a3865aa64c98c2fcf281 (MD5); Made available in DSpace on 2022-12-13T11:38:52Z (GMT). No. of bitstreams: 1 ijimai7_6_5.pdf: 1618020 bytes, checksum: c83bebe1b826a3865aa64c98c2fcf281 (MD5)

Edge Face Recognition System Based on One-Shot Augmented Learning

Edge Face Recognition System Based on One-Shot Augmented Learning Jiménez-Bravo, Diego M.; Lozano Murciego, Álvaro; Sales, A.; Augusto Silva, Luis; De La Iglesia, Daniel H. There is growing concern among users of computer systems about how their data is handled. In this sense, IT (Information Technology) professionals are not unaware of this problem and are looking for solutions to meet the requirements and concerns of their users. During the last few years, various techniques and technologies have emerged that allow us to answer to the problem posed by users. Technologies such as edge computing and techniques such as one-shot learning and data augmentation enable progress in this regard. Thus, in this article, we propose the creation of a system that makes use of these techniques and technologies to solve the problem of face recognition and form a low-cost security system. The results obtained show that the combination of these techniques is effective in most of the face detection algorithms and allows an effective solution to the problem raised. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-13T11:27:02Z No. of bitstreams: 1 ijimai7_6_4.pdf: 4275272 bytes, checksum: fe696c449ced17867c0c7589e008fb04 (MD5); Made available in DSpace on 2022-12-13T11:27:02Z (GMT). No. of bitstreams: 1 ijimai7_6_4.pdf: 4275272 bytes, checksum: fe696c449ced17867c0c7589e008fb04 (MD5)

A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms

A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms Henriques, João; Caldeira, Filipe Telecommunication Company’s (TELCO) are continuously delivering their efforts on the effectiveness of their daily work. Planning the activities for their workers is a crucial sensitive, and time-consuming task usually taken by experts. This plan aims to find an optimized solution maximizing the number of activities assigned to workers and minimizing the inherent costs (e.g., labor from workers, fuel, and other transportation costs). This paper proposes a model that allows computing a maximized plan for the activities assigned to their workers, allowing to alleviate the burden of the existing experts, even if supported by software implementing rule-based heuristic models. The proposed model is inspired by nature and relies on two stages supported by Genetic and Ant Colony evolutionary algorithms. At the first stage, a Genetic Algorithms (GA) identifies the optimal set of activities to be assigned to workers as the way to maximize the revenues. At a second step, an Ant Colony algorithm searches for an efficient path among the activities to minimize the costs. The conducted experimental work validates the effectiveness of the proposed model in the optimization of the planning TELCO work-field activities in comparison to a rule-based heuristic model. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-13T11:15:29Z No. of bitstreams: 1 ijimai7_6_3.pdf: 1213846 bytes, checksum: 5725afaa75f98da6dca5fc8ad1ba448b (MD5); Made available in DSpace on 2022-12-13T11:15:29Z (GMT). No. of bitstreams: 1 ijimai7_6_3.pdf: 1213846 bytes, checksum: 5725afaa75f98da6dca5fc8ad1ba448b (MD5)

Editor's Note

Editor's Note Shu, Lei; Rodrigues, Joel J.P.C.; Cohn, Anthony G.; Mao, Qirong; Li, Maozhen As the Internet of Things (IoT) further develops and expands to the Internet of Everything (IoE), high-speed multimedia streaming data processing, analysis, and shorter response times are increasingly becoming the demands of today. Driven by the Internet of Things (IoT), a new computing paradigm, Edge computing, is currently developing rapidly. Compared with traditional centralized generalpurpose computing, Edge computing is a distributed architecture. The operations of applications, data and services are moved from the central node of the network to the edge nodes on the network logic for processing. Under this structure, the analysis of data and the generation of knowledge are closer to the source of the data, so it is more suitable for processing. However, with the rapid development of 5G, IoT and other services and scenarios, there are more and more intelligent terminal devices. Multimedia streaming processing in IoT becomes a very prominent problem. To overcome this problem, the adoption of intelligent Edge or Artificial Intelligence (AI) powered Edge computing (Edge-AI) can achieve the goals of lower cost, higher security, lower latency, and ease of management. Recently, many network modeling methods, computing algorithms, and signal processing technologies have been successfully developed and applied to multimedia streaming processing in IoT with Edge Intelligence. A total of 13 papers are presented in this special issue for the purpose of collecting the latest developments and results on this research topic. We divide them into three categories: production and life applications, security, and text and image processing. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-12-13T11:02:05Z No. of bitstreams: 1 ijimai_7_5_0 (1).pdf: 57934 bytes, checksum: f5174c18dbace77d839513db23c74daa (MD5); Made available in DSpace on 2022-12-13T11:02:05Z (GMT). No. of bitstreams: 1 ijimai_7_5_0 (1).pdf: 57934 bytes, checksum: f5174c18dbace77d839513db23c74daa (MD5)

Electromiographic Signal Processing Using Embedded Artificial Intelligence: An Adaptive Filtering Approach

Electromiographic Signal Processing Using Embedded Artificial Intelligence: An Adaptive Filtering Approach Proaño-Guevara, Daniel; Blanco Valencia, Xiomara Patricia; Rosero-Montalvo, Paul D.; Peluffo-Ordóñez, Diego H. In recent times, Artificial Intelligence (AI) has become ubiquitous in technological fields, mainly due to its ability to perform computations in distributed systems or the cloud. Nevertheless, for some applications -as the case of EMG signal processing- it may be highly advisable or even mandatory an on-the-edge processing, i.e., an embedded processing methodology. On the other hand, sEMG signals have been traditionally processed using LTI techniques for simplicity in computing. However, making this strong assumption leads to information loss and spurious results. Considering the current advances in silicon technology and increasing computer power, it is possible to process these biosignals with AI-based techniques correctly. This paper presents an embedded-processing-based adaptive filtering system (here termed edge AI) being an outstanding alternative in contrast to a sensor-computer- actuator system and a classical digital signal processor (DSP) device. Specifically, a PYNQ-Z1 embedded system is used. For experimental purposes, three methodologies on similar processing scenarios are compared. The results show that the edge AI methodology is superior to benchmark approaches by reducing the processing time compared to classical DSPs and general standards while maintaining the signal integrity and processing it, considering that the EMG system is not LTI. Likewise, due to the nature of the proposed architecture, handling information exhibits no leakages. Findings suggest that edge computing is suitable for EMG signal processing when an on-device analysis is required. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-24T12:32:00Z No. of bitstreams: 1 ijimai_7_5_5.pdf: 2505299 bytes, checksum: a1cd541c74e13838cb329bb21f83d7ee (MD5); Made available in DSpace on 2022-10-24T12:32:00Z (GMT). No. of bitstreams: 1 ijimai_7_5_5.pdf: 2505299 bytes, checksum: a1cd541c74e13838cb329bb21f83d7ee (MD5)

ED-Dehaze Net: Encoder and Decoder Dehaze Network

ED-Dehaze Net: Encoder and Decoder Dehaze Network Zhang, Hongqi; Wei, Yixiong; Zhou, Hongqiao; Wu, Qianhao The presence of haze will significantly reduce the quality of images, such as resulting in lower contrast and blurry details. This paper proposes a novel end-to-end dehazing method, called Encoder and Decoder Dehaze Network (ED-Dehaze Net), which contains a Generator and a Discriminator. In particular, the Generator uses an Encoder-Decoder structure to effectively extract the texture and semantic features of hazy images. Between the Encoder and Decoder we use Multi-Scale Convolution Block (MSCB) to enhance the process of feature extraction. The proposed ED-Dehaze Net is trained by combining Adversarial Loss, Perceptual Loss and Smooth L1 Loss. Quantitative and qualitative experimental results showed that our method can obtain the state-of-the-art dehazing performance. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-24T12:19:23Z No. of bitstreams: 1 ijimai_7_5_11.pdf: 1073103 bytes, checksum: d17c5e128ba713f448c3f5ce626236bb (MD5); Made available in DSpace on 2022-10-24T12:19:23Z (GMT). No. of bitstreams: 1 ijimai_7_5_11.pdf: 1073103 bytes, checksum: d17c5e128ba713f448c3f5ce626236bb (MD5)

Interactive Causal Correlation Space Reshape for Multi-Label Classification

Interactive Causal Correlation Space Reshape for Multi-Label Classification Zhang, Chao; Cheng, Yusheng; Wang, Yibin; Xu, Yuting Most existing multi-label classification models focus on distance metrics and feature spare strategies to extract specific features of labels. Those models use the cosine similarity to construct the label correlation matrix to constraint solution space, and then mine the latent semantic information of the label space. However, the label correlation matrix is usually directly added to the model, which ignores the interactive causality of the correlation between the labels. Considering the label-specific features based on the distance method merely may have the problem of distance measurement failure in the high-dimensional space, while based on the sparse weight matrix method may cause the problem that parameter is dependent on manual selection. Eventually, this leads to poor classifier performance. In addition, it is considered that logical labels cannot describe the importance of different labels and cannot fully express semantic information. Based on these, we propose an Interactive Causal Correlation Space Reshape for Multi-Label Classification (CCSRMC) algorithm. Firstly, the algorithm constructs the label propagation matrix using characteristic that similar instances can be linearly represented by each other. Secondly, label co-occurrence matrix is constructed by combining the conditional probability test method, which is based on the label propagation reshaping the label space to rich label semantics. Then the label co-occurrence matrix combines with the label correlation matrix to construct the label interactive causal correlation matrix to perform multi-label classification learning on the obtained numerical label matrix. Finally, the algorithm in this paper is compared with multiple advanced algorithms on multiple benchmark multi-label datasets. The results show that considering the interactive causal label correlation can reduce the redundant information in the model and improve the performance of the multi-label classifier. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-24T12:11:48Z No. of bitstreams: 1 ijimai_7_5_13.pdf: 1807426 bytes, checksum: 96ad73357576576500aa03ce6588e770 (MD5); Made available in DSpace on 2022-10-24T12:11:48Z (GMT). No. of bitstreams: 1 ijimai_7_5_13.pdf: 1807426 bytes, checksum: 96ad73357576576500aa03ce6588e770 (MD5)

Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization

Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization Ling, Yongfa; Guan, Wenbo; Ruan, Qiang; Song, Heping; Lai, Yuping he finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability. Under the conventional variational inference (VI) framework, the analytically tractable solution to the optimization of the variational posterior distribution cannot be obtained, since the variational object function involves evaluation of intractable moments. With the recently proposed extended variational inference (EVI) framework, a new function is proposed to replace the original variational object function in order to avoid intractable moment computation, so that the analytically tractable solution of the IBLMM can be derived in an effective way. The good performance of the proposed approach is demonstrated by experiments with both synthesized data and a real-world application namely text categorization. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-24T11:57:46Z No. of bitstreams: 1 ijimai_7_5_9.pdf: 1568609 bytes, checksum: dbf2880f5751c6294df6ba662961d4aa (MD5); Made available in DSpace on 2022-10-24T11:57:46Z (GMT). No. of bitstreams: 1 ijimai_7_5_9.pdf: 1568609 bytes, checksum: dbf2880f5751c6294df6ba662961d4aa (MD5)

Design of Integrated Artificial Intelligence Techniques for Video Surveillance on IoT Enabled Wireless Multimedia Sensor Networks

Design of Integrated Artificial Intelligence Techniques for Video Surveillance on IoT Enabled Wireless Multimedia Sensor Networks Mansour, Romany F.; Soto, Carlos; Soto-Díaz, Roosvel; Escorcia Gutierrez, José; Gupta, Deepak; Khanna, Ashish The recent advancements in the Internet of Things (IoT) and Wireless Multimedia Sensor Networks (WMSN) made high-speed multimedia streaming, data processing, and essential analytics processes with minimal delay. Multimedia sensors used in WMSN-based surveillance applications are beneficial helpful in attaining accurate and elaborate details. However, it has become essential to design an effective and lightweight solution for data traffic management in WMSN owing to the massive quantities of data, generated by multimedia sensors. The development of Artificial Intelligence (AI) and Machine Learning (ML) techniques can be leveraged to investigate, collect, store, and process multimedia streaming data for decision-making in real-time scenarios. In this aspect, the current study develops an Integrated AI technique for Video Surveillance in IoT-enabled WMSN, called IAIVS-WMSN. The proposed IAIVS-WMSN technique aims to design a practical scheme for object detection and data transmission in WMSN. The proposed IAIVS-WMSN approach encompasses three stages: object detection, image compression, and clustering. The Mask Regional Convolutional Neural Network (Mask RCNN) technique is primarily utilized for object detection in the target region. Besides, Neighbourhood Correlation Sequence-based Image Compression (NCSIC) technique is applied to reduce data transmission. Finally, Artificial Flora Algorithm (AFA)-based clustering technique is designed for the election of Cluster Heads (CHs) and construction clusters. The design of object detection with compression and clustering techniques for WMSN shows the novelty of the work. These three processes’ designs enable one to accomplish effective data transmission in IoT-enabled WMSN. The researchers conducted multiple simulations to highlight the supreme performance of the IAIVS-WMSN approach. The simulation outcomes inferred the enhanced performance of the IAIVS-WMSN algorithm to the existing approaches. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-24T11:47:16Z No. of bitstreams: 1 ijimai_7_5_2.pdf: 945311 bytes, checksum: 73bae6ed6679f9f2d0cda8b9026fa47d (MD5); Made available in DSpace on 2022-10-24T11:47:16Z (GMT). No. of bitstreams: 1 ijimai_7_5_2.pdf: 945311 bytes, checksum: 73bae6ed6679f9f2d0cda8b9026fa47d (MD5)

Content-Based Hyperspectral Image Compression Using a Multi-Depth Weighted Map With Dynamic Receptive Field Convolution

Content-Based Hyperspectral Image Compression Using a Multi-Depth Weighted Map With Dynamic Receptive Field Convolution Pan, Shaoming; Gu, XiaoLin; Chong, Yanwen; Guo, Yuanyuan In content-based image compression, the importance map guides the bit allocation based on its ability to represent the importance of image contents. In this paper, we improve the representational power of importance map using Squeeze-and-Excitation (SE) block, and propose multi-depth structure to reconstruct non-important channel information at low bit rates. Furthermore, Dynamic Receptive Field convolution (DRFc) is introduced to improve the ability of normal convolution to extract edge information, so as to increase the weight of edge content in the importance map and improve the reconstruction quality of edge regions. Results indicate that our proposed method can extract an importance map with clear edges and fewer artifacts so as to provide obvious advantages for bit rate allocation in content-based image compression. Compared with typical compression methods, our proposed method can greatly improve the performance of Peak Signal-to-Noise Ratio (PSNR), structural similarity (SSIM) and spectral angle (SAM) on three public datasets, and can produce a much better visual result with sharp edges and fewer artifacts. As a result, our proposed method reduces the SAM by 42.8% compared to the recently SOTA method to achieve the same low bpp (0.25) on the KAIST dataset. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-24T11:13:28Z No. of bitstreams: 1 ijimai_7_5_10.pdf: 2184511 bytes, checksum: 5372b03215f7ec31be4ee91a33d8737f (MD5); Made available in DSpace on 2022-10-24T11:13:28Z (GMT). No. of bitstreams: 1 ijimai_7_5_10.pdf: 2184511 bytes, checksum: 5372b03215f7ec31be4ee91a33d8737f (MD5)

Improvement in Quality of Service Against Doppelganger Attacks for Connected Network

Improvement in Quality of Service Against Doppelganger Attacks for Connected Network Choudhary, Deepak; Pahuja, Roop Because they are in a high-risk location, remote sensors are vulnerable to malicious ambushes. A doppelganger attack, in which a malicious hub impersonates a legitimate network junction and then attempts to take control of the entire network, is one of the deadliest types of ambushes. Because remote sensor networks are portable, hub doppelganger ambushes are particularly ineffective in astute wellness contexts. Keeping the framework safe from hostile hubs is critical because the information in intelligent health frameworks is so sensitive. This paper developed a new Steering Convention for Vitality Effective Systems (SC-VFS) technique for detecting doppelganger attacks in IoT-based intelligent health applications such as a green corridor for transplant pushback. This method's main advantage is that it improves vitality proficiency, a critical constraint in WSN frameworks. To emphasize the suggested scheme's execution, latency, remaining vitality, throughput, vitality effectiveness, and blunder rate are all used. To see how proper the underutilized technique is compared to the existing Half Breed Multi-Level Clustering (HMLC) computation. The suggested approach yields latency of 0.63ms and 0.6ms, respectively, when using dead hubs and keeping a strategic distance from doppelganger assault. Furthermore, during the 2500 cycles, the suggested system achieves the highest remaining vitality of 49.5J. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-20T11:18:26Z No. of bitstreams: 1 ijimai_7_5_6.pdf: 1342027 bytes, checksum: 7c92439ec000450b620e83a1e9180138 (MD5); Made available in DSpace on 2022-10-20T11:18:26Z (GMT). No. of bitstreams: 1 ijimai_7_5_6.pdf: 1342027 bytes, checksum: 7c92439ec000450b620e83a1e9180138 (MD5)

A Security Situation Awareness Approach for IoT Software Chain Based on Markov Game Model

A Security Situation Awareness Approach for IoT Software Chain Based on Markov Game Model Zhu, Xudong; Deng, Honggao Since Internet of Things (IoT) has been widely used in our daily life nowadays, it is regarded as a promising and popular application of the Internet, and has attracted more and more attention. However, IoT is also suffered by some security problems which seriously affect the implementation of IoT system. Similar to traditional software, IoT software is always threated by many vulnerabilities, thus how to evaluate the security situation of IoT software chain becomes a basic requirement. In this paper, A framework of security situation awareness for IoT software chain is proposed, which mainly includes two processes: IoT security situation classification based on support vector machine and security situation awareness based on Markov game model. The proposed method firstly constructs a classification model using support vector machine (IoT) to automatically evaluates the security situation of IoT software chain. Based on the situation classification, we further proposed to adopt Markov model to simulate and predict the next behaviors of participants that involved in IoT system. Additionally, we have designed and developed a security situation awareness system for IoT software chain, the developed system supports the detection of typical IoT vulnerabilities and inherits more than 20 vulnerability detection methods, which shows great potential in IoT system protection. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-20T11:14:03Z No. of bitstreams: 1 ijimai_7_5_7.pdf: 448210 bytes, checksum: a3a6c4eb9dbbf54f4b39f20b17f5037a (MD5); Made available in DSpace on 2022-10-20T11:14:03Z (GMT). No. of bitstreams: 1 ijimai_7_5_7.pdf: 448210 bytes, checksum: a3a6c4eb9dbbf54f4b39f20b17f5037a (MD5)

A Diverse Domain Generative Adversarial Network for Style Transfer on Face Photographs

A Diverse Domain Generative Adversarial Network for Style Transfer on Face Photographs Tahir, Rabia; Cheng, Keyang; Memon, Bilal Ahmed; Liu, Qing The applications of style transfer on real time photographs are very trending now. This is used in various applications especially in social networking sites such as SnapChat and beauty cameras. A number of style transfer algorithms have been proposed but they are computationally expensive and generate artifacts in output image. Besides, most of research work only focuses on some traditional painting style transfer on real photographs. However, our work is unique as it considers diverse style domains to be transferred on real photographs by using one model. In this paper, we propose a Diverse Domain Generative Adversarial Network (DD-GAN) which performs fast diverse domain style translation on human face images. Our work is highly efficient and focused on applying different attractive and unique painting styles to human photographs while keeping the content preserved after translation. Moreover, we adopt a new loss function in our model and use PReLU activation function which improves and fastens the training procedure and helps in achieving high accuracy rates. Our loss function helps the proposed model in achieving better reconstructed images. The proposed model also occupies less memory space during training. We use various evaluation parameters to inspect the accuracy of our model. The experimental results demonstrate the effectiveness of our method as compared to state-of-the-art results. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-20T11:09:34Z No. of bitstreams: 1 ijimai_7_5_12.pdf: 1419197 bytes, checksum: f6514d66ec1ea8bc2bb0aaa16de27c0e (MD5); Made available in DSpace on 2022-10-20T11:09:35Z (GMT). No. of bitstreams: 1 ijimai_7_5_12.pdf: 1419197 bytes, checksum: f6514d66ec1ea8bc2bb0aaa16de27c0e (MD5)

STAIBT: Blockchain and CP-ABE Empowered Secure and Trusted Agricultural IoT Blockchain Terminal

STAIBT: Blockchain and CP-ABE Empowered Secure and Trusted Agricultural IoT Blockchain Terminal Zhang, Guofeng; Chen, Xiao; Zhang, Lei; Feng, Bin; Guo, Xuchao; Liang, Jingyun; Zhang, Yanan The integration of agricultural Internet of Things (IoT) and blockchain has become the key technology of precision agriculture. How to protect data privacy and security from data source is one of the difficult issues in agricultural IoT research. This work integrates cryptography, blockchain and Interplanetary File System (IPFS) technologies, and proposes a general IoT blockchain terminal system architecture, which strongly supports the integration of the IoT and blockchain technology. This research innovatively designed a fine-grained and flexible terminal data access control scheme based on the ciphertext-policy attribute-based encryption (CP-ABE) algorithm. Based on CP-ABE and DES algorithms, a hybrid data encryption scheme is designed to realize 1-to-N encrypted data sharing. A "horizontal + vertical" IoT data segmentation scheme under blockchain technology is proposed to realize the classified release of different types of data on the blockchain. The experimental results show that the design scheme can ensure data access control security, privacy data confidentiality, and data high-availability security. This solution significantly reduces the complexity of key management, can realize efficient sharing of encrypted data, flexibly set access control strategies, and has the ability to store large data files in the agricultural IoT. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-19T13:36:18Z No. of bitstreams: 1 ijimai_7_5_8.pdf: 1468614 bytes, checksum: f7a8958add3d998f5b782372ef4574b2 (MD5); Made available in DSpace on 2022-10-19T13:36:18Z (GMT). No. of bitstreams: 1 ijimai_7_5_8.pdf: 1468614 bytes, checksum: f7a8958add3d998f5b782372ef4574b2 (MD5)

A Method of the Coverage Ratio of Street Trees Based on Deep Learning

A Method of the Coverage Ratio of Street Trees Based on Deep Learning Han, Wen; Cao, Lei; Xu, Sheng The street trees coverage ratio provides reliable data support for urban ecological environment assessment, which plays an important part in the ecological environment index calculation. Aiming at the statistical estimation of urban street trees coverage ratio, an integrated model based on YOLOv4 and Unet network for detecting and extracting street trees from remote sensing images is proposed, and obtain the estimated street trees coverage ratio in images accurately. The experiments are carried out under self-made dataset, and the results show that the accuracy of street trees detection is 94.91%, and the street trees coverage ratio is 16.30% and 13.81% in the two experimental urban scenes. The MIoU of contour extraction is 98.25%, and the estimated coverage accuracy is improved by 6.89% and 5.79%, respectively. The result indicates that the proposed model achieves the automation of contour extraction of street trees and more accurate estimation of street trees coverage ratio. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-19T13:29:33Z No. of bitstreams: 1 ijimai_7_5_3.pdf: 1317222 bytes, checksum: c7affc1942dda0dbd6e289c687e80f75 (MD5); Made available in DSpace on 2022-10-19T13:29:33Z (GMT). No. of bitstreams: 1 ijimai_7_5_3.pdf: 1317222 bytes, checksum: c7affc1942dda0dbd6e289c687e80f75 (MD5)

Improved GWO Algorithm for UAV Path Planning on Crop Pest Monitoring

Improved GWO Algorithm for UAV Path Planning on Crop Pest Monitoring Ding, Qun; Xu, Xiaolong Agricultural information monitoring is the monitoring of the agricultural production process, and its task is to monitor the growth process of major crops systematically. When assessing the pest situation of crops in this process, the traditional satellite monitoring method has the defects of poor real-time and high operating cost, whereas the pest monitoring through Unmanned Aerial Vehicles (UAVs) effectively solves the above problems, so this method is widely used. An important key issue involved in monitoring technology is path planning. In this paper, we proposed an Improved Grey Wolf Optimization algorithm, IGWO, to realize the flight path planning of UAV in crop pest monitoring. A map environment model is simulated, and information traversal is performed, then the search of feasible paths for UAV flight is carried out by the Grey Wolf Optimization algorithm (GWO). However, the algorithm search process has the defect of falling into local optimum which leading to path planning failure. To avoid such a situation, we introduced the probabilistic leap mechanism of the Simulated Annealing algorithm (SA). Besides, the convergence factor is modified with an exponential decay mode for improving the convergence rate of the algorithm. Compared with the GWO algorithm, IGWO has the 8.3%, 16.7%, 28.6% and 39.6% lower total cost of path distance on map models with precision of 15, 20, 25 and 30 respectively, and also has better path planning results in contrast to other swarm intelligence algorithms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-19T13:19:20Z No. of bitstreams: 1 ijimai_7_5_4.pdf: 1625214 bytes, checksum: f64c3c28f5c8645d6ca6a10ead4fcc1e (MD5); Made available in DSpace on 2022-10-19T13:19:20Z (GMT). No. of bitstreams: 1 ijimai_7_5_4.pdf: 1625214 bytes, checksum: f64c3c28f5c8645d6ca6a10ead4fcc1e (MD5)

An EEG Signal Recognition Algorithm During Epileptic Seizure Based on Distributed Edge Computing

An EEG Signal Recognition Algorithm During Epileptic Seizure Based on Distributed Edge Computing Qiu, Shi; Cheng, Keyang; Zhou, Tao; Tahir, Rabia; Ting, Liang Epilepsy is one kind of brain diseases, and its sudden unpredictability is the main cause of disability and even death. Thus, it is of great significance to identify electroencephalogram (EEG) during the seizure quickly and accurately. With the rise of cloud computing and edge computing, the interface between local detection and cloud recognition is established, which promotes the development of portable EEG detection and diagnosis. Thus, we construct a framework for identifying EEG signals in epileptic seizure based on cloud-edge computing. The EEG signals are obtained in real time locally, and the horizontal viewable model is established at the edge to enhance the internal correlation of the signals. The Takagi-Sugeno-Kang (TSK) fuzzy system is established to analyze the epileptic signals. In the cloud, the fusion of clinical features and signal features is established to establish a deep learning framework. Through local signal acquisition, edge signal processing and cloud signal recognition, the diagnosis of epilepsy is realized, which can provide a new idea for the real-time diagnosis and feedback of EEG during epileptic seizure. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-19T13:13:42Z No. of bitstreams: 1 ijimai_7_5_1.pdf: 1175870 bytes, checksum: e40110c06e611ff697052c4273db5617 (MD5); Made available in DSpace on 2022-10-19T13:13:42Z (GMT). No. of bitstreams: 1 ijimai_7_5_1.pdf: 1175870 bytes, checksum: e40110c06e611ff697052c4273db5617 (MD5)

Editor's Note

Editor's Note Dey, Nilanjan The International Journal of Interactive Multimedia and Artificial Intelligence – IJIMAI (ISSN 1989-1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances in Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. The present volume (June 2022), consists of 20 articles of diverse applications of great impact in several fields. The issue consistently showcases the utilization of AI techniques or mathematical models with an artificial intelligence base, as a standard element. Different manuscripts on usability and satisfaction, machine learning models, genetic algorithms, computer entertainment technologies, oral pathologies, optimistic motion planning, data analysis for decision making, etc. can be found in this volume. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-10T11:52:59Z No. of bitstreams: 1 ijimai_7_4_0.pdf: 69053 bytes, checksum: a695e4b34ddc8592cf4f4ed7c4ad72c5 (MD5); Made available in DSpace on 2022-10-10T11:52:59Z (GMT). No. of bitstreams: 1 ijimai_7_4_0.pdf: 69053 bytes, checksum: a695e4b34ddc8592cf4f4ed7c4ad72c5 (MD5)

Optimistic Motion Planning Using Recursive Sub- Sampling: A New Approach to Sampling-Based Motion Planning

Optimistic Motion Planning Using Recursive Sub- Sampling: A New Approach to Sampling-Based Motion Planning Kenye, Lhilo; Kala, Rahul Sampling-based motion planning in the field of robot motion planning has provided an effective approach to finding path for even high dimensional configuration space and with the motivation from the concepts of sampling based-motion planners, this paper presents a new sampling-based planning strategy called Optimistic Motion Planning using Recursive Sub-Sampling (OMPRSS), for finding a path from a source to a destination sanguinely without having to construct a roadmap or a tree. The random sample points are generated recursively and connected by straight lines. Generating sample points is limited to a range and edge connectivity is prioritized based on their distances from the line connecting through the parent samples with the intention to shorten the path. The planner is analysed and compared with some sampling strategies of probabilistic roadmap method (PRM) and the experimental results show agile planning with early convergence. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-10T11:49:55Z No. of bitstreams: 1 ijimai_7_4_8.pdf: 762337 bytes, checksum: a07f5f3fddbfa898a9ef7915ac5a0749 (MD5); Made available in DSpace on 2022-10-10T11:49:55Z (GMT). No. of bitstreams: 1 ijimai_7_4_8.pdf: 762337 bytes, checksum: a07f5f3fddbfa898a9ef7915ac5a0749 (MD5)

ERBM-SE: Extended Restricted Boltzmann Machine for Multi-Objective Single-Channel Speech Enhancement

ERBM-SE: Extended Restricted Boltzmann Machine for Multi-Objective Single-Channel Speech Enhancement Khattak, Muhammad Irfan; Saleem, Nasir; Nawaz, Aamir; Ahmed Almani, Aftab; Umer, Farhana; Verdú, Elena Machine learning-based supervised single-channel speech enhancement has achieved considerable research interest over conventional approaches. In this paper, an extended Restricted Boltzmann Machine (RBM) is proposed for the spectral masking-based noisy speech enhancement. In conventional RBM, the acoustic features for the speech enhancement task are layerwise extracted and the feature compression may result in loss of vital information during the network training. In order to exploit the important information in the raw data, an extended RBM is proposed for the acoustic feature representation and speech enhancement. In the proposed RBM, the acoustic features are progressively extracted by multiple-stacked RBMs during the pre-training phase. The hidden acoustic features from the previous RBM are combined with the raw input data that serve as the new inputs to the present RBM. By adding the raw data to RBMs, the layer-wise features related to the raw data are progressively extracted, that is helpful to mine valuable information in the raw data. The results using the TIMIT database showed that the proposed method successfully attenuated the noise and gained improvements in the speech quality and intelligibility. The STOI, PESQ and SDR are improved by 16.86%, 25.01% and 3.84dB over the unprocessed noisy speech. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-10T11:41:09Z No. of bitstreams: 1 ijimai_7_4_16.pdf: 1617071 bytes, checksum: 924724aff644ee8bb4ddccaf3ab83aff (MD5); Made available in DSpace on 2022-10-10T11:41:09Z (GMT). No. of bitstreams: 1 ijimai_7_4_16.pdf: 1617071 bytes, checksum: 924724aff644ee8bb4ddccaf3ab83aff (MD5)

Predictive Model for Taking Decision to Prevent University Dropout

Predictive Model for Taking Decision to Prevent University Dropout Urbina-Nájera, Argelia B.; Méndez-Ortega, Luis A. Dropout is an educational phenomenon studied for decades due to the diversity of its causes, whose effects fall on society's development. This document presents an experimental study to obtain a predictive model that allows anticipating a university dropout. The study uses 51,497 instances with 26 attributes obtained from social sciences, administrative sciences, and engineering collected from 2010 to 2019. Artificial neural networks and decision trees were implemented as classification algorithms, and also, algorithms of attribute selection and resampling methods were used to balance the main class. The results show that the best performing model was that of Random Forest with a Matthew correlation coefficient of 87.43% against 53.39% obtained by artificial neural networks and 94.34% accuracy by Random Forest. The model has allowed predicting an approximate number of possible dropouts per period, contributing to the involved instances in preventing or reducing dropout in higher education. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-10T11:24:45Z No. of bitstreams: 1 ijimai_7_4_18.pdf: 902273 bytes, checksum: 5b285eb589a27c9f23853cd8fb2adae9 (MD5); Made available in DSpace on 2022-10-10T11:24:45Z (GMT). No. of bitstreams: 1 ijimai_7_4_18.pdf: 902273 bytes, checksum: 5b285eb589a27c9f23853cd8fb2adae9 (MD5)

A Novel Technique to Detect and Track Multiple Objects in Dynamic Video Surveillance Systems

A Novel Technique to Detect and Track Multiple Objects in Dynamic Video Surveillance Systems Adimoolam, M.; Mohan, Senthilkumar; A., John; Srivastava, Gautam Video surveillance is one of the important state of the art systems to be utilized in order to monitor different areas of modern society surveillance like the general public surveillance system, city traffic monitoring system, and forest monitoring system. Hence, surveillance systems have become especially relevant in the digital era. The needs of the video surveillance systems and its video analytics have become inevitable due to an increase in crimes and unethical behavior. Thus enabling the tracking of individuals object in video surveillance is an essential part of modern society. With the advent of video surveillance, performance measures for such surveillance also need to be improved to keep up with the ever increasing crime rates. So far, many methodologies relating to video surveillance have been introduced ranging from single object detection with a single or multiple cameras to multiple object detection using single or multiple cameras. Despite this, performance benchmarks and metrics need further improvements. While mechanisms exist for single or multiple object detection and prediction on videos or images, none can meet the criteria of detection and tracking of multiple objects in static as well as dynamic environments. Thus, real-world multiple object detection and prediction systems need to be introduced that are both accurate as well as fast and can also be adopted in static and dynamic environments. This paper introduces the Densely Feature selection Convolutional neural Network – Hyper Parameter tuning (DFCNHP) and it is a hybrid protocol with faster prediction time and high accuracy levels. The proposed system has successfully tracked multiple objects from multiple channels and is a combination of dense block, feature selection, background subtraction and Bayesian methods. The results of the experiment conducted demonstrated an accuracy of 98% and 1.11 prediction time and these results have also been compared with existing methods such as Kalman Filtering (KF) and Deep Neural Network (DNN). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-10T11:18:19Z No. of bitstreams: 1 ijimai_7_4_10.pdf: 990877 bytes, checksum: b9671b8b61f9af6d09f47c0ac796232a (MD5); Made available in DSpace on 2022-10-10T11:18:19Z (GMT). No. of bitstreams: 1 ijimai_7_4_10.pdf: 990877 bytes, checksum: b9671b8b61f9af6d09f47c0ac796232a (MD5)

Automatic Finding Trapezoidal Membership Functions in Mining Fuzzy Association Rules Based on Learning Automata

Automatic Finding Trapezoidal Membership Functions in Mining Fuzzy Association Rules Based on Learning Automata Anari, Z.; Hatamlou, A.; Anari, B. Association rule mining is an important data mining technique used for discovering relationships among all data items. Membership functions have a significant impact on the outcome of the mining association rules. An important challenge in fuzzy association rule mining is finding an appropriate membership functions, which is an optimization issue. In the most relevant studies of fuzzy association rule mining, only triangle membership functions are considered. This study, as the first attempt, used a team of continuous action-set learning automata (CALA) to find both the appropriate number and positions of trapezoidal membership functions (TMFs). The spreads and centers of the TMFs were taken into account as parameters for the research space and a new approach for the establishment of a CALA team to optimize these parameters was introduced. Additionally, to increase the convergence speed of the proposed approach and remove bad shapes of membership functions, a new heuristic approach has been proposed. Experiments on two real data sets showed that the proposed algorithm improves the efficiency of the extracted rules by finding optimized membership functions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-10T10:55:42Z No. of bitstreams: 1 ijimai_7_4_3.pdf: 2909923 bytes, checksum: 99e7b28cabb8d299891af90b3a8ce380 (MD5); Made available in DSpace on 2022-10-10T10:55:42Z (GMT). No. of bitstreams: 1 ijimai_7_4_3.pdf: 2909923 bytes, checksum: 99e7b28cabb8d299891af90b3a8ce380 (MD5)

Towards a Robust Thermal-Visible Heterogeneous Face Recognition Approach Based on a Cycle Generative Adversarial Network

Towards a Robust Thermal-Visible Heterogeneous Face Recognition Approach Based on a Cycle Generative Adversarial Network Kamel Benamara, Nadir; Zigh, Ehlem; Boudghene Stambouli, Tarik; Keche, Mokhtar Security is a sensitive area that concerns all authorities around the world due to the emerging terrorism phenomenon. Contactless biometric technologies such as face recognition have grown in interest for their capacity to identify probe subjects without any human interaction. Since traditional face recognition systems use visible spectrum sensors, their performances decrease rapidly when some visible imaging phenomena occur, mainly illumination changes. Unlike the visible spectrum, Infrared spectra are invariant to light changes, which makes them an alternative solution for face recognition. However, in infrared, the textural information is lost. We aim, in this paper, to benefit from visible and thermal spectra by proposing a new heterogeneous face recognition approach. This approach includes four scientific contributions. The first one is the annotation of a thermal face database, which has been shared via Github with all the scientific community. The second is the proposition of a multi-sensors face detector model based on the last YOLO v3 architecture, able to detect simultaneously faces captured in visible and thermal images. The third contribution takes up the challenge of modality gap reduction between visible and thermal spectra, by applying a new structure of CycleGAN, called TV-CycleGAN, which aims to synthesize visible-like face images from thermal face images. This new thermal-visible synthesis method includes all extreme poses and facial expressions in color space. To show the efficacy and the robustness of the proposed TV-CycleGAN, experiments have been applied on three challenging benchmark databases, including different real-world scenarios: TUFTS and its aligned version, NVIE and PUJ. The qualitative evaluation shows that our method generates more realistic faces. The quantitative one demonstrates that the proposed TV -CycleGAN gives the best improvement on face recognition rates. Therefore, instead of applying a direct matching from thermal to visible images which allows a recognition rate of 47,06% for TUFTS Database, a proposed TV-CycleGAN ensures accuracy of 57,56% for the same database. It contributes to a rate enhancement of 29,16%, and 15,71% for NVIE and PUJ databases, respectively. It reaches an accuracy enhancement of 18,5% for the aligned TUFTS database. It also outperforms some recent state of the art methods in terms of F1-Score, AUC/EER and other evaluation metrics. Furthermore, it should be mentioned that the obtained visible synthesized face images using TV-CycleGAN method are very promising for thermal facial landmark detection as a fourth contribution of this paper. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-10T10:48:59Z No. of bitstreams: 1 ijimai_7_4_12.pdf: 2334388 bytes, checksum: 8b23aedc9101f894bb60fa7392804bbd (MD5); Made available in DSpace on 2022-10-10T10:48:59Z (GMT). No. of bitstreams: 1 ijimai_7_4_12.pdf: 2334388 bytes, checksum: 8b23aedc9101f894bb60fa7392804bbd (MD5)

Social Relations and Methods in Recommender Systems: A Systematic Review

Social Relations and Methods in Recommender Systems: A Systematic Review Medel, Diego; González-González, Carina; V. Aciar, Silvana With the constant growth of information, data sparsity problems, and cold start have become a complex problem in obtaining accurate recommendations. Currently, authors consider the user's historical behavior and find contextual information about the user, such as social relationships, time information, and location. In this work, a systematic review of the literature on recommender systems that use the information on social relationships between users was carried out. As the main findings, social relations were classified into three groups: trust, friend activities, and user interactions. Likewise, the collaborative filtering approach was the most used, and with the best results, considering the methods based on memory and model. The most used metrics that we found, and the recommendation methods studied in mobile applications are presented. The information provided by this study can be valuable to increase the precision of the recommendations. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-10T10:34:17Z No. of bitstreams: 1 ijimai_7_4_1.pdf: 420403 bytes, checksum: 2eb5843e3904855c004b7899a5fde684 (MD5); Made available in DSpace on 2022-10-10T10:34:17Z (GMT). No. of bitstreams: 1 ijimai_7_4_1.pdf: 420403 bytes, checksum: 2eb5843e3904855c004b7899a5fde684 (MD5)

Towards the Grade’s Prediction. A Study of Different Machine Learning Approaches to Predict Grades from Student Interaction Data

Towards the Grade’s Prediction. A Study of Different Machine Learning Approaches to Predict Grades from Student Interaction Data Alonso-Misol Gerlache, Héctor; Moreno-Ger, Pablo; de-la-Fuente-Valentín, Luis There is currently an open problem within the field of Artificial Intelligence applied to the educational field, which is the prediction of students’ grades. This problem aims to predict early school failure and dropout, and to determine the well-founded analysis of student performance for the improvement of educational quality. This document deals the problem of predicting grades of UNIR university master’s degree students in the on-line mode, proposing a working model and comparing different technologies to determine which one fits best with the available data set. In order to make the predictions, the dataset was submitted to a cleaning and analysis phases, being prepared for the use of Machine Learning algorithms, such as Naive Bayes, Decision Tree, Random Forest and Neural Networks. A comparison is made that addresses a double prediction on a homogeneous set of input data, predicting the final grade per subject and the final master’s degree grade. The results were obtained demonstrate that the use of these techniques makes possible the grade predictions. The data gives some figures in which we can see how Artificial Intelligence is able to predict situations with an accuracy above 96%. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-10T10:27:59Z No. of bitstreams: 1 ijimai_7_4_17.pdf: 951607 bytes, checksum: 7f940735fa1bb87d7f4622b3cc05daae (MD5); Made available in DSpace on 2022-10-10T10:27:59Z (GMT). No. of bitstreams: 1 ijimai_7_4_17.pdf: 951607 bytes, checksum: 7f940735fa1bb87d7f4622b3cc05daae (MD5)

LIPSNN: A Light Intrusion-Proving Siamese Neural Network Model for Facial Verification

LIPSNN: A Light Intrusion-Proving Siamese Neural Network Model for Facial Verification Alcaide, Asier; Patricio, Miguel A.; Berlanga, Antonio; Arroyo, Angel; Cuadrado Gallego, Juan J. Facial verification has experienced a breakthrough in recent years, not only due to the improvement in accuracy of the verification systems but also because of their increased use. One of the main reasons for this has been the appearance and use of new models of Deep Learning to address this problem. This extension in the use of facial verification has had a high impact due to the importance of its applications, especially on security, but the extension of its use could be significantly higher if the problem of the required complex calculations needed by the Deep Learning models, that usually need to be executed on machines with specialised hardware, were solved. That would allow the use of facial verification to be extended, making it possible to run this software on computers with low computing resources, such as Smartphones or tablets. To solve this problem, this paper presents the proposal of a new neural model, called Light Intrusion-Proving Siamese Neural Network, LIPSNN. This new light model, which is based on Siamese Neural Networks, is fully presented from the description of its two block architecture, going through its development, including its training with the well- known dataset Labeled Faces in the Wild, LFW; to its benchmarking with other traditional and deep learning models for facial verification in order to compare its performance for its use in low computing resources systems for facial recognition. For this comparison the attribute parameters, storage, accuracy and precision have been used, and from the results obtained it can be concluded that the LIPSNN can be an alternative to the existing models to solve the facet problem of running facial verification in low computing resource devices. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-10T10:21:24Z No. of bitstreams: 1 ijimai_7_4_11.pdf: 666771 bytes, checksum: 2b1ac5da5fc7c530be14580f1b8cf2a2 (MD5); Made available in DSpace on 2022-10-10T10:21:24Z (GMT). No. of bitstreams: 1 ijimai_7_4_11.pdf: 666771 bytes, checksum: 2b1ac5da5fc7c530be14580f1b8cf2a2 (MD5)

Multimodal Human Eye Blink Recognition Using Z-score Based Thresholding and Weighted Features

Multimodal Human Eye Blink Recognition Using Z-score Based Thresholding and Weighted Features Singh Lamba, Puneet; Virmani, Deepali; Pillai, Manu S.; Chaudhary, Gopal A novel real-time multimodal eye blink detection method using an amalgam of five unique weighted features extracted from the circle boundary formed from the eye landmarks is proposed. The five features, namely (Vertical Head Positioning, Orientation Factor, Proportional Ratio, Area of Intersection, and Upper Eyelid Radius), provide imperative gen (z score threshold) accurately predicting the eye status and thus the blinking status. An accurate and precise algorithm employing the five weighted features is proposed to predict eye status (open/close). One state-of-the-art dataset ZJU (eye-blink), is used to measure the performance of the method. Precision, recall, F1-score, and ROC curve measure the proposed method performance qualitatively and quantitatively. Increased accuracy (of around 97.2%) and precision (97.4%) are obtained compared to other existing unimodal approaches. The efficiency of the proposed method is shown to outperform the state-of-the-art methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-07T09:48:08Z No. of bitstreams: 1 ijimai_7_4_9.pdf: 1644982 bytes, checksum: aa8f04a311082d578e06a4b2e090bb6f (MD5); Made available in DSpace on 2022-10-07T09:48:08Z (GMT). No. of bitstreams: 1 ijimai_7_4_9.pdf: 1644982 bytes, checksum: aa8f04a311082d578e06a4b2e090bb6f (MD5)

Obtaining Anti-Missile Decoy Launch Solution from a Ship Using Machine Learning Techniques

Obtaining Anti-Missile Decoy Launch Solution from a Ship Using Machine Learning Techniques Touza, Ramón; Martínez Torres, Javier; Álvarez, María; Roca, Javier One of the most dangerous situations a warship may face is a missile attack launched from other ships, aircrafts, submarines or land. In addition, given the current scenario, it is not ruled out that a terrorist group may acquire missiles and use them against ships operating close to the coast, which increases their vulnerabilitydue to the limited reaction time. One of the means the ship has for its defense are decoys, designed to deceive the enemy missile. However, for their use to be effective it is necessary to obtain, in a quick way, a valid launching solution. The purpose of this article is to design a methodology to solve the problem of decoy launching and to provide the ship immediately with the necessary data to make the firing decision. To solve the problem machine learning models (neural networks and support vector machines) and a set of training data obtained in simulations will be used. The performance measures obtained with the implementation of multilayer perceptron models allow the replacement of the current procedures based on tables and launching rules with machine learning algorithms that are more flexible and adaptable to a larger number of scenarios. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-07T09:37:22Z No. of bitstreams: 1 ijimai_7_4_14.pdf: 1088848 bytes, checksum: 5bcc4cde49dc064dda3c1146ecf9f6e4 (MD5); Made available in DSpace on 2022-10-07T09:37:22Z (GMT). No. of bitstreams: 1 ijimai_7_4_14.pdf: 1088848 bytes, checksum: 5bcc4cde49dc064dda3c1146ecf9f6e4 (MD5)

Automatic Classification of Oral Pathologies Using Orthopantomogram Radiography Images Based on Convolutional Neural Network

Automatic Classification of Oral Pathologies Using Orthopantomogram Radiography Images Based on Convolutional Neural Network Laishram, Anuradha; Thongam, Khelchandra An attempt has been made to device a robust method to classify different oral pathologies using Orthopantomogram (OPG) images based on Convolutional Neural Network (CNN). This system will provide a novel approach for the classification of types of teeth (viz., incisors and molar teeth) and also some underlying oral anomalies such as fixed partial denture (cap) and impacted teeth. To this end, various image preprocessing techniques are performed. The input OPG images are resized, pixels are scaled and erroneous data are excluded. The proposed algorithm is implemented using CNN with Dropout and the fully connected layer has been trained using hybrid GA-BP learning. Using the Dropout regularization technique, over fitting has been avoided and thereby making the network to correctly classify the objects. The CNN has been implemented with different convolutional layers and the highest accuracy of 97.92% has been obtained with two convolutional layers. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-07T09:29:14Z No. of bitstreams: 1 ijimai_7_4_6.pdf: 928867 bytes, checksum: c993b0942ed94e191524a4b12964d850 (MD5); Made available in DSpace on 2022-10-07T09:29:14Z (GMT). No. of bitstreams: 1 ijimai_7_4_6.pdf: 928867 bytes, checksum: c993b0942ed94e191524a4b12964d850 (MD5)

Improving Pipelining Tools for Pre-processing Data

Improving Pipelining Tools for Pre-processing Data Novo-Lourés, María; Lage, Yeray; Pavón, Reyes; Laza, Rosalía; Ruano-Ordás, David; Méndez, José Ramón The last several years have seen the emergence of data mining and its transformation into a powerful tool that adds value to business and research. Data mining makes it possible to explore and find unseen connections between variables and facts observed in different domains, helping us to better understand reality. The programming methods and frameworks used to analyse data have evolved over time. Currently, the use of pipelining schemes is the most reliable way of analysing data and due to this, several important companies are currently offering this kind of services. Moreover, several frameworks compatible with different programming languages are available for the development of computational pipelines and many research studies have addressed the optimization of data processing speed. However, as this study shows, the presence of early error detection techniques and developer support mechanisms is very limited in these frameworks. In this context, this study introduces different improvements, such as the design of different types of constraints for the early detection of errors, the creation of functions to facilitate debugging of concrete tasks included in a pipeline, the invalidation of erroneous instances and/or the introduction of the burst-processing scheme. Adding these functionalities, we developed Big Data Pipelining for Java (BDP4J, https://github.com/sing-group/bdp4j), a fully functional new pipelining framework that shows the potential of these features. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-07T08:59:09Z No. of bitstreams: 1 ijimai_7_4_19.pdf: 796775 bytes, checksum: 2f9a55a24d7cdaefca917b6c0cd3a578 (MD5); Made available in DSpace on 2022-10-07T08:59:09Z (GMT). No. of bitstreams: 1 ijimai_7_4_19.pdf: 796775 bytes, checksum: 2f9a55a24d7cdaefca917b6c0cd3a578 (MD5)

MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network

MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network Deore, Mahendra; Kulkarni, Uday Technological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical devices. On the other hand, there exists an increased ‘Threat’ of an ‘Attack’. Attackers are targeting these devices, as it may provide an easier ‘backdoor entry to the users’ network’.MALicious softWARE (MalWare) is a major threat to user security. Fast and accurate detection of malware attacks are the sine qua non of IoE, where large scale networking is involved. The paper proposes use of a visualization technique where the disassembled malware code is converted into gray images, as well as use of Image Similarity based Statistical Parameters (ISSP) such as Normalized Cross correlation (NCC), Average difference (AD), Maximum difference (MaxD), Singular Structural Similarity Index Module (SSIM), Laplacian Mean Square Error (LMSE), MSE and PSNR. A vector consisting of gray image with statistical parameters is trained using a Faster Region proposals Convolution Neural Network (F-RCNN) classifier. The experiment results are promising as the proposed method includes ISSP with F-RCNN training. Overall training time of learning the semantics of higher-level malicious behaviors is less. Identification of malware (testing phase) is also performed in less time. The fusion of image and statistical parameter enhances system performance with greater accuracy. The benchmark database from Microsoft Malware Classification challenge has been used to analyze system performance, which is available on the Kaggle website. An overall average classification accuracy of 98.12% is achieved by the proposed method. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-07T08:51:31Z No. of bitstreams: 1 ijimai_7_4_13.pdf: 2185179 bytes, checksum: 7e73cb85b1421ad1e8b871a6a5ffd9e9 (MD5); Made available in DSpace on 2022-10-07T08:51:31Z (GMT). No. of bitstreams: 1 ijimai_7_4_13.pdf: 2185179 bytes, checksum: 7e73cb85b1421ad1e8b871a6a5ffd9e9 (MD5)

CDPS-IoT: Cardiovascular Disease Prediction System Based on IoT using Machine Learning

CDPS-IoT: Cardiovascular Disease Prediction System Based on IoT using Machine Learning Ahamed, Jameel; Manan Koli, Abdul; Ahmad, Khaleel; Alam Jamal, Mohd.; Gupta, B. B. Internet of Things, Machine learning, and Cloud computing are the emerging domains of information communication and technology. These techniques can help to save the life of millions in the medical assisted environment and can be utilized in health-care system where health expertise is less available. Fast food consumption increased from the past few decades, which makes up cholesterol, diabetes, and many more problems that affect the heart and other organs of the body. Changing lifestyle is another parameter that results in health issues including cardio-vascular diseases. Affirming to the World Health Organization, the cardiovascular diseases, or heart diseases lead to more death than any other disease globally. The objective of this research is to analyze the available data pertaining to cardiovascular diseases for prediction of heart diseases at an earlier stage to prevent it from occurring. The dataset of heart disease patients was taken from Jammu and Kashmir, India and stored over the cloud. Stored data is then pre-processed and further analyzed using machine learning techniques for the prediction of heart diseases. The analysis of the dataset using numerous machines learning techniques like Random Forest, Decision Tree, Naive based, K-nearest neighbors, and Support Vector Machine revealed the performance metrics (F1 Score, Precision and Recall) for all the techniques which shows that Naive Bayes is better without parameter tuning while Random Forest algorithm proved as the best technique with hyperparameter tuning. In this paper, the proposed model is developed in such a systematic way that the clinical data can be obtained through the use of IoT with the help of available medical sensors to predict cardiovascular diseases on a real-time basis. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-07T08:26:31Z No. of bitstreams: 1 ijimai_7_4_7.pdf: 503531 bytes, checksum: 7e4b62c1a9e3651de585e8e4a5703f64 (MD5); Made available in DSpace on 2022-10-07T08:26:31Z (GMT). No. of bitstreams: 1 ijimai_7_4_7.pdf: 503531 bytes, checksum: 7e4b62c1a9e3651de585e8e4a5703f64 (MD5)

Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities

Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities Bobadilla, Jesús; Gutiérrez, Abraham; Alonso, Santiago; González-Prieto, Ángel Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just return rating predictions. This paper proposes the use of a classification-based approach, returning both rating predictions and their reliabilities. The extra information (prediction reliabilities) can be used in a variety of relevant collaborative filtering areas such as detection of shilling attacks, recommendations explanation or navigational tools to show users and items dependences. Additionally, recommendation reliabilities can be gracefully provided to users: “probably you will like this film”, “almost certainly you will like this song”, etc. This paper provides the proposed neural architecture; it also tests that the quality of its recommendation results is as good as the state of art baselines. Remarkably, individual rating predictions are improved by using the proposed architecture compared to baselines. Experiments have been performed making use of four popular public datasets, showing generalizable quality results. Overall, the proposed architecture improves individual rating predictions quality, maintains recommendation results and opens the doors to a set of relevant collaborative filtering fields. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-07T08:19:01Z No. of bitstreams: 1 ijimai_7_4_2.pdf: 812772 bytes, checksum: 1d0ba7a63f430bcaefa94dd684e0e3d7 (MD5); Made available in DSpace on 2022-10-07T08:19:01Z (GMT). No. of bitstreams: 1 ijimai_7_4_2.pdf: 812772 bytes, checksum: 1d0ba7a63f430bcaefa94dd684e0e3d7 (MD5)

Research on the Application of Computer Graphic Advertisement Design Based on a Genetic Algorithm and TRIZ Theory

Research on the Application of Computer Graphic Advertisement Design Based on a Genetic Algorithm and TRIZ Theory Song, Yang In view of the shortcomings of the traditional thinking of computer graphic advertising design, this paper introduces TRIZ innovative thinking to design computer advertising. First of all, combined with specific cases of computer creative print advertising, this paper analyzes the creative methods of stimulating divergent thinking, aggregation thinking and transformation thinking from the innovation principle of TRIZ theory as the origin, and applies them to the creative mechanism and application program of print advertising creativity. The whole process is led by rational principles of perceptual thinking, driven by specific principles of abstract imagination, to explore the thinking source of creative design essence of print advertising. The theory and its application mechanism become a new thinking method and application attempt in the creative field of print advertisement. Then, based on the TRIZ innovation theory, the business model of advertising content arrangement is constructed, and the mathematical model is constructed according to the planning business media resource planning on the business model to realize the multi-objective optimization of efficient use of orders and precise delivery of time. Finally, a multi-objective optimization mathematical model of parallel genetic algorithm is designed to solve the advertisement content arrangement. The innovative thinking of TRIZ and the application of genetic algorithm in content arrangement of computer graphic advertisement design are verified by experiments. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-07T08:08:16Z No. of bitstreams: 1 ijimai_7_4_4.pdf: 668673 bytes, checksum: 98d196361d54488f275d18da8a88f9a8 (MD5); Made available in DSpace on 2022-10-07T08:08:16Z (GMT). No. of bitstreams: 1 ijimai_7_4_4.pdf: 668673 bytes, checksum: 98d196361d54488f275d18da8a88f9a8 (MD5)

CompareML: A Novel Approach to Supporting Preliminary Data Analysis Decision Making

CompareML: A Novel Approach to Supporting Preliminary Data Analysis Decision Making Fernández-García, Antonio Jesús; Preciado, Juan Carlos; Prieto, Álvaro E.; Sánchez-Figueroa, Fernando; Gutiérrez, Juan D. There are a large number of machine learning algorithms as well as a wide range of libraries and services that allow one to create predictive models. With machine learning and artificial intelligence playing a major role in dealing with engineering problems, practising engineers often come to the machine learning field so overwhelmed with the multitude of possibilities that they find themselves needing to address difficulties before actually starting on carrying out any work. Datasets have intrinsic properties that make it hard to select the algorithm that is best suited to some specific objective, and the ever-increasing number of providers together make this selection even harder. These were the reasons underlying the design of CompareML, an approach to supporting the evaluation and comparison of machine learning libraries and services without deep machine learning knowledge. CompareML makes it easy to compare the performance of different models by using well-known classification and regression algorithms already made available by some of the most widely used providers. It facilitates the practical application of methods and techniques of artificial intelligence that let a practising engineer decide whether they might be used to resolve hitherto intractable problems. Thus, researchers and engineering practitioners can uncover the potential of their datasets for the inference of new knowledge by selecting the most appropriate machine learning algorithm and determining the provider best suited to their data. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-07T08:02:37Z No. of bitstreams: 1 ijimai_7_4_20.pdf: 1548182 bytes, checksum: 3c51065ee7e9093b4a0eeba4466c21da (MD5); Made available in DSpace on 2022-10-07T08:02:37Z (GMT). No. of bitstreams: 1 ijimai_7_4_20.pdf: 1548182 bytes, checksum: 3c51065ee7e9093b4a0eeba4466c21da (MD5)

Computer Entertainment Technologies for the Visually Impaired: An Overview

Computer Entertainment Technologies for the Visually Impaired: An Overview López Ibáñez, Manuel; Romero-Hernández, Alejandro; Manero, Borja; Guijarro, María Over the last years, works related to accessible technologies have increased both in number and in quality. This work presents a series of articles which explore different trends in the field of accessible video games for the blind or visually impaired. Reviewed articles are distributed in four categories covering the following subjects: (1) video game design and architecture, (2) video game adaptations, (3) accessible games as learning tools or treatments and (4) navigation and interaction in virtual environments. Current trends in accessible game design are also analysed, and data is presented regarding keyword use and thematic evolution over time. As a conclusion, a relative stagnation in the field of human-computer interaction for the blind is detected. However, as the video game industry is becoming increasingly interested in accessibility, new research opportunities are starting to appear. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-07T07:48:35Z No. of bitstreams: 1 ijimai_7_4_5.pdf: 611853 bytes, checksum: de5b9d70e405c0656b23bed7fe0e6913 (MD5); Made available in DSpace on 2022-10-07T07:48:35Z (GMT). No. of bitstreams: 1 ijimai_7_4_5.pdf: 611853 bytes, checksum: de5b9d70e405c0656b23bed7fe0e6913 (MD5)

Writing Order Recovery in Complex and Long Static Handwriting

Writing Order Recovery in Complex and Long Static Handwriting Diaz, Moises; Crispo, Gioele; Parziale, Antonio; Marcelli, Angelo; Ferrer, Miguel A. The order in which the trajectory is executed is a powerful source of information for recognizers. However, there is still no general approach for recovering the trajectory of complex and long handwriting from static images. Complex specimens can result in multiple pen-downs and in a high number of trajectory crossings yielding agglomerations of pixels (also known as clusters). While the scientific literature describes a wide range of approaches for recovering the writing order in handwriting, these approaches nevertheless lack a common evaluation metric. In this paper, we introduce a new system to estimate the order recovery of thinned static trajectories, which allows to effectively resolve the clusters and select the order of the executed pendowns. We evaluate how knowing the starting points of the pen-downs affects the quality of the recovered writing. Once the stability and sensitivity of the system is analyzed, we describe a series of experiments with three publicly available databases, showing competitive results in all cases. We expect the proposed system, whose code is made publicly available to the research community, to reduce potential confusion when the order of complex trajectories are recovered, and this will in turn make the trajectories recovered to be viable for further applications, such as velocity estimation. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-10-07T07:33:24Z No. of bitstreams: 1 ijimai_7_4_15.pdf: 1326471 bytes, checksum: f521c2d83796761e8152f7b955502258 (MD5); Made available in DSpace on 2022-10-07T07:33:24Z (GMT). No. of bitstreams: 1 ijimai_7_4_15.pdf: 1326471 bytes, checksum: f521c2d83796761e8152f7b955502258 (MD5)

Research Collaboration Influence Analysis Using Dynamic Co-authorship and Citation Networks

Research Collaboration Influence Analysis Using Dynamic Co-authorship and Citation Networks Razzaq, Sidra; Kamran Malik, Ahmad; Raza, Basit; Ali Khattak, Hasan; Moscoso Zegarra, Giomar W.; Díaz Zelada, Yvan Collaborative research is increasing in terms of publications, skills, and formal interactions, which certainly makes it the hotspot in both academia and the industrial sector. Knowing the factors and behavior of dynamic collaboration network provides insights that helps in improving the researcher’s profile and coordinator’s productivity of research. Despite rapid developments in the research collaboration process with various outcomes, its validity is still difficult to address. Existing approaches have used bibliometric network analysis with different aspects to understand collaboration patterns that measure the quality of their corresponding relationships. At this point in time, we would like to investigate an efficient method to outline the credibility of findings in publication—author relations. In this research, we propose a new collaboration method to analyze the structure of research articles using four types of graphs for discerning authors’ influence. We apply different combinations of network relationships and bibliometric analysis on the G-index parameter to disclose their interrelated differences. Our model is designed to find the dynamic indicators of co-authored collaboration with an influence on the author’s behavior in terms of change in research area/interest. In the research we investigate the dynamic relations in an academic field using metadata of openly available articles and collaborating international authors in interrelated areas/domains. Based on filtered evidence of relationship networks and their statistical results, the research shows an increment in productivity and better influence over time. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-23T08:56:54Z No. of bitstreams: 1 ijimai7_3_10_0.pdf: 1917831 bytes, checksum: 18756ee4e984bc423bb9c79843b0ee00 (MD5); Made available in DSpace on 2022-05-23T08:56:54Z (GMT). No. of bitstreams: 1 ijimai7_3_10_0.pdf: 1917831 bytes, checksum: 18756ee4e984bc423bb9c79843b0ee00 (MD5)

Machine Learning in Business Intelligence 4.0: Cost Control in a Destination Hotel

Machine Learning in Business Intelligence 4.0: Cost Control in a Destination Hotel Sánchez-Torres, Fulgencio; González, Iván; Dobrescu, Cosmin C. Cost control is a recurring problem in companies where studies have provided different solutions. The main objective of this research is to propose and validate an alternative to cost control using data science to support decision-making using the business intelligence 4.0 paradigm. The work uses Machine Learning (ML) to support decision-making in company cost-control management. Specifically, we used the ability of hierarchical agglomerative clustering (HAC) algorithms to generate clusters and suggest possible candidate products that could be substituted for other, more cost-effective ones. These candidate products were analyzed by a panel of company experts, facilitating decisions based on business costs. We needed to analyze and modify the company's ecosystem and its associated variables to obtain an adequate data warehouse during the study, which was developed over three years and validated HAC as a support to decision-making in cost control. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-23T08:50:20Z No. of bitstreams: 1 ijimai7_3_8_0.pdf: 662619 bytes, checksum: e14c1821a0007cbf28e6fe80ba05e3d4 (MD5); Made available in DSpace on 2022-05-23T08:50:20Z (GMT). No. of bitstreams: 1 ijimai7_3_8_0.pdf: 662619 bytes, checksum: e14c1821a0007cbf28e6fe80ba05e3d4 (MD5)

Editor's Note

Editor's Note Golpe, Antonio A.; Isasi, Pedro; Martín-Álvarez, Juan Manuel; Quintana, David Machine learning (ML) is generating new opportunities for innovative research in areas apparently unrelated such as, economics, business or/and finance. Specifically, it has also been widely used in applications related to the economic and financial analysis, such as economic recessions prediction, labor market trends, risk management, prices analysis among others. However, it is important to note the differences between classical statistics/econometrics and machine learning. On the one hand, econometrics set out to build models designed to describe economic problems, while machine learning uses algorithms, generally for prediction, classification, and also, can manage a large amount of structured and unstructured data and make fast decisions or forecasts. As S. Athey points out, perhaps “a key advantage of ML is that it frames empirical analysis in terms of algorithms that estimate and compare many alternative models. This approach contrasts with econometrics, where (in principle, though rarely in reality) the researcher picks a model based on principles and estimates it once”. This Special Issue presents nine contributions that illustrate both approaches in the domain of economics, finance and business. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-23T08:12:34Z No. of bitstreams: 1 ijimai7_3_0.pdf: 58542 bytes, checksum: 521ed0728cdb1a2bd1e04486981d07b4 (MD5); Made available in DSpace on 2022-05-23T08:12:34Z (GMT). No. of bitstreams: 1 ijimai7_3_0.pdf: 58542 bytes, checksum: 521ed0728cdb1a2bd1e04486981d07b4 (MD5)

Foreword

Foreword Velarde Molina, Jehovanni Fabricio This time, in the Special Issue on Artificial Intelligence in Economics, Finance and Business, we present a series of publications focused on artificial intelligence and finance. This compilation of research will bring new information to researchers in different disciplines, and at the same time, it will be an ideal space to present studies that have an international scope. UNIR, dedicated to the training of professionals in different academic programs, through its journal is consolidating a culture of research and expanding the knowledge that contributes to an excellent education. For this reason, we consider the dissemination of scientific articles essential, since this guarantees the transfer of results, in addition to the conclusions of high-impact research. Currently the world is going through a complicated scenario, a fluctuating economy and problems in health services that require immediate attention; in this sense, science and knowledge management open space to opportunities in search of medium and long-term solutions. It is a great honor to present this issue of the International Journal of Interactive Multimedia and Artificial Intelligence, whose contribution to the knowledge society is invaluable. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-23T07:57:14Z No. of bitstreams: 1 ijimai7_3_00.pdf: 59365 bytes, checksum: 8d7e35f96ee9570c6b66e9776f9b0049 (MD5); Made available in DSpace on 2022-05-23T07:57:14Z (GMT). No. of bitstreams: 1 ijimai7_3_00.pdf: 59365 bytes, checksum: 8d7e35f96ee9570c6b66e9776f9b0049 (MD5)

Finite Sample Properties of Parameterized Expectations Algorithm Solutions; Is the Length So Determinant?

Finite Sample Properties of Parameterized Expectations Algorithm Solutions; Is the Length So Determinant? Sánchez-Fuentes, A. Jesús The solution of the Parameterized Expectations Algorithm (PEA) is well defined based on asymptotic properties. In practice, it depends on the specific replication of the exogenous shock(s) used for the resolution process. Typically, this problem is reduced when a sufficiently long replication is considered. In this paper, we suggest an alternative approach which consists of using several, shorter replications. A centrality measure (the median) is used then to discriminate among the different solutions using two different criteria, which differ in the information used. On the one hand, the distance to the vector composed by median values of PEA coefficients is minimized. On the other hand, distances to the median impulse response is minimized. Finally, we explore the impact of considering alternative approaches in an empirical illustration. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-23T07:53:55Z No. of bitstreams: 1 ijimai7_3_3.pdf: 1717721 bytes, checksum: 24b382e746f4eb2114f3cea8942e70e7 (MD5); Made available in DSpace on 2022-05-23T07:53:55Z (GMT). No. of bitstreams: 1 ijimai7_3_3.pdf: 1717721 bytes, checksum: 24b382e746f4eb2114f3cea8942e70e7 (MD5)

The Yield Curve as a Recession Leading Indicator. An Application for Gradient Boosting and Random Forest

The Yield Curve as a Recession Leading Indicator. An Application for Gradient Boosting and Random Forest Cadahia Delgado, Pedro; Congregado, Emilio; Golpe, Antonio A.; Vides, José Carlos Most representative decision-tree ensemble methods have been used to examine the variable importance of Treasury term spreads to predict US economic recessions with a balance of generating rules for US economic recession detection. A strategy is proposed for training the classifiers with Treasury term spreads data and the results are compared in order to select the best model for interpretability. We also discuss the use of SHapley Additive exPlanations (SHAP) framework to understand US recession forecasts by analyzing feature importance. Consistently with the existing literature we find the most relevant Treasury term spreads for predicting US economic recession and a methodology for detecting relevant rules for economic recession detection. In this case, the most relevant term spread found is 3-month–6-month, which is proposed to be monitored by economic authorities. Finally, the methodology detected rules with high lift on predicting economic recession that can be used by these entities for this propose. This latter result stands in contrast to a growing body of literature demonstrating that machine learning methods are useful for interpretation comparing many alternative algorithms and we discuss the interpretation for our result and propose further research lines aligned with this work. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-20T10:35:56Z No. of bitstreams: 1 ijimai7_3_1.pdf: 1003194 bytes, checksum: ac975a0f6cfa3fb863045bc8c7aeed35 (MD5); Made available in DSpace on 2022-05-20T10:35:56Z (GMT). No. of bitstreams: 1 ijimai7_3_1.pdf: 1003194 bytes, checksum: ac975a0f6cfa3fb863045bc8c7aeed35 (MD5)

Comparative Analysis of Building Insurance Prediction Using Some Machine Learning Algorithms

Comparative Analysis of Building Insurance Prediction Using Some Machine Learning Algorithms Ejiyi, Chukwuebuka Joseph; Qin, Zhen; Salako, Abdulhaq Adetunji; Happy, Monday Nkanta; Nneji, Grace Ugochi; Ukwuoma, Chiagoziem Chima; Chikwendu, Ijeoma Amuche; Gen, Ji In finance and management, insurance is a product that tends to reduce or eliminate in totality or partially the loss caused due to different risks. Various factors affect house insurance claims, some of which contribute to formulating insurance policies including specific features that the house has. Machine Learning (ML) when brought into the field of insurance would enable seamless formulation of insurance policies with a better performance which will also save time. Various classification algorithms have been used since they have a long history and have also got some modifications for optimum functionality. To illustrate the performance of each of the ML algorithms that we used here, we analyzed an insurance dataset drawn from Zindi Africa competition which is said to be from Olusola Insurance Company in Lagos Nigeria. This study therefore, compares the performance of Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), Kernel Support Vector Machine (kSVM), Naïve Bayes (NB), and Random Forest (RF) Regressors on a dataset got from Zindi.africa competition and their performances are checked using not only accuracy and precision metrics but also recall, and F1 score metrics, all displayed on the confusion matrix. The accuracy result shows that logistic regression and Kernel SVM both gave 78% but kSVM outperformed LR in precision with a percentage of 70.8% for kSVM and 64.8% for LR showing that kSVM offered the best result. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-20T09:53:30Z No. of bitstreams: 1 ijimai7_3_7.pdf: 543039 bytes, checksum: c8857d0579cf9c51fddf8466a5e61ba2 (MD5); Made available in DSpace on 2022-05-20T09:53:30Z (GMT). No. of bitstreams: 1 ijimai7_3_7.pdf: 543039 bytes, checksum: c8857d0579cf9c51fddf8466a5e61ba2 (MD5)

An Ensemble Classifier for Stock Trend Prediction Using Sentence-Level Chinese News Sentiment and Technical Indicators

An Ensemble Classifier for Stock Trend Prediction Using Sentence-Level Chinese News Sentiment and Technical Indicators Chen, Chun-Hao; Chen, Po-Yeh; Chun-Wei Lin, Jerry In the financial market, predicting stock trends based on stock market news is a challenging task, and researchers are devoted to developing forecasting models. From the existing literature, the performance of the forecasting model is better when news sentiment and technical analysis are considered than when only one of them is used. However, analyzing news sentiment for trend forecasting is a difficult task, especially for Chinese news, because it is unstructured data and extracting the most important features is difficult. Moreover, positive or negative news does not always affect stock prices in a certain way. Therefore, in this paper, we propose an approach to build an ensemble classifier using sentiment in Chinese news at sentence level and technical indicators to predict stock trends. In the training stages, we first divide each news item into a set of sentences. TextRank and word2vec are then used to generate a predefined number of key sentences. The sentiment scores of these key sentences are computed using the given financial lexicon. The sentiment values of the key phrases, the three values of the technical indicators and the stock trend label are merged as a training instance. Based on the sentiment values of the key sets, the corpora are divided into positive and negative news datasets. The two datasets formed are then used to build positive and negative stock trend prediction models using the support vector machine. To increase the reliability of the prediction model, a third classifier is created using the Bollinger Bands. These three classifiers are combined to form an ensemble classifier. In the testing phase, a voting mechanism is used with the trained ensemble classifier to make the final decision based on the trading signals generated by the three classifiers. Finally, experiments were conducted on five years of news and stock prices of one company to show the effectiveness of the proposed approach, and results show that the accuracy and P / L ratio of the proposed approach are 61% and 4.0821 are better than the existing approach. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-20T09:00:06Z No. of bitstreams: 1 ijimai7_3_5.pdf: 786085 bytes, checksum: d6a9561d04496b63178d717866109231 (MD5); Made available in DSpace on 2022-05-20T09:00:06Z (GMT). No. of bitstreams: 1 ijimai7_3_5.pdf: 786085 bytes, checksum: d6a9561d04496b63178d717866109231 (MD5)

AWS PredSpot: Machine Learning for Predicting the Price of Spot Instances in AWS Cloud

AWS PredSpot: Machine Learning for Predicting the Price of Spot Instances in AWS Cloud Baldominos Gómez, Alejandro; Saez, Yago; Quintana, David; Isasi, Pedro Elastic Cloud Compute (EC2) is one of the most well-known services provided by Amazon for provisioning cloud computing resources, also known as instances. Besides the classical on-demand scheme, where users purchase compute capacity at a fixed cost, EC2 supports so-called spot instances, which are offered following a bidding scheme, where users can save up to 90% of the cost of the on-demand instance. EC2 spot instances can be a useful alternative for attaining an important reduction in infrastructure cost, but designing bidding policies can be a difficult task, since bidding under their cost will either prevent users from provisioning instances or losing those that they already own. Towards this extent, accurate forecasting of spot instance prices can be of an outstanding interest for designing working bidding policies. In this paper, we propose the use of different machine learning techniques to estimate the future price of EC2 spot instances. These include linear, ridge and lasso regressions, multilayer perceptrons, K-nearest neighbors, extra trees and random forests. The obtained performance varies significantly between instances types, and root mean squared errors ranges between values very close to zero up to values over 60 in some of the most expensive instances. Still, we can see that for most of the instances, forecasting performance is remarkably good, encouraging further research in this field of study. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-20T08:30:51Z No. of bitstreams: 1 ijimai7_3_6.pdf: 791834 bytes, checksum: ff2030e80f5bb96f91d3b2fbc0e9feef (MD5); Made available in DSpace on 2022-05-20T08:30:51Z (GMT). No. of bitstreams: 1 ijimai7_3_6.pdf: 791834 bytes, checksum: ff2030e80f5bb96f91d3b2fbc0e9feef (MD5)

A Comparative Analysis of Machine Learning Models for Banking News Extraction by Multiclass Classification With Imbalanced Datasets of Financial News: Challenges and Solutions

A Comparative Analysis of Machine Learning Models for Banking News Extraction by Multiclass Classification With Imbalanced Datasets of Financial News: Challenges and Solutions Dogra, Varun; Verma, Sahil; Verma, Kavita; Jhanjhi, NZ; Ghosh, Uttam; Le, Dac-Nhuong Online portals provide an enormous amount of news articles every day. Over the years, numerous studies have concluded that news events have a significant impact on forecasting and interpreting the movement of stock prices. The creation of a framework for storing news-articles and collecting information for specific domains is an important and untested problem for the Indian stock market. When online news portals produce financial news articles about many subjects simultaneously, finding news articles that are important to the specific domain is nontrivial. A critical component of the aforementioned system should, therefore, include one module for extracting and storing news articles, and another module for classifying these text documents into a specific domain(s). In the current study, we have performed extensive experiments to classify the financial news articles into the predefined four classes Banking, Non-Banking, Governmental, and Global. The idea of multi-class classification was to extract the Banking news and its most correlated news articles from the pool of financial news articles scraped from various web news portals. The news articles divided into the mentioned classes were imbalanced. Imbalance data is a big difficulty with most classifier learning algorithms. However, as recent works suggest, class imbalances are not in themselves a problem, and degradation in performance is often correlated with certain variables relevant to data distribution, such as the existence in noisy and ambiguous instances in the adjacent class boundaries. A variety of solutions to addressing data imbalances have been proposed recently, over-sampling, down-sampling, and ensemble approach. We have presented the various challenges that occur with data imbalances in multiclass classification and solutions in dealing with these challenges. The paper has also shown a comparison of the performances of various machine learning models with imbalanced data and data balances using sampling and ensemble techniques. From the result, it’s clear that the performance of Random Forest classifier with data balances using the over-sampling technique SMOTE is best in terms of precision, recall, F-1, and accuracy. From the ensemble classifiers, the Balanced Bagging classifier has shown similar results as of the Random Forest classifier with SMOTE. Random forest classifier's accuracy, however, was 100% and it was 99% with the Balanced Bagging classifier. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-20T08:12:13Z No. of bitstreams: 1 ijimai7_3_4.pdf: 763331 bytes, checksum: bc3ebf945c8c25485f193bf223598f87 (MD5); Made available in DSpace on 2022-05-20T08:12:13Z (GMT). No. of bitstreams: 1 ijimai7_3_4.pdf: 763331 bytes, checksum: bc3ebf945c8c25485f193bf223598f87 (MD5)

Using Customer Knowledge Surveys to Explain Sales of Postgraduate Programs: A Machine Learning Approach

Using Customer Knowledge Surveys to Explain Sales of Postgraduate Programs: A Machine Learning Approach Asensio, Eva; Almeida, Alejandro; Galiano, Aida; Martín-Álvarez, Juan Manuel Universities collect information from each customer that contacts them through their websites and social media profiles. Customer knowledge surveys are the main information-gathering tool used to obtain this information about potential students. In this paper, we propose using the information gained via surveys along with enrolment databases, to group customers into homogeneous clusters in order to identify target customers who are more likely to enroll. The use of such a cluster strategy will increase the probability of converting contacts into customers and will allow the marketing and admission departments to focus on those customers with a greater probability of enrolling, thereby increasing efficiency. The specific characteristics of each cluster and those postgraduate programs that are more likely to be selected are identified. In addition, better insight into customers regarding their enrolment choices thanks to our cluster strategy, will allow universities to personalize their services resulting in greater satisfaction and, consequently, in increased future enrolment. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-20T07:48:35Z No. of bitstreams: 1 ijimai7_3_9.pdf: 745086 bytes, checksum: a0984f3cd37a6e5e718556a73abeaa0d (MD5); Made available in DSpace on 2022-05-20T07:48:35Z (GMT). No. of bitstreams: 1 ijimai7_3_9.pdf: 745086 bytes, checksum: a0984f3cd37a6e5e718556a73abeaa0d (MD5)

Re-Evaluating the Relationship Between Economic Development and Self-Employment, at the Macro-Level: A Bayesian Model Averaging Approach

Re-Evaluating the Relationship Between Economic Development and Self-Employment, at the Macro-Level: A Bayesian Model Averaging Approach Rodriguez-Santiago, Ana We re-evaluate the relationship between stages of economic development and entrepreneurship, at the macro level. We first conduct a literature review of previous empirical research on cross-country determinants of entrepreneurship in order to put our contribution in perspective. To circumvent problems related to model uncertainty we use Bayesian Model Averaging (BMA) to evaluate the robustness of determinants of economic growth in a new dataset of 117 countries in the 2005-2019 period, allowing fixed effects and investigating the existence of heterogeneity allowing interactions of our focus variable with other regressors. Our empirical analysis then shows that the variation of self-employment rates across countries are mainly determined by variations in the unemployment, the stage of economic development and the variations in labor market frictions. When interactions are taken into account, results confirm that there is a differential effect of labor market frictions in countries with different levels of income. Frictions in labor market may encourage becoming self-employed in richer countries. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-20T07:20:53Z No. of bitstreams: 1 ijimai7_3_2.pdf: 672659 bytes, checksum: 8bc024d1f11be3e9c2717950ddc43015 (MD5); Made available in DSpace on 2022-05-20T07:20:53Z (GMT). No. of bitstreams: 1 ijimai7_3_2.pdf: 672659 bytes, checksum: 8bc024d1f11be3e9c2717950ddc43015 (MD5)

Mapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations

Mapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations Hameed Abdulkareem, Karrar; Arbaiy, Nureize; Hussein Arif, Zainab; Nasser Al-Mhiqani, Mohammed; Abed Mohammed, Mazin; Kadry, Seifedine; Alkareem Alyasseri, Zaid Abdi Our study aims to review and analyze the most relevant studies in the image dehazing field. Many aspects have been deemed necessary to provide a broad understanding of various studies that have been examined through surveying the existing literature. These aspects are as follows: datasets that have been used in the literature, challenges that other researchers have faced, motivations, and recommendations for diminishing the obstacles in the reported literature. A systematic protocol is employed to search all relevant articles on image dehazing, with variations in keywords, in addition to searching for evaluation and benchmark studies. The search process is established on three online databases, namely, IEEE Xplore, Web of Science (WOS), and ScienceDirect (SD), from 2008 to 2021. These indices are selected because they are sufficient in terms of coverage. Along with definition of the inclusion and exclusion criteria, we include 152 articles to the final set. A total of 55 out of 152 articles focused on various studies that conducted image dehazing, and 13 out 152 studies covered most of the review papers based on scenarios and general overviews. Finally, most of the included articles centered on the development of image dehazing algorithms based on real-time scenario (84/152) articles. Image dehazing removes unwanted visual effects and is often considered an image enhancement technique, which requires a fully automated algorithm to work under real-time outdoor applications, a reliable evaluation method, and datasets based on different weather conditions. Many relevant studies have been conducted to meet these critical requirements. We conducted objective image quality assessment experimental comparison of various image dehazing algorithms. In conclusions unlike other review papers, our study distinctly reflects different observations on image dehazing areas. We believe that the result of this study can serve as a useful guideline for practitioners who are looking for a comprehensive view on image dehazing. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-11T12:07:45Z No. of bitstreams: 1 ijimai7_2_16_0.pdf: 673766 bytes, checksum: 6f99792edfcf6637a4da6f9b2805caa7 (MD5); Made available in DSpace on 2022-05-11T12:07:45Z (GMT). No. of bitstreams: 1 ijimai7_2_16_0.pdf: 673766 bytes, checksum: 6f99792edfcf6637a4da6f9b2805caa7 (MD5)

Editor's Note

Editor's Note Blanco Valencia, Xiomara Patricia The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI - provides a space in which scientists and professionals can report about new advances in Artificial Intelligence (AI). On this occasion, for the last edition of the year, I am pleased to present a regular issue including different investigations covering aspects and problems in AI and its use in various fields such as medicine, education, image analysis, protection of data, among others. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-11T11:45:44Z No. of bitstreams: 1 ijimai7_2_0_0.pdf: 69842 bytes, checksum: 12909db8b3fc6ff8060906ae0e1a5aa5 (MD5); Made available in DSpace on 2022-05-11T11:45:44Z (GMT). No. of bitstreams: 1 ijimai7_2_0_0.pdf: 69842 bytes, checksum: 12909db8b3fc6ff8060906ae0e1a5aa5 (MD5)

A Study on RGB Image Multi-Thresholding using Kapur/Tsallis Entropy and Moth-Flame Algorithm

A Study on RGB Image Multi-Thresholding using Kapur/Tsallis Entropy and Moth-Flame Algorithm Rajinikanth, V.; Kadry, Seifedine; González-Crespo, Rubén; Verdú, Elena In the literature, a considerable number of image processing and evaluation procedures are proposed and implemented in various domains due to their practical importance. Thresholding is one of the pre-processing techniques, widely implemented to enhance the information in a class of gray/RGB class pictures. The thresholding helps to enhance the image by grouping the similar pixels based on the chosen thresholds. In this research, an entropy assisted threshold is implemented for the benchmark RGB images. The aim of this work is to examine the thresholding performance of well-known entropy functions, such as Kapur’s and Tsallis for a chosen image threshold. This work employs a Moth-Flame-Optimization (MFO) algorithm to support the automatic identification of the finest threshold (Th) on the benchmark RGB image for a chosen threshold value (Th=2,3,4,5). After getting the threshold image, a comparison is performed against its original picture and the necessary Picture-Quality-Values (PQV) is computed to confirm the merit of the proposed work. The experimental investigation is demonstrated using benchmark images with various dimensions and the outcome of this study confirms that the MFO helps to get a satisfactory result compared to the other heuristic algorithms considered in this study. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-11T11:42:38Z No. of bitstreams: 1 ijimai7_2_15_0.pdf: 2567870 bytes, checksum: e38f65b0be37534802f6765f3deb16b7 (MD5); Made available in DSpace on 2022-05-11T11:42:38Z (GMT). No. of bitstreams: 1 ijimai7_2_15_0.pdf: 2567870 bytes, checksum: e38f65b0be37534802f6765f3deb16b7 (MD5)

Local Technology to Enhance Data Privacy and Security in Educational Technology

Local Technology to Enhance Data Privacy and Security in Educational Technology Amo, Daniel; Prinsloo, Paul; Alier, Marc; Fonseca, David; Torres Kompen, Ricardo; Canaleta, Xavier; Herrero-Martín, Javier In educational environments, technological adoption in the last 10 years has enabled a data-driven and decisionmaking paradigm in organizations. The integration of cloud services in schools and universities is a positive shift in the field of learning, but it also presents threats to all academic roles that need to be discussed in terms of protection, privacy, and confidentiality. Cloud storage brings the ubiquity of data to this technical transition and a delusive opportunity for cost savings. In many cases, this suggests that certain actors, beyond the control of schools and colleges, collect, handle and treat educational data on private servers and data centers. This privatization enables the manipulation of stored records, leaks, and unauthorized access. In this article, we expose the possibilities that open from the viewpoint of local technology adoption. We seek to reduce or even totally solve the detrimental effects of using cloud-based instructional and analytical technology, mixing or only using local technology. Technological methods that conform to this alternate viewpoint and new lines of study are also being suggested and created. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-11T09:59:51Z No. of bitstreams: 1 ijimai7_2_23_0.pdf: 616385 bytes, checksum: 1a3b22dd452061264d7aa7cab4e0873e (MD5); Made available in DSpace on 2022-05-11T09:59:51Z (GMT). No. of bitstreams: 1 ijimai7_2_23_0.pdf: 616385 bytes, checksum: 1a3b22dd452061264d7aa7cab4e0873e (MD5)

Cross-Lingual Neural Network Speech Synthesis Based on Multiple Embeddings

Cross-Lingual Neural Network Speech Synthesis Based on Multiple Embeddings Nosek, Tijana V.; Suzić, Siniša B.; Pekar, Darko J.; Obradović, Radovan J.; Sečujski, Milan S.; Delić, Vlado D. The paper presents a novel architecture and method for speech synthesis in multiple languages, in voices of multiple speakers and in multiple speaking styles, even in cases when speech from a particular speaker in the target language was not present in the training data. The method is based on the application of neural network embedding to combinations of speaker and style IDs, but also to phones in particular phonetic contexts, without any prior linguistic knowledge on their phonetic properties. This enables the network not only to efficiently capture similarities and differences between speakers and speaking styles, but to establish appropriate relationships between phones belonging to different languages, and ultimately to produce synthetic speech in the voice of a certain speaker in a language that he/she has never spoken. The validity of the proposed approach has been confirmed through experiments with models trained on speech corpora of American English and Mexican Spanish. It has also been shown that the proposed approach supports the use of neural vocoders, i.e. that they are able to produce synthesized speech of good quality even in languages that they were not trained on. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-11T09:49:49Z No. of bitstreams: 1 ijimai7_2_10_0.pdf: 519266 bytes, checksum: e996a82f085e590cd34e1ee6e1e1490c (MD5); Made available in DSpace on 2022-05-11T09:49:49Z (GMT). No. of bitstreams: 1 ijimai7_2_10_0.pdf: 519266 bytes, checksum: e996a82f085e590cd34e1ee6e1e1490c (MD5)

Learning Analytics to Detect Evidence of Fraudulent Behaviour in Online Examinations

Learning Analytics to Detect Evidence of Fraudulent Behaviour in Online Examinations Balderas, Antonio; Palomo-Duarte, Manuel; Caballero-Hernández, Juan Antonio; Rodriguez-Garcia, Mercedes; Dodero, Juan Manuel Lecturers are often reluctant to set examinations online because of the potential problems of fraudulent behaviour from their students. This concern has increased during the coronavirus pandemic because courses that were previously designed to be taken face-to-face have to be conducted online. The courses have had to be redesigned, including seminars, laboratory sessions and evaluation activities. This has brought lecturers and students into conflict because, according to the students, the activities and examinations that have been redesigned to avoid cheating are also harder. The lecturers’ concern is that students can collaborate in taking examinations that must be taken individually without the lecturers being able to do anything to prevent it, i.e. fraudulent collaboration. This research proposes a process model to obtain evidence of students who attempt to fraudulently collaborate, based on the information in the learning environment logs. It is automated in a software tool that checks how the students took the examinations and the grades that they obtained. It is applied in a case study with more than 100 undergraduate students. The results are positive and its use allowed lecturers to detect evidence of fraudulent collaboration by several clusters of students from their submission timestamps and the grades obtained. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T12:33:15Z No. of bitstreams: 1 ijimai7_2_21_0.pdf: 800273 bytes, checksum: c4750db2120f55ac1a6e1c2443fd10fb (MD5); Made available in DSpace on 2022-05-10T12:33:15Z (GMT). No. of bitstreams: 1 ijimai7_2_21_0.pdf: 800273 bytes, checksum: c4750db2120f55ac1a6e1c2443fd10fb (MD5)

Optimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM

Optimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM Kumari, R. Radha; Kumar, V. Vijaya; Naidu, K. Rama The rapid growth of Internet and the fast emergence of multi-media applications over the past decades have led to new problems such as illegal copying, digital plagiarism, distribution and use of copyrighted digital data. Watermarking digital data for copyright protection is a current need of the community. For embedding watermarks, robust algorithms in die media will resolve copyright infringements. Therefore, to enhance the robustness, optimization techniques and deep neural network concepts are utilized. In this paper, the optimized Discrete Wavelet Transform (DWT) is utilized for embedding the watermark. The optimization algorithm is a combination of Simulated Annealing (SA) and Tunicate Swarm Algorithm (TSA). After performing the embedding process, the extraction is processed by deep neural network concept of Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM). From the extraction process, the original image is obtained by this RNN-LSTM method. The experimental set up is carried out in the MATLAB platform. The performance metrics of PSNR, NC and SSIM are determined and compared with existing optimization and machine learning approaches. The results are achieved under various attacks to show the robustness of the proposed work. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T11:58:38Z No. of bitstreams: 1 ijimai7_2_14_0.pdf: 1179334 bytes, checksum: df77eba60fbb6264e4a2d75b4b7c206b (MD5); Made available in DSpace on 2022-05-10T11:58:38Z (GMT). No. of bitstreams: 1 ijimai7_2_14_0.pdf: 1179334 bytes, checksum: df77eba60fbb6264e4a2d75b4b7c206b (MD5)

Music Boundary Detection using Convolutional Neural Networks: A Comparative Analysis of Combined Input Features

Music Boundary Detection using Convolutional Neural Networks: A Comparative Analysis of Combined Input Features Hernandez-Olivan, Carlos; Beltran, Jose R.; Diaz-Guerra, David The analysis of the structure of musical pieces is a task that remains a challenge for Artificial Intelligence, especially in the field of Deep Learning. It requires prior identification of the structural boundaries of the music pieces, whose structural boundary analysis has recently been studied with unsupervised methods and supervised neural networks trained with human annotations. The supervised neural networks that have been used in previous studies are Convolutional Neural Networks (CNN) that use Mel-Scaled Log-magnitude Spectograms features (MLS), Self-Similarity Matrices (SSM) or Self-Similarity Lag Matrices (SSLM) as inputs. In previously published studies, pre-processing is done in different ways using different distance metrics, and different audio features are used for computing the inputs, so a generalised pre-processing method for calculating model inputs is missing. The objective of this work is to establish a general method to pre-process these inputs by comparing the results obtained by taking the inputs calculated from different pooling strategies, distance metrics and audio characteristics, also taking into account the computing time to obtain them. We also establish the most effective combination of inputs to be delivered to the CNN to provide the most efficient way to extract the boundaries of the structure of the music pieces. With an adequate combination of input matrices and pooling strategies, we obtain an accuracy F1 of 0.411 that outperforms a current work done under the same conditions (same public available dataset for training and testing). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T11:49:12Z No. of bitstreams: 1 ijimai7_2_8_0.pdf: 1263458 bytes, checksum: fa397fb6164f4ce8bcd04a49851d0259 (MD5); Made available in DSpace on 2022-05-10T11:49:12Z (GMT). No. of bitstreams: 1 ijimai7_2_8_0.pdf: 1263458 bytes, checksum: fa397fb6164f4ce8bcd04a49851d0259 (MD5)

An Extensive Analysis of Machine Learning Based Boosting Algorithms for Software Maintainability Prediction

An Extensive Analysis of Machine Learning Based Boosting Algorithms for Software Maintainability Prediction Gupta, Shikha; Chug, Anuradha Software Maintainability is an indispensable factor to acclaim for the quality of particular software. It describes the ease to perform several maintenance activities to make a software adaptable to the modified environment. The availability & growing popularity of a wide range of Machine Learning (ML) algorithms for data analysis further provides the motivation for predicting this maintainability. However, an extensive analysis & comparison of various ML based Boosting Algorithms (BAs) for Software Maintainability Prediction (SMP) has not been made yet. Therefore, the current study analyzes and compares five different BAs, i.e., AdaBoost, GBM, XGB, LightGBM, and CatBoost, for SMP using open-source datasets. Performance of the propounded prediction models has been evaluated using Root Mean Square Error (RMSE), Mean Magnitude of Relative Error (MMRE), Pred(0.25), Pred(0.30), & Pred(0.75) as prediction accuracy measures followed by a non-parametric statistical test and a post hoc analysis to account for the differences in the performances of various BAs. Based on the residual errors obtained, it was observed that GBM is the best performer, followed by LightGBM for RMSE, whereas, in the case of MMRE, XGB performed the best for six out of the seven datasets, i.e., for 85.71% of the total datasets by providing minimum values for MMRE, ranging from 0.90 to 3.82. Further, on applying the statistical test and on performing the post hoc analysis, it was found that significant differences exist in the performance of different BAs and, XGB and CatBoost outperformed all other BAs for MMRE. Lastly, a comparison of BAs with four other ML algorithms has also been made to bring out BAs superiority over other algorithms. This study would open new doors for the software developers for carrying out comparatively more precise predictions well in time and hence reduce the overall maintenance costs. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T11:27:53Z No. of bitstreams: 1 ijimai7_2_9_0.pdf: 759011 bytes, checksum: aa9073f3d12617e05859b21b70e6c44f (MD5); Made available in DSpace on 2022-05-10T11:27:53Z (GMT). No. of bitstreams: 1 ijimai7_2_9_0.pdf: 759011 bytes, checksum: aa9073f3d12617e05859b21b70e6c44f (MD5)

Extensive Classification of Visual Art Paintings for Enhancing Education System using Hybrid SVM-ANN with Sparse Metric Learning based on Kernel Regression

Extensive Classification of Visual Art Paintings for Enhancing Education System using Hybrid SVM-ANN with Sparse Metric Learning based on Kernel Regression Xu, Fei; Wu, Tong; Huang, Shali; Han, Kuntong; Lin, Wenwen; Wu, Shizhong; CB, Sivaparthipan; Dinesh Jackson, Samuel R In recent decades, the collection of visual art paintings is large, digitized, and available for public uses that are rapidly growing. The development of multi-media systems is needed due to the huge amount of digitized artwork collections for retrieving and archiving this large-scale data. This multimedia system benefits from high-level tasks and has an essential step for measuring the similarity of visual between the artistic items. For modeling the similarities between the artworks or paintings, it is essential to extract useful features of visual paintings and propose the best approach for learning these similarity metrics. The infield of visual arts education, knowing the similarities and features, makes education more attractive by enhancing cognitive development in students. In this paper, the detailed visual features are listed, and the similarity measurement between the paintings is optimized by the Sparse Metric Learning-based Kernel Regression (KR-SML). A classification model is developed using hybrid SVM-ANN for semantic-level understanding to predict painting’s genre, artist, and style. Furthermore, the Human-Computer Interaction (HCI) based formulation model is built to analyze the proposed technique. The simulation results show that the proposed model is better in terms of performance than other existing techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T10:21:56Z No. of bitstreams: 1 ijimai7_2_19_0.pdf: 727196 bytes, checksum: 4be73a56f4fce334e76982d11114a5fb (MD5); Made available in DSpace on 2022-05-10T10:21:56Z (GMT). No. of bitstreams: 1 ijimai7_2_19_0.pdf: 727196 bytes, checksum: 4be73a56f4fce334e76982d11114a5fb (MD5)

Audio-Visual Automatic Speech Recognition Using PZM, MFCC and Statistical Analysis

Audio-Visual Automatic Speech Recognition Using PZM, MFCC and Statistical Analysis Debnath, Saswati; Roy, Pinki Audio-Visual Automatic Speech Recognition (AV-ASR) has become the most promising research area when the audio signal gets corrupted by noise. The main objective of this paper is to select the important and discriminative audio and visual speech features to recognize audio-visual speech. This paper proposes Pseudo Zernike Moment (PZM) and feature selection method for audio-visual speech recognition. Visual information is captured from the lip contour and computes the moments for lip reading. We have extracted 19th order of Mel Frequency Cepstral Coefficients (MFCC) as speech features from audio. Since all the 19 speech features are not equally important, therefore, feature selection algorithms are used to select the most efficient features. The various statistical algorithm such as Analysis of Variance (ANOVA), Kruskal-wallis, and Friedman test are employed to analyze the significance of features along with Incremental Feature Selection (IFS) technique. Statistical analysis is used to analyze the statistical significance of the speech features and after that IFS is used to select the speech feature subset. Furthermore, multiclass Support Vector Machine (SVM), Artificial Neural Network (ANN) and Naive Bayes (NB) machine learning techniques are used to recognize the speech for both the audio and visual modalities. Based on the recognition rate combined decision is taken from the two individual recognition systems. This paper compares the result achieved by the proposed model and the existing model for both audio and visual speech recognition. Zernike Moment (ZM) is compared with PZM and shows that our proposed model using PZM extracts better discriminative features for visual speech recognition. This study also proves that audio feature selection using statistical analysis outperforms methods without any feature selection technique. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T10:09:43Z No. of bitstreams: 1 ijimai7_2_11_0.pdf: 784862 bytes, checksum: 035a4cc4bff43d9f2f2d56c0006aacd5 (MD5); Made available in DSpace on 2022-05-10T10:09:43Z (GMT). No. of bitstreams: 1 ijimai7_2_11_0.pdf: 784862 bytes, checksum: 035a4cc4bff43d9f2f2d56c0006aacd5 (MD5)

Feasibility and Acceptability of a Mobile-Based Emotion Recognition Approach for Bipolar Disorder

Feasibility and Acceptability of a Mobile-Based Emotion Recognition Approach for Bipolar Disorder Daus, H.; Backenstrass, M. Over the past years, the mobile Health approach has motivated research projects to develop mood monitoring systems for bipolar disorder. Whereas mobile-based approaches have examined self-assessment or sensor data, so far, potentially important emotional aspects of this disease have been neglected. Thus, we developed an emotion-sensitive system that analyzes the verbal and facial expressions of bipolar patients in regard to their emotional cues. In this article, preliminary findings of a pilot study with five bipolar patients with respect to the acceptability and feasibility of the new approach are presented and discussed. There were individual differences in the usage frequency of the participants, and improvements regarding its handling were suggested. From the technical point of view, the video analysis was less dependable than the audio analysis and recognized almost exclusively the facial expressions of happiness. However, the system was feasible and well-accepted. The results indicate that further developments could facilitate the long-term analysis of expressed emotions in bipolar or other disorders without invading the privacy of patients. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T09:57:04Z No. of bitstreams: 1 ijimai7_2_1_0.pdf: 347042 bytes, checksum: 4f2e7d6bd582069c7e54223681bdedd6 (MD5); Made available in DSpace on 2022-05-10T09:57:04Z (GMT). No. of bitstreams: 1 ijimai7_2_1_0.pdf: 347042 bytes, checksum: 4f2e7d6bd582069c7e54223681bdedd6 (MD5)

Design of a Virtual Assistant to Improve Interaction Between the Audience and the Presenter

Design of a Virtual Assistant to Improve Interaction Between the Audience and the Presenter Cobos-Guzman, S.; Nuere, S.; De Miguel, L. This article presents a novel design of a Virtual Assistant as part of a human-machine interaction system to improve communication between the presenter and the audience that can be used in education or general presentations for improving interaction during the presentations (e.g., auditoriums with 200 people). The main goal of the proposed model is the design of a framework of interaction to increase the level of attention of the public in key aspects of the presentation. In this manner, the collaboration between the presenter and Virtual Assistant could improve the level of learning among the public. The design of the Virtual Assistant relies on non-anthropomorphic forms with ‘live’ characteristics generating an intuitive and self-explainable interface. A set of intuitive and useful virtual interactions to support the presenter was designed. This design was validated from various types of the public with a psychological study based on a discrete emotions’ questionnaire confirming the adequacy of the proposed solution. The human-machine interaction system supporting the Virtual Assistant should automatically recognize the attention level of the audience from audiovisual resources and synchronize the Virtual Assistant with the presentation. The system involves a complex artificial intelligence architecture embracing perception of high-level features from audio and video, knowledge representation, and reasoning for pervasive and affective computing and reinforcement learning to teach the intelligent agent to decide on the best strategy to increase the level of attention of the audience. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T12:28:43Z No. of bitstreams: 1 ijimai7_2_20_0.pdf: 957939 bytes, checksum: b1c73d047076f7adcd9f2a6149d79c46 (MD5); Made available in DSpace on 2022-05-09T12:28:43Z (GMT). No. of bitstreams: 1 ijimai7_2_20_0.pdf: 957939 bytes, checksum: b1c73d047076f7adcd9f2a6149d79c46 (MD5)

A Case-Based Reasoning Model Powered by Deep Learning for Radiology Report Recommendation

A Case-Based Reasoning Model Powered by Deep Learning for Radiology Report Recommendation Amador-Domínguez, Elvira; Serrano, Emilio; Manrique, Daniel; Bajo, Javier Case-Based Reasoning models are one of the most used reasoning paradigms in expert-knowledge-driven areas. One of the most prominent fields of use of these systems is the medical sector, where explainable models are required. However, these models are considerably reliant on user input and the introduction of relevant curated data. Deep learning approaches offer an analogous solution, where user input is not required. This paper proposes a hybrid Case-Based Reasoning, Deep Learning framework for medical-related applications, focusing on the generation of medical reports. The proposal combines the explainability and user-focused approach of case-based reasoning models with the deep learning techniques performance. Moreover, the framework is fully modular to fit a wide variety of tasks and data, such as real-time sensor captured data, images, or text, to name a few. An implementation of the proposed framework focusing on radiology report generation assistance is provided. This implementation is used to evaluate the proposal, showing that it can provide meaningful and accurate corrections, even when the amount of information available is minimal. Additional tests on the optimization degree of the case base are also performed, evidencing how the proposed framework can optimize this base to achieve optimal performance. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T12:13:39Z No. of bitstreams: 1 ijimai7_2_2_0.pdf: 1951937 bytes, checksum: b7226a70b3bb8ff7393614bff7771290 (MD5); Made available in DSpace on 2022-05-09T12:13:39Z (GMT). No. of bitstreams: 1 ijimai7_2_2_0.pdf: 1951937 bytes, checksum: b7226a70b3bb8ff7393614bff7771290 (MD5)

Acoustic Classification of Mosquitoes using Convolutional Neural Networks Combined with Activity Circadian Rhythm Information

Acoustic Classification of Mosquitoes using Convolutional Neural Networks Combined with Activity Circadian Rhythm Information Kim, Jaehoon; Oh, Jeongkyu; Heo, Tae-Young Many researchers have used sound sensors to record audio data from insects, and used these data as inputs of machine learning algorithms to classify insect species. In image classification, the convolutional neural network (CNN), a well-known deep learning algorithm, achieves better performance than any other machine learning algorithm. This performance is affected by the characteristics of the convolution filter (ConvFilter) learned inside the network. Furthermore, CNN performs well in sound classification. Unlike image classification, however, there is little research on suitable ConvFilters for sound classification. Therefore, we compare the performances of three convolution filters, 1D-ConvFilter, 3×1 2D-ConvFilter, and 3×3 2D-ConvFilter, in two different network configurations, when classifying mosquitoes using audio data. In insect sound classification, most machine learning researchers use only audio data as input. However, a classification model, which combines other information such as activity circadian rhythm, should intuitively yield improved classification results. To utilize such relevant additional information, we propose a method that defines this information as a priori probabilities and combines them with CNN outputs. Of the networks, VGG13 with 3×3 2D-ConvFilter showed the best performance in classifying mosquito species, with an accuracy of 80.8%. Moreover, adding activity circadian rhythm information to the networks showed an average performance improvement of 5.5%. The VGG13 network with 1D-ConvFilter achieved the highest accuracy of 85.7% with the additional activity circadian rhythm information. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T11:55:08Z No. of bitstreams: 1 ijimai7_2_6_0.pdf: 809756 bytes, checksum: c065207b8a017f7d4558654b23d8afb1 (MD5); Made available in DSpace on 2022-05-09T11:55:08Z (GMT). No. of bitstreams: 1 ijimai7_2_6_0.pdf: 809756 bytes, checksum: c065207b8a017f7d4558654b23d8afb1 (MD5)

Deep Multi-Model Fusion for Human Activity Recognition Using Evolutionary Algorithms

Deep Multi-Model Fusion for Human Activity Recognition Using Evolutionary Algorithms Verma, Kamal Kant; Singh, Brij Mohan Machine recognition of the human activities is an active research area in computer vision. In previous study, either one or two types of modalities have been used to handle this task. However, the grouping of maximum information improves the recognition accuracy of human activities. Therefore, this paper proposes an automatic human activity recognition system through deep fusion of multi-streams along with decision-level score optimization using evolutionary algorithms on RGB, depth maps and 3d skeleton joint information. Our proposed approach works in three phases, 1) space-time activity learning using two 3D Convolutional Neural Network (3DCNN) and a Long Sort Term Memory (LSTM) network from RGB, Depth and skeleton joint positions 2) Training of SVM using the activities learned from previous phase for each model and score generation using trained SVM 3) Score fusion and optimization using two Evolutionary algorithm such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. The proposed approach is validated on two 3D challenging datasets, MSRDailyActivity3D and UTKinectAction3D. Experiments on these two datasets achieved 85.94% and 96.5% accuracies, respectively. The experimental results show the usefulness of the proposed representation. Furthermore, the fusion of different modalities improves recognition accuracies rather than using one or two types of information and obtains the state-of-art results. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T11:48:54Z No. of bitstreams: 1 ijimai7_2_5_0.pdf: 1042722 bytes, checksum: 9b2093802be0e848d08c745da7e2c2f7 (MD5); Made available in DSpace on 2022-05-09T11:48:54Z (GMT). No. of bitstreams: 1 ijimai7_2_5_0.pdf: 1042722 bytes, checksum: 9b2093802be0e848d08c745da7e2c2f7 (MD5)

Towards a Solution to Create, Test and Publish Mixed Reality Experiences for Occupational Safety and Health Learning: Training-MR

Towards a Solution to Create, Test and Publish Mixed Reality Experiences for Occupational Safety and Health Learning: Training-MR Lopez, Miguel Angel; Terrón, Sara; Lombardo, Juan Manuel; González-Crespo, Rubén Artificial intelligence, Internet of Things, Human Augmentation, virtual reality, or mixed reality have been rapidly implemented in Industry 4.0, as they improve the productivity of workers. This productivity improvement can come largely from modernizing tools, improving training, and implementing safer working methods. Human Augmentation is helping to place workers in unique environments through virtual reality or mixed reality, by applying them to training actions in a totally innovative way. Science still has to overcome several technological challenges to achieve widespread application of these tools. One of them is the democratisation of these experiences, for which is essential to make them more accessible, reducing the cost of creation that is the main barrier to entry. The cost of these mixed reality experiences lies in the effort required to design and build these mixed reality training experiences. Nevertheless, the tool presented in this paper is a solution to these current limitations. A solution for designing, building and publishing experiences is presented in this paper. With the solution, content creators will be able to create their own training experiences in a semiassisted way and eventually publish them in the Cloud. Students will be able to access this training offered as a service, using Microsoft HoloLens2. In this paper, the reader will find technical details of the Training-MR, its architecture, mode of operation and communication Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T11:06:25Z No. of bitstreams: 1 ijimai7_2_18_0.pdf: 576012 bytes, checksum: c6f28db410ee001b42b21c4253108fe8 (MD5); Made available in DSpace on 2022-05-09T11:06:25Z (GMT). No. of bitstreams: 1 ijimai7_2_18_0.pdf: 576012 bytes, checksum: c6f28db410ee001b42b21c4253108fe8 (MD5)

Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks

Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks Li, Yuanfeng; Deng, Jiangang; Wu, Qun; Wang, Ying Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition and physiological indicators, the establishment of a dynamic and complete database, and the addition of high-tech innovative products become recent trends in AC. This research aims to develop a deep gradient convolutional neural network (DGCNN) for classifying affection by using an eye-tracking signals. General signal process tools and pre-processing methods were applied firstly, such as Kalman filter, windowing with hamming, short-time Fourier transform (SIFT), and fast Fourier transform (FTT). Secondly, the eye-moving and tracking signals were converted into images. A convolutional neural networks-based training structure was subsequently applied; the experimental dataset was acquired by an eye-tracking device by assigning four affective stimuli (nervous, calm, happy, and sad) of 16 participants. Finally, the performance of DGCNN was compared with a decision tree (DT), Bayesian Gaussian model (BGM), and k-nearest neighbor (KNN) by using indices of true positive rate (TPR) and false negative rate (FPR). Customizing mini-batch, loss, learning rate, and gradients definition for the training structure of the deep neural network was also deployed finally. The predictive classification matrix showed the effectiveness of the proposed method for eye moving and tracking signals, which performs more than 87.2% inaccuracy. This research provided a feasible way to find more natural human-computer interaction through eye moving and tracking signals and has potential application on the affective production design process. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T10:43:19Z No. of bitstreams: 1 ijimai7_2_4_0.pdf: 1115306 bytes, checksum: 12738b8c684505e1608f4e5f72641b36 (MD5); Made available in DSpace on 2022-05-09T10:43:19Z (GMT). No. of bitstreams: 1 ijimai7_2_4_0.pdf: 1115306 bytes, checksum: 12738b8c684505e1608f4e5f72641b36 (MD5)

Using Grip Strength as a Cardiovascular Risk Indicator Based on Hybrid Algorithms

Using Grip Strength as a Cardiovascular Risk Indicator Based on Hybrid Algorithms Bareño-Castellanos, E.F.; Gaona-García, Paulo Alonso; Ortiz-Guzmán, J.E.; Montenegro-Marin, Carlos Enrique This article shows the application and design of a hybrid algorithm capable of classifying people into risk groups using data such as prehensile strength, body mass index and percentage of fat. The implementation was done on Python and proposes a tool to help make medical decisions regarding the cardiovascular health of patients. The data were taken in a systematic way, k-means and c-means algorithms were used for the classification of the data, for the prediction of new data two vectorial support machines were used, one for the k-means and the other for the c-means, obtaining as a result a 100% of precision in the vectorial support machine with c-means and a 92% in the one of k-means. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T10:04:52Z No. of bitstreams: 1 ijimai7_2_3_0.pdf: 523245 bytes, checksum: a61323622496bfa5d731b2fe40c15ba6 (MD5); Made available in DSpace on 2022-05-09T10:04:52Z (GMT). No. of bitstreams: 1 ijimai7_2_3_0.pdf: 523245 bytes, checksum: a61323622496bfa5d731b2fe40c15ba6 (MD5)

Neighborhood Structure-Based Model for Multilingual Arbitrarily-Oriented Text Localization in Images/Videos

Neighborhood Structure-Based Model for Multilingual Arbitrarily-Oriented Text Localization in Images/Videos Basavaraju, H.T. H.T.; Manjunath Aradhya, V.N.; Guru, D.S. The text matter in an image or a video provides more important clue and semantic information of the particular event in the actual situation. Text localization task stands an interesting and challenging research-oriented process in the zone of image processing due to irregular alignments, brightness, degradation, and complexbackground. The multilingual textual information has different types of geometrical shapes and it makes further complex to locate the text information. In this work, an effective model is presented to locate the multilingual arbitrary oriented text. The proposed method developed a neighborhood structure model to locate the text region. Initially, the maxmin cluster is applied along with 3X3 sliding window to sharpen the text region. The neighborhood structure creates the boundary for every component using normal deviation calculated from the sharpened image. Finally, the double stroke structure model is employed to locate the accurate text region. The presented model is analyzed on five standard datasets such as NUS, arbitrarily oriented text, Hua's, MRRC and real-time video dataset with performance metrics such as recall, precision, and f-measure. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T09:55:41Z No. of bitstreams: 1 ijimai7_2_12_0.pdf: 1719075 bytes, checksum: de7f8084c7e91f0a18dbbcd61eafbf71 (MD5); Made available in DSpace on 2022-05-09T09:55:41Z (GMT). No. of bitstreams: 1 ijimai7_2_12_0.pdf: 1719075 bytes, checksum: de7f8084c7e91f0a18dbbcd61eafbf71 (MD5)

Deep Feature Representation and Similarity Matrix based Noise Label Refinement Method for Efficient Face Annotation

Deep Feature Representation and Similarity Matrix based Noise Label Refinement Method for Efficient Face Annotation Suruliandi, A.; Kasthuri, A.; Raja, S. P. Face annotation is a naming procedure that assigns the correct name to a person emerging from an image. Faces that are manually annotated by people in online applications include incorrect labels, giving rise to the issue of label ambiguity. This may lead to mislabelling in face annotation. Consequently, an efficient method is still essential to enhance the reliability of face annotation. Hence, in this work, a novel method named the Similarity Matrix-based Noise Label Refinement (SMNLR) is proposed, which effectively predicts the accurate label from the noisy labelled facial images. To enhance the performance of the proposed method, the deep learning technique named Convolutional Neural Networks (CNN) is used for feature representation. Several experiments are conducted to evaluate the effectiveness of the proposed face annotation method using the LFW, IMFDB and Yahoo datasets. The experimental results clearly illustrate the robustness of the proposed SMNLR method in dealing with noisy labelled faces. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T09:20:44Z No. of bitstreams: 1 ijimai7_2_7_0.pdf: 809172 bytes, checksum: 1e76e2d158efeed6ce1d2ca5a22e329b (MD5); Made available in DSpace on 2022-05-09T09:20:44Z (GMT). No. of bitstreams: 1 ijimai7_2_7_0.pdf: 809172 bytes, checksum: 1e76e2d158efeed6ce1d2ca5a22e329b (MD5)

Performance and Convergence Analysis of Modified C-Means Using Jeffreys-Divergence for Clustering

Performance and Convergence Analysis of Modified C-Means Using Jeffreys-Divergence for Clustering Seal, Ayan; Karlekar, Aditya; Krejcar, Ondrej; Herrera-Viedma, Enrique The size of data that we generate every day across the globe is undoubtedly astonishing due to the growth of the Internet of Things. So, it is a common practice to unravel important hidden facts and understand the massive data using clustering techniques. However, non- linear relations, which are essentially unexplored when compared to linear correlations, are more widespread within data that is high throughput. Often, nonlinear links can model a large amount of data in a more precise fashion and highlight critical trends and patterns. Moreover, selecting an appropriate measure of similarity is a well-known issue since many years when it comes to data clustering. In this work, a non-Euclidean similarity measure is proposed, which relies on non-linear Jeffreys-divergence (JS). We subsequently develop c- means using the proposed JS (J-c-means). The various properties of the JS and J-c-means are discussed. All the analyses were carried out on a few real-life and synthetic databases. The obtained outcomes show that J-c-means outperforms some cutting-edge c-means algorithms empirically. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T09:04:12Z No. of bitstreams: 1 ijimai7_2_13_0.pdf: 1127561 bytes, checksum: 81ce1c10d0a4d67f6f8b9358569277f2 (MD5); Made available in DSpace on 2022-05-09T09:04:12Z (GMT). No. of bitstreams: 1 ijimai7_2_13_0.pdf: 1127561 bytes, checksum: 81ce1c10d0a4d67f6f8b9358569277f2 (MD5)

A Systematic Literature Review of Empirical Studies on Learning Analytics in Educational Games

A Systematic Literature Review of Empirical Studies on Learning Analytics in Educational Games Tlili, Ahmed; Chang, Maiga; Moon, Jewoong; Liu, Zhichun; Burgos, Daniel; Chen, Nian-Shing; Kinshuk Learning analytics (LA) in educational games is considered an emerging practice due to its potential of enhancing the learning process. Growing research on formative assessment has shed light on the ways in which students' meaningful and in-situ learning experiences can be supported through educational games. To understand learners' playful experiences during gameplay, researchers have applied LA, which focuses on understanding students' in-game behaviour trajectories and personal learning needs during play. However, there is a lack of studies exploring how further research on LA in educational games can be conducted. Only a few analyses have discussed how LA has been designed, integrated, and implemented in educational games. Accordingly, this systematic literature review examined how LA in educational games has evolved. The study findings suggest that: (1) there is an increasing need to consider factors such as student modelling, iterative game design and personalisation when designing and implementing LA through educational games; and (2) the use of LA creates several challenges from technical, data management and ethical perspectives. In addition to outlining these findings, this article offers important notes for practitioners, and discusses the implications of the study’s results. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T07:55:16Z No. of bitstreams: 1 ijimai7_2_22_0.pdf: 566366 bytes, checksum: 6ffdc8dea2da263c81f5f221c6dbb46e (MD5); Made available in DSpace on 2022-05-09T07:55:16Z (GMT). No. of bitstreams: 1 ijimai7_2_22_0.pdf: 566366 bytes, checksum: 6ffdc8dea2da263c81f5f221c6dbb46e (MD5)

Foundations for the Design of a Creative System Based on the Analysis of the Main Techniques that Stimulate Human Creativity

Foundations for the Design of a Creative System Based on the Analysis of the Main Techniques that Stimulate Human Creativity De Garrido, L.; Gómez Sanz, J.J.; Pavón Mestras, Juan This work presents the design of a computational system with creative capacity, based on the synthesis of the main methods that stimulate human creativity. When analyzing each method, a set of characteristics that the computer system must have in order to emulate a creative capacity has been suggested. In this way, by integrating all the suggestions in a structured way, it is possible to design the general architecture and functioning strategy of a computer system that has the incremental creative capacity of well-known creative methods. This computational system is designed as a multi-agent system, made up of two groups of agents, the problem solving group and the creative group, the first one exploring and evaluating paths for suitable solutions, the second implementing creative methods to generate new paths that are provided to the first group. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T07:24:52Z No. of bitstreams: 1 ijimai7_2_17.pdf: 982798 bytes, checksum: 8de4157a28208ebfdd5829fc0d99cd16 (MD5); Made available in DSpace on 2022-05-09T07:24:52Z (GMT). No. of bitstreams: 1 ijimai7_2_17.pdf: 982798 bytes, checksum: 8de4157a28208ebfdd5829fc0d99cd16 (MD5)

Editor's Note

Editor's Note Burgos, Daniel; Oviedo, Lluis; Griffiths, Dai; Vestrucci, Andrea Research on the relationship between computing and the meaning of human life flourishes proportionally to the increasing digitalization of our world. More and more, reflections on ethics and politics, spiritual values and religious experiences, beliefs, and practices make use of digital media in order to spread their content or express themselves. If we still consider that there is truth in the well-known dictum that “the medium is the message”, then it is worth asking how the content of these reflections and practices are changing today. Every change is the introduction of something new, and this novelty can be interpreted either as the improvement or the worsening of the current situation. Generally speaking, research on either the positive or negative interactions between the advances in AI and the dimension of spirituality and analogue thinking are based on at least three approaches. The first produces analogies between concepts from human studies and concepts from computer science; for instance, speaking of “modeling” for concepts in human sciences, or considering the universe to be intelligently organized in an algorithmic order. The second approach is the application of research on AI and computer science to develop new insights on the extents, limits, and perfectibility of spiritual topics, discussions, or even practices. Finally, the third approach applies sociological, philosophical, aesthetic, or even theological concepts to assess the changes that digitalization introduces in spiritual practices, beliefs, and cultures. This special issue analyzes the current state of the art, and it addresses all three models of the research. By doing so, the issue will place the general question of the distinction between human and machine into sharper relief. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T08:22:56Z No. of bitstreams: 1 ijimai7_1_0.pdf: 80862 bytes, checksum: 09f5c25f9992e878cc1fb9aa6c6ab27d (MD5); Made available in DSpace on 2022-05-06T08:22:56Z (GMT). No. of bitstreams: 1 ijimai7_1_0.pdf: 80862 bytes, checksum: 09f5c25f9992e878cc1fb9aa6c6ab27d (MD5)

Can AI Help Us to Understand Belief? Sources, Advances, Limits, and Future Directions

Can AI Help Us to Understand Belief? Sources, Advances, Limits, and Future Directions Vestrucci, Andrea; Lumbreras, Sara; Oviedo, Lluis The study of belief is expanding and involves a growing set of disciplines and research areas. These research programs attempt to shed light on the process of believing, understood as a central human cognitive function. Computational systems and, in particular, what we commonly understand as Artificial Intelligence (AI), can provide some insights on how beliefs work as either a linear process or as a complex system. However, the computational approach has undergone some scrutiny, in particular about the differences between what is distinctively human and what can be inferred from AI systems. The present article investigates to what extent recent developments in AI provide new elements to the debate and clarify the process of belief acquisition, consolidation, and recalibration. The article analyses and debates current issues and topics of investigation such as: different models to understand belief, the exploration of belief in an automated reasoning environment, the case of religious beliefs, and future directions of research. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T08:19:11Z No. of bitstreams: 1 ijimai7_1_3_0.pdf: 657004 bytes, checksum: 926f0d77a9f183dff708ce1b495bf438 (MD5); Made available in DSpace on 2022-05-06T08:19:11Z (GMT). No. of bitstreams: 1 ijimai7_1_3_0.pdf: 657004 bytes, checksum: 926f0d77a9f183dff708ce1b495bf438 (MD5)

Artificial Intelligence Seen Through the Lens of Bateson’s Ecology of Mind

Artificial Intelligence Seen Through the Lens of Bateson’s Ecology of Mind Griffiths, Dai Gregory Bateson developed a number of ideas which are relevant to artificial intelligence, and in particular to the ascription of qualities such as mind, consciousness, spirituality and the sacred. Relevant sections of Bateson’s key works are discussed, and his intellectual framework for an ecology of mind is summarized, and in particular his concepts of mind, learning, and the sacred. These are then applied to discuss whether artificial intelligence applications can be considered to possess ‘mind’. It is concluded that symbolic artificial intelligence falls short of Bateson’s criteria for mind, as do neural networks, although approach more closely. Nor are computers based on the rules of formal logic able to engage with the sacred, which is paradoxical in nature. However, artificial intelligence applications can form part of an ecology of mind and can be involved in the experience of the sacred. Bateson’s writing remains a fertile source of ideas relevant to an understanding of the nature and capabilities of artificial intelligence. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T08:01:14Z No. of bitstreams: 1 ijimai7_1_7.pdf: 852518 bytes, checksum: c112e138701f5952d979ea7881dd4598 (MD5); Made available in DSpace on 2022-05-06T08:01:14Z (GMT). No. of bitstreams: 1 ijimai7_1_7.pdf: 852518 bytes, checksum: c112e138701f5952d979ea7881dd4598 (MD5)

Emergent Models for Moral AI Spirituality

Emergent Models for Moral AI Spirituality Graves, Mark Examining AI spirituality can illuminate problematic assumptions about human spirituality and AI cognition, suggest possible directions for AI development, reduce uncertainty about future AI, and yield a methodological lens sufficient to investigate human-AI sociotechnical interaction and morality. Incompatible philosophical assumptions about human spirituality and AI limit investigations of both and suggest a vast gulf between them. An emergentist approach can replace dualist assumptions about human spirituality and identify emergent behavior in AI computation to overcome overly reductionist assumptions about computation. Using general systems theory to organize models of human experience yields insight into human morality and spirituality, upon which AI modeling can also draw. In this context, the pragmatist Josiah Royce’s semiotic philosophy of spirituality identifies unanticipated overlap between symbolic AI and spirituality and suggests criteria for a human-AI community focused on modeling morality that would result in an emergent Interpreter-Spirit sufficient to influence the ongoing development of human and AI morality and spirituality. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T07:56:08Z No. of bitstreams: 1 ijimai7_1_1.pdf: 452895 bytes, checksum: 4f1b3aa0b3d1071ad7c36b0653a9fd4e (MD5); Made available in DSpace on 2022-05-06T07:56:08Z (GMT). No. of bitstreams: 1 ijimai7_1_1.pdf: 452895 bytes, checksum: 4f1b3aa0b3d1071ad7c36b0653a9fd4e (MD5)

Why the Future Might Actually Need Us: A Theological Critique of the ‘Humanity-As-Midwife-For-Artificial-Superintelligence’ Proposal

Why the Future Might Actually Need Us: A Theological Critique of the ‘Humanity-As-Midwife-For-Artificial-Superintelligence’ Proposal Dorobantu, Marius If machines could one day acquire superhuman intelligence, what role would still be left for humans to play in the world? The ‘midwife proposal,’ coming from futurists like Ray Kurzweil or James Lovelock, sees the invention of AI as a fulfillment of humanity’s cosmic destiny. The universe ‘strives’ to be saturated with intelligence, and our cyborg descendants are much better equipped to advance this goal. By creating AI, humans play their humble, but instrumental, part in the grand scheme. The midwife proposal looks remarkably similar to modern Christian anthropology and cosmology, which regard humankind as “evolution becoming conscious of itself” (Pierre Teilhard de Chardin), and matter as having a predisposition to evolve toward spirit (Karl Rahner, Dumitru Stăniloae). This paper demonstrates that the similarity is only superficial. Compared to the midwife hypothesis, Christian theological accounts define the cosmic role of humanity quite differently, and they provide a more satisfactory teleology. In addition, the scientific and philosophical assumptions behind the midwife hypothesis – that the cosmos is fundamentally informational, that it intrinsically promotes higher intelligence, or that we are heading toward a technological singularity - are rather questionable, with potentially significant theological and ethical consequences. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T07:51:34Z No. of bitstreams: 1 ijimai7_1_5.pdf: 632327 bytes, checksum: 281b28acc14ea84939893cf490de8339 (MD5); Made available in DSpace on 2022-05-06T07:51:34Z (GMT). No. of bitstreams: 1 ijimai7_1_5.pdf: 632327 bytes, checksum: 281b28acc14ea84939893cf490de8339 (MD5)

“The Singularity is near!” Visions of Artificial Intelligence in Posthumanism and Transhumanism

“The Singularity is near!” Visions of Artificial Intelligence in Posthumanism and Transhumanism Krüger, Oliver Over the past 20 years, the idea of singularity has become increasingly important to the technological visions of posthumanism and transhumanism. The article first introduces key posthumanist authors such as Marvin Minsky, Ray Kurzweil, Hans Moravec, and Frank Tipler. In the following, the concept of singularity is reviewed from a cultural studies perspective, first with regard to the cosmological singularity and then to the technological singularity. According to posthumanist thinkers the singularity is marked by the emergence of a superhuman computer intelligence that will solve all of humanity’s problems. At the same time, it heralds the end of the human era. Most authors refer to the British mathematician Irving John Good’s 1965 essay Speculations Concerning the First Ultraintelligent Machine as the originator of the idea of superintelligence. Individual elements of the singularity idea such as the impenetrable event horizon, the frontier and the ongoing acceleration of progress are contextualized historically and culturally. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T07:47:15Z No. of bitstreams: 1 ijimai7_1_2.pdf: 531069 bytes, checksum: 728472ad62c03193016fd1faa165e18d (MD5); Made available in DSpace on 2022-05-06T07:47:15Z (GMT). No. of bitstreams: 1 ijimai7_1_2.pdf: 531069 bytes, checksum: 728472ad62c03193016fd1faa165e18d (MD5)

Rituals and Data Analytics: A Mixed-Methods Model to Process Personal Beliefs

Rituals and Data Analytics: A Mixed-Methods Model to Process Personal Beliefs Burgos, Daniel The goal of this research is to delve into ritual, religious, and secular phenomenology. It concentrates specifically on the relationship between pagan, cultural, celebratory, and traditional rituals and any other form of representation of a social sentiment focused on identifying, enjoying, or replacing a feeling (e.g. transcendence) as well as how these rituals overlap, replace, nourish, or make use of religious rituals bi-directionally. To achieve this goal, the research develops a semi-automatic process that leans on a mixed-methods approach, to explore the degree of ritual identity. This approach combines qualitative and quantitative research, applying a number of tools, such as systematic literature review, semi-structured interviews, data-analytics generic framework, and case studies. After a thorough systematic review of 251 publications, a semi-structured interview is designed and applied to 51 subjects. 10 significant aspects that define rituals are extracted. Subsequently, this list is completed with the 17 common elements of ritual identity from the systematic literature review. These combined indicators constitute the basis for building a data-analytics generic framework of ritual affinity through weighing each element’s relevance and presence to obtain a degree of total affinity. That framework is then applied to 34 representative case studies. The core findings reinforce the initial hypothesis, determining that rituals follow a similar pattern of structure and preparation according to a predetermined set of common elements, whether linked to religious or secular settings. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T07:27:53Z No. of bitstreams: 1 ijimai7_1_6.pdf: 1196512 bytes, checksum: e90905d2529040388139582eb6c2fcfb (MD5); Made available in DSpace on 2022-05-06T07:27:53Z (GMT). No. of bitstreams: 1 ijimai7_1_6.pdf: 1196512 bytes, checksum: e90905d2529040388139582eb6c2fcfb (MD5)

Artificial Intelligence and Spirituality

Artificial Intelligence and Spirituality Calderero Hernández, José Fernando Drawing from a conceptual review of the terms ‘mind’, ‘intelligence’, ‘spirit’, ‘spirituality’, ‘spiritual intelligence’ and their possible interrelations, an approach to the concept ‘human nature’ is made in relation to transhumanism and post-humanism. In addition, through a reflection on the nature and meaning of the terms ‘datum’, ‘coding’, ‘language’, ‘energy’, ‘concrete’, and ‘abstract’, some dimensions of ‘artificial intelligence’ (AI) and their analogies and differences with ‘the spiritual’ are shown. After a brief foray into the concept of ‘reality’ and its probable ‘fuzziness’, we discuss their intrinsic and inherent mutability, and the possible existential dependence of some of their parts on the intentional activity of personal beings. We point out the dangers, for intellectual rigor and therefore for life in general, and human life in particular, of reductionist interpretations of reality that, arguing at having been scientifically proven, are intended to provide a closed and indisputable explanation of facts and phenomena of diverse aetiology, ignoring the need for ‘management of the unknown’. Consequently, an open, synergetic, harmonious vision of the role of technology and the humanities, especially those most focused on the study of the intangible, is necessary for the progress of knowledge and, therefore, for the mutually beneficial care of humanity and nature. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T07:23:59Z No. of bitstreams: 1 ijimai7_1_4.pdf: 645923 bytes, checksum: ff560293f40dc22e74e47726f67c5f59 (MD5); Made available in DSpace on 2022-05-06T07:23:59Z (GMT). No. of bitstreams: 1 ijimai7_1_4.pdf: 645923 bytes, checksum: ff560293f40dc22e74e47726f67c5f59 (MD5)

Editor's Note

Editor's Note Chun-Wei Lin, Jerry; Srivastava, Gautam; Tseng, Vicent S. In today’s world, we have witnessed an onset of multimedia content being uploaded/downloaded and shared through a multitude of platforms both online and offline. In support of this trend, multimedia processing and analyzing has become very popular in all kinds of information extraction and attracts research interest from both academia and industry. This is to be expected as the multimedia digital world is worth trillions of dollars worldwide. However, multimedia information is hard to encode, interpret and recognize because it is combined with many complex components. Recently, there are many research areas related to the overall notion of intelligent multimedia processing. Therefore, the collected papers in this special issue provide a systematic overview and state-of-the-art research in the field of intelligent multimedia processing and analyzing system and outline new developments in fundamental, theorems, approaches, methodologies, software systems, recommendations, and real-world applications in this area. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T12:30:24Z No. of bitstreams: 1 ijimai6_7_0.pdf: 85925 bytes, checksum: ba27e470d7b795be7800324d985cad96 (MD5); Made available in DSpace on 2022-05-03T12:30:24Z (GMT). No. of bitstreams: 1 ijimai6_7_0.pdf: 85925 bytes, checksum: ba27e470d7b795be7800324d985cad96 (MD5)

Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network

Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network Chan Chiu, Po; Selamat, Ali; Krejcar, Ondrej; Kuok Kuok, King; Herrera-Viedma, Enrique; Fenza, Giuseppe Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel pre-processing mechanism for non-precipitation data by using principal component analysis (PCA). Before the imputation, PCA is used to extract the most relevant features from the meteorological data. The final output of the PCA is combined with the rainfall data from the nearest neighbor gauging stations and then used as the input to the neural network for missing data imputation. Second, a sine cosine algorithm is presented to optimize neural network for infilling the missing rainfall data. The proposed sine cosine function fitting neural network (SC-FITNET) was compared with the sine cosine feedforward neural network (SCFFNN), feedforward neural network (FFNN) and long short-term memory (LSTM) approaches. The results showed that the proposed SC-FITNET outperformed LSTM, SC-FFNN and FFNN imputation in terms of mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R), with an average accuracy of 90.9%. This study revealed that as the percentage of missingness increased, the precision of the four imputation methods reduced. In addition, this study also revealed that PCA has potential in pre-processing meteorological data into an understandable format for the missing data imputation. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T12:26:22Z No. of bitstreams: 1 ijimai6_7_4.pdf: 2067955 bytes, checksum: 2af2444d93e5c0c255b6cad33152c6a1 (MD5); Made available in DSpace on 2022-05-03T12:26:22Z (GMT). No. of bitstreams: 1 ijimai6_7_4.pdf: 2067955 bytes, checksum: 2af2444d93e5c0c255b6cad33152c6a1 (MD5)

Integration of Genetic Programming and TABU Search Mechanism for Automatic Detection of Magnetic Resonance Imaging in Cervical Spondylosis

Integration of Genetic Programming and TABU Search Mechanism for Automatic Detection of Magnetic Resonance Imaging in Cervical Spondylosis Juan, Chun-Jung; Wang, Chen-Shu; Lee, Bo-Yi; Chiang, Shang-Yu; Yeh, Chun-Chang; Cho, Der-Yang; Shen, Wu-Chung Cervical spondylosis is a kind of degenerative disease which not only occurs in elder patients. The age distribution of patients is unfortunately decreasing gradually. Magnetic Resonance Imaging (MRI) is the best tool to confirm the cervical spondylosis severity but it requires radiologist to spend a lot of time for image check and interpretation. In this study, we proposed a prediction model to evaluate the cervical spine condition of patients by using MRI data. Furthermore, to ensure the computing efficiency of the proposed model, we adopted a heuristic programming, genetic programming (GP), to build the core of refereeing engine by combining the TABU search (TS) with the evolutionary GP. Finally, to validate the accuracy of the proposed model, we implemented experiments and compared our prediction results with radiologist’s diagnosis to the same MRI image. The experiment found that using clinical indicators to optimize the TABU list in GP+TABU got better fitness than the other two methods and the accuracy rate of our proposed model can achieve 88% on average. We expected the proposed model can help radiologists reduce the interpretation effort and improve the relationship between doctors and patients. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T12:05:15Z No. of bitstreams: 1 ijimai6_7_11.pdf: 2984088 bytes, checksum: 9d394359a3de2e44d7cbf1bac731ef61 (MD5); Made available in DSpace on 2022-05-03T12:05:15Z (GMT). No. of bitstreams: 1 ijimai6_7_11.pdf: 2984088 bytes, checksum: 9d394359a3de2e44d7cbf1bac731ef61 (MD5)

Modeling of Performance Creative Evaluation Driven by Multimodal Affective Data

Modeling of Performance Creative Evaluation Driven by Multimodal Affective Data Wu, Yufeng; Zhang, Longfei; Ding, Gangyi; Xue, Tong; Zhang, Fuquan Performance creative evaluation can be achieved through affective data, and the use of affective featuresto evaluate performance creative is a new research trend. This paper proposes a “Performance Creative—Multimodal Affective (PC-MulAff)” model based on the multimodal affective features for performance creative evaluation. The multimedia data acquisition equipment is used to collect the physiological data of the audience, including the multimodal affective data such as the facial expression, heart rate and eye movement. Calculate affective features of multimodal data combined with director annotation, and defined “Performance Creative—Affective Acceptance (PC-Acc)” based on multimodal affective features to evaluate the quality of performance creative. This paper verifies the PC-MulAff model on different performance data sets. The experimental results show that the PC-MulAff model shows high evaluation quality in different performance forms. In the creative evaluation of dance performance, the accuracy of the model is 7.44% and 13.95% higher than that of the single textual and single video evaluation. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T11:58:51Z No. of bitstreams: 1 ijimai6_7_9.pdf: 4988076 bytes, checksum: 9c5e1818386608466023e1d750104196 (MD5); Made available in DSpace on 2022-05-03T11:58:51Z (GMT). No. of bitstreams: 1 ijimai6_7_9.pdf: 4988076 bytes, checksum: 9c5e1818386608466023e1d750104196 (MD5)

Optimal QoE Scheduling in MPEG-DASH Video Streaming

Optimal QoE Scheduling in MPEG-DASH Video Streaming Chang, Shin-Hung; Tsai, Min-Lun; Lee, Meng-Huang; Ho, Jan-Ming DASH is a popular technology for video streaming over the Internet. However, the quality of experience (QoE), a measure of humans’ perceived satisfaction of the quality of these streamed videos, is their subjective opinion, which is difficult to evaluate. Previous studies only considered network-based indices and focused on them to provide smooth video playback instead of improving the true QoE experienced by humans. In this study, we designed a series of click density experiments to verify whether different resolutions could affect the QoE for different video scenes. We observed that, in a single video segment, different scenes with the same resolution could affect the viewer’s QoE differently. It is true that the user’s satisfaction as a result of watching high-resolution video segments is always greater than that when watching low-resolution video segments of the same scenes. However, the most important observation is that low-resolution video segments yield higher viewing QoE gain in slow motion scenes than in fast motion scenes. Thus, the inclusion of more high-resolution segments in the fast motion scenes and more low-resolution segments in the slow motion scenes would be expected to maximize the user’s viewing QoE. In this study, to evaluate the user’s true experience, we convert the viewing QoE into a satisfaction quality score, termed the Q-score, for scenes with different resolutions in each video segment. Additionally, we developed an optimal segment assignment (OSA) algorithm for Q-score optimization in environments characterized by a constrained network bandwidth. Our experimental results show that application of the OSA algorithm to the playback schedule significantly improved users’ viewing satisfaction. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T11:40:28Z No. of bitstreams: 1 ijimai6_7_7.pdf: 5765364 bytes, checksum: 48622abe49860609c898f21c6f7b9269 (MD5); Made available in DSpace on 2022-05-03T11:40:28Z (GMT). No. of bitstreams: 1 ijimai6_7_7.pdf: 5765364 bytes, checksum: 48622abe49860609c898f21c6f7b9269 (MD5)

Modified YOLOv4-DenseNet Algorithm for Detection of Ventricular Septal Defects in Ultrasound Images

Modified YOLOv4-DenseNet Algorithm for Detection of Ventricular Septal Defects in Ultrasound Images Chen, Shih-Hsin; Wang, Chun-Wei; Tai, I-Hsin; Weng, Ken-Pen; Chen, Yi-Hui; Hsieh, Kai-Sheng Doctors conventionally analyzed echocardiographic images for diagnosing congenital heart diseases (CHDs). However, this process is laborious and depends on the experience of the doctors. This study investigated the use of deep learning algorithms for the image detection of the ventricular septal defect (VSD), the most common type. Color Doppler echocardiographic images containing three types of VSDs were tested with color doppler ultrasound medical images. To the best of our knowledge, this study is the first one to solve this object detection problem by using a modified YOLOv4–DenseNet framework. Because some techniques of YOLOv4 are not suitable for echocardiographic object detection, we revised the algorithm for this problem. The results revealed that the YOLOv4–DenseNet outperformed YOLOv4, YOLOv3, YOLOv3–SPP, and YOLOv3–DenseNet in terms of metric mAP-50. The F1-score of YOLOv4-DenseNet and YOLOv3-DenseNet were better than those of others. Hence, the contribution of this study establishes the feasibility of using deep learning for echocardiographic image detection of VSD investigation and a better YOLOv4-DenseNet framework could be employed for the VSD detection. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T11:09:38Z No. of bitstreams: 1 ijimai6_7_10.pdf: 2345492 bytes, checksum: 2425a3a4bbc97ba66c00fefb13bfe324 (MD5); Made available in DSpace on 2022-05-03T11:09:38Z (GMT). No. of bitstreams: 1 ijimai6_7_10.pdf: 2345492 bytes, checksum: 2425a3a4bbc97ba66c00fefb13bfe324 (MD5)

Pulmonary Nodule Classification in Lung Cancer from 3D Thoracic CT Scans Using fastai and MONAI

Pulmonary Nodule Classification in Lung Cancer from 3D Thoracic CT Scans Using fastai and MONAI Kaliyugarasan, Satheshkumar; Lundervold, Arvid; Lundervold, Alexander Selvikvåg We construct a convolutional neural network to classify pulmonary nodules as malignant or benign in the context of lung cancer. To construct and train our model, we use our novel extension of the fastai deep learning framework to 3D medical imaging tasks, combined with the MONAI deep learning library. We train and evaluate the model using a large, openly available data set of annotated thoracic CT scans. Our model achieves a nodule classification accuracy of 92.4% and a ROC AUC of 97% when compared to a “ground truth” based on multiple human raters subjective assessment of malignancy. We further evaluate our approach by predicting patient-level diagnoses of cancer, achieving a test set accuracy of 75%. This is higher than the 70% obtained by aggregating the human raters assessments. Class activation maps are applied to investigate the features used by our classifier, enabling a rudimentary level of explainability for what is otherwise close to “black box” predictions. As the classification of structures in chest CT scans is useful across a variety of diagnostic and prognostic tasks in radiology, our approach has broad applicability. As we aimed to construct a fully reproducible system that can be compared to new proposed methods and easily be adapted and extended, the full source code of our work is available at https://github.com/MMIV-ML/Lung-CT-fastai-2020. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T11:00:30Z No. of bitstreams: 1 ijimai6_7_8.pdf: 4159041 bytes, checksum: 2d9ab0ea8671e64fe47a484809bd380d (MD5); Made available in DSpace on 2022-05-03T11:00:30Z (GMT). No. of bitstreams: 1 ijimai6_7_8.pdf: 4159041 bytes, checksum: 2d9ab0ea8671e64fe47a484809bd380d (MD5)

A Generalized Wine Quality Prediction Framework by Evolutionary Algorithms

A Generalized Wine Quality Prediction Framework by Evolutionary Algorithms Hui-Ye Chiu, Terry; Wu, Chienwen; Chen, Chun-Hao Wine is an exciting and complex product with distinctive qualities that makes it different from other manufactured products. Therefore, the testing approach to determine the quality of wine is complex and diverse. Several elements influence wine quality, but the views of experts can cause the most considerable influence on how people view the quality of wine. The views of experts on quality is very subjective, and may not match the taste of consumer. In addition, the experts may not always be available for the wine testing. To overcome this issue, many approaches based on machine learning techniques that get the attention of the wine industry have been proposed to solve it. However, they focused only on using a particular classifier with a specific set of wine dataset. In this paper, we thus firstly propose the generalized wine quality prediction framework to provide a mechanism for finding a useful hybrid model for wine quality prediction. Secondly, based on the framework, the generalized wine quality prediction algorithm using the genetic algorithms is proposed. It first encodes the classifiers as well as their hyperparameters into a chromosome. The fitness of a chromosome is then evaluated by the average accuracy of the employed classifiers. The genetic operations are performed to generate new offspring. The evolution process is continuing until reaching the stop criteria. As a result, the proposed approach can automatically find an appropriate hybrid set of classifiers and their hyperparameters for optimizing the prediction result and independent on the dataset. At last, experiments on the wine datasets were made to show the merits and effectiveness of the proposed approach. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T10:36:21Z No. of bitstreams: 1 ijimai6_7_6.pdf: 2091621 bytes, checksum: 8181aed690d44bb4cf32cd015ab0582d (MD5); Made available in DSpace on 2022-05-03T10:36:21Z (GMT). No. of bitstreams: 1 ijimai6_7_6.pdf: 2091621 bytes, checksum: 8181aed690d44bb4cf32cd015ab0582d (MD5)

Alzheimer Disease Detection Techniques and Methods: A Review

Alzheimer Disease Detection Techniques and Methods: A Review Afzal, Sitara; Maqsood, Muazzam; Khan, Umair; Mehmood, Irfan; Nawaz, Hina; Aadil, Farhan; Song, Oh-Young; Yunyoung, Nam Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T10:29:48Z No. of bitstreams: 1 ijimai6_7_3.pdf: 1256360 bytes, checksum: c7087c8a9916347c36d1dbc9f1985568 (MD5); Made available in DSpace on 2022-05-03T10:29:48Z (GMT). No. of bitstreams: 1 ijimai6_7_3.pdf: 1256360 bytes, checksum: c7087c8a9916347c36d1dbc9f1985568 (MD5)

Design and Development of an Energy Efficient Multimedia Cloud Data Center with Minimal SLA Violation

Design and Development of an Energy Efficient Multimedia Cloud Data Center with Minimal SLA Violation Biswas, Nirmal Kr.; Banerjee, Sourav; Biswas, Utpal Multimedia computing (MC) is rising as a nascent computing paradigm to process multimedia applications and provide efficient multimedia cloud services with optimal Quality of Service (QoS) to the multimedia cloud users. But, the growing popularity of MC is affecting the climate. Because multimedia cloud data centers consume an enormous amount of energy to provide services, it harms the environment due to carbon dioxide emissions. Virtual machine (VM) migration can effectively address this issue; it reduces the energy consumption of multimedia cloud data centers. Due to the reduction of Energy Consumption (EC), the Service Level Agreement violation (SLAV) may increase. An efficient VM selection plays a crucial role in maintaining the stability between EC and SLAV. This work highlights a novel VM selection policy based on identifying the Maximum value among the differences of the Sum of Squares Utilization Rate (MdSSUR) parameter to reduce the EC of multimedia cloud data centers with minimal SLAV. The proposed MdSSUR VM selection policy has been evaluated using real workload traces in CloudSim. The simulation result of the proposed MdSSUR VM selection policy demonstrates the rate of improvements of the EC, the number of VM migrations, and the SLAV by 28.37%, 89.47%, and 79.14%, respectively. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T09:50:01Z No. of bitstreams: 1 ijimai6_7_5.pdf: 1978034 bytes, checksum: d3cbcf721b71de8db1dddea4644721f4 (MD5); Made available in DSpace on 2022-05-03T09:50:01Z (GMT). No. of bitstreams: 1 ijimai6_7_5.pdf: 1978034 bytes, checksum: d3cbcf721b71de8db1dddea4644721f4 (MD5)

A Novel Fog Computing Approach for Minimization of Latency in Healthcare using Machine Learning

A Novel Fog Computing Approach for Minimization of Latency in Healthcare using Machine Learning Kishor, Amit; Chakraborty, Chinmay; Jeberson, Wilson In the recent scenario, the most challenging requirements are to handle the massive generation of multimedia data from the Internet of Things (IoT) devices which becomes very difficult to handle only through the cloud. Fog computing technology emerges as an intelligent solution and uses a distributed environment to operate. The objective of the paper is latency minimization in e-healthcare through fog computing. Therefore, in IoT multimedia data transmission, the parameters such as transmission delay, network delay, and computation delay must be reduced as there is a high demand for healthcare multimedia analytics. Fog computing provides processing, storage, and analyze the data nearer to IoT and end-users to overcome the latency. In this paper, the novel Intelligent Multimedia Data Segregation (IMDS) scheme using Machine learning (k-fold random forest) is proposed in the fog computing environment that segregates the multimedia data and the model used to calculate total latency (transmission, computation, and network). With the simulated results, we achieved 92% as the classification accuracy of the model, an approximately 95% reduction in latency as compared with the pre-existing model, and improved the quality of services in e-healthcare. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T09:22:41Z No. of bitstreams: 1 ijimai6_7_1.pdf: 2233977 bytes, checksum: 28018ceafc10d13be7a3337606e0dd14 (MD5); Made available in DSpace on 2022-05-03T09:22:41Z (GMT). No. of bitstreams: 1 ijimai6_7_1.pdf: 2233977 bytes, checksum: 28018ceafc10d13be7a3337606e0dd14 (MD5)

An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs

An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs Srivastava, Varun; Gupta, Shilp; Chaudhary, Gopal; Balodi, Arun; Khari, Manju; García-Díaz, Vicente Content Based Image Retrieval (CBIR) techniques based on texture have gained a lot of popularity in recent times. In the proposed work, a feature vector is obtained by concatenation of features extracted from local mesh peak valley edge pattern (LMePVEP) technique; a dynamic threshold based local mesh ternary pattern technique and texture of the image in five different directions. The concatenated feature vector is then used to classify images of two datasets viz. Emphysema dataset and Early Lung Cancer Action Program (ELCAP) lung database. The proposed framework has improved the accuracy by 12.56%, 9.71% and 7.01% in average for data set 1 and 9.37%, 8.99% and 7.63% in average for dataset 2 over three popular algorithms used for image retrieval. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T09:07:16Z No. of bitstreams: 1 ijimai6_7_2.pdf: 1271084 bytes, checksum: 0aa7a702bd7f3d0dcc56163946b02a5a (MD5); Made available in DSpace on 2022-05-03T09:07:16Z (GMT). No. of bitstreams: 1 ijimai6_7_2.pdf: 1271084 bytes, checksum: 0aa7a702bd7f3d0dcc56163946b02a5a (MD5)

Editor's Note

Editor's Note Martínez Torres, Javier The International Journal of Interactive Multimedia and Artificial Intelligence – IJIMAI (ISSN 1989-1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. The present volume, June volume, consists of 24 articles of diverse applications of great impact in different fields, always having as a common element the use of artificial intelligence techniques or mathematical models with an artificial intelligence base. As is logical, COVID is present in several manuscripts of this volume, always focused on the prediction and estimation of the presence of the disease. In addition to this expected presence, there are manuscripts of a semantic or syntactic analysis nature as well as works in the field of management and recommender systems. It is also worth mentioning several works in the field of video compression and signal processing. Of course, the Internet of Things and text analysis for several applications could not be missed in this volume. Finally, different manuscripts on usability and satisfaction, investments, solar panels, malware detection, video analysis, audio analysis and learning can also be found in this volume. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T10:24:42Z No. of bitstreams: 1 ijimai_6_6_0.pdf: 457899 bytes, checksum: 341ee1b31869f052722760878a3d09a1 (MD5); Made available in DSpace on 2022-04-29T10:24:42Z (GMT). No. of bitstreams: 1 ijimai_6_6_0.pdf: 457899 bytes, checksum: 341ee1b31869f052722760878a3d09a1 (MD5)

Your Teammate Just Sent You a New Message! The Effects of Using Telegram on Individual Acquisition of Teamwork Competence

Your Teammate Just Sent You a New Message! The Effects of Using Telegram on Individual Acquisition of Teamwork Competence Conde, Miguel Á.; Rodríguez-Sedano, Francisco J.; Hernández-García, Ángel; Gutiérrez-Fernández, Alexis; Guerrero-Higueras, Ángel M. Students’ acquisition of teamwork competence has become a priority for educational institutions. The development of teamwork competence in education generally relies in project-based learning methodologies and challenges. The assessment of teamwork in project-based learning involves, among others, assessing students’ participation and the interactions between team members. Project-based learning can easily be handled in small-size courses, but course management and teamwork assessment become a burdensome task for instructors as the size of the class increases. Additionally, when project-based learning happens in a virtual space, such as online learning, interactions occur in a less natural way. This study explores the use of instant messaging apps (more precisely, the use of Telegram) as team communication space in project-based learning, using a learning analytics tool to extract and analyze student interactions. Further, the study compares student interactions (e.g., number of messages exchanged) and individual teamwork competence acquisition between traditional asynchronous (e.g., LMS message boards) and synchronous instant messaging communication environments. The results show a preference of students for IM tools and increased participation in the course. However, the analysis does not find significant improvement in the acquisition of individual teamwork competence. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T10:21:53Z No. of bitstreams: 1 ijimai_6_6_23.pdf: 909024 bytes, checksum: 90460b7f5f70b467031371f78ea08dff (MD5); Made available in DSpace on 2022-04-29T10:21:53Z (GMT). No. of bitstreams: 1 ijimai_6_6_23.pdf: 909024 bytes, checksum: 90460b7f5f70b467031371f78ea08dff (MD5)

Bayesian Knowledge Tracing for Navigation through Marzano’s Taxonomy

Bayesian Knowledge Tracing for Navigation through Marzano’s Taxonomy Cervantes-Perez, Francisco; Navarro-Perales, Joaquin; Franzoni-Velázquez, Ana L.; de-la-Fuente-Valentín, Luis In this paper we propose a theoretical model of an ITS (Intelligent Tutoring Systems) capable of improving and updating computer-aided navigation based on Bloom’s taxonomy. For this we use the Bayesian Knowledge Tracing algorithm, performing an adaptive control of the navigation among different levels of cognition in online courses. These levels are defined by a taxonomy of educational objectives with a hierarchical order in terms of the control that some processes have over others, called Marzano’s Taxonomy, that takes into account the metacognitive system, responsible for the creation of goals as well as strategies to fulfill them. The main improvements of this proposal are: 1) An adaptive transition between individual assessment questions determined by levels of cognition. 2) A student model based on the initial response of a group of learners which is then adjusted to the ability of each learner. 3) The promotion of metacognitive skills such as goal setting and self-monitoring through the estimation of attempts required to pass the levels. One level of Marzano's taxonomy was left in the hands of the human teacher, clarifying that a differentiation must be made between the tasks in which an ITS can be an important aid and in which it would be more difficult. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T10:13:40Z No. of bitstreams: 1 ijimai_6_6_24.pdf: 721459 bytes, checksum: 1120f6837e112c002e2ef61b9a79ce15 (MD5); Made available in DSpace on 2022-04-29T10:13:40Z (GMT). No. of bitstreams: 1 ijimai_6_6_24.pdf: 721459 bytes, checksum: 1120f6837e112c002e2ef61b9a79ce15 (MD5)

Application of Artificial Intelligence Algorithms Within the Medical Context for Non-Specialized Users: the CARTIER-IA Platform

Application of Artificial Intelligence Algorithms Within the Medical Context for Non-Specialized Users: the CARTIER-IA Platform García-Peñalvo, Francisco; Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; Sampedro-Gómez, Jesús; Sánchez-Puente, Antonio; Vicente-Palacios, Víctor; Dorado-Díaz, P. Ignacio; Sánchez, Pedro L. The use of advanced algorithms and models such as Machine Learning, Deep Learning and other related approaches of Artificial Intelligence have grown in their use given their benefits in different contexts. One of these contexts is the medical domain, as these algorithms can support disease detection, image segmentation and other multiple tasks. However, it is necessary to organize and arrange the different data resources involved in these scenarios and tackle the heterogeneity of data sources. This work presents the CARTIER-IA platform: a platform for the management of medical data and imaging. The goal of this project focuses on providing a friendly and usable interface to organize structured data, to visualize and edit medical images, and to apply Artificial Intelligence algorithms on the stored resources. One of the challenges of the platform design is to ease these complex tasks in a way that non-AI-specialized users could benefit from the application of AI algorithms without further training. Two use cases of AI application within the platform are provided, as well as a heuristic evaluation to assess the usability of the first version of CARTIER-IA. Year of Publication 2021 Journal International Journal of Interactive Multimedia and Artificial Intelligence Volume 6 Issue Regular Issue Number 6 Number of Pages 46-53 Date Published 06/2021 ISSN Number 1989-1660 URL https://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_5.pdf DOI 10.9781/ijimai.2021.05.005 DOI Google Scholar BibTeX EndNote X3 XML EndNote 7 XML Endnote tagged Marc RIS Attachment ijimai_6_6_5.pdf 932.11 KB Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T09:35:07Z No. of bitstreams: 1 ijimai_6_6_5.pdf: 959452 bytes, checksum: e608a8b469e8938f546009d1144c3771 (MD5); Made available in DSpace on 2022-04-29T09:35:07Z (GMT). No. of bitstreams: 1 ijimai_6_6_5.pdf: 959452 bytes, checksum: e608a8b469e8938f546009d1144c3771 (MD5)

Dynamic Generation of Investment Recommendations Using Grammatical Evolution

Dynamic Generation of Investment Recommendations Using Grammatical Evolution Martín, Carlos; Quintana, David; Isasi, Pedro The attainment of trading rules using Grammatical Evolution traditionally follows a static approach. A single rule is obtained and then used to generate investment recommendations over time. The main disadvantage of this approach is that it does not consider the need to adapt to the structural changes that are often associated with financial time series. We improve the canonical approach introducing an alternative that involves a dynamic selection mechanism that switches between an active rule and a candidate one optimized for the most recent market data available. The proposed solution seeks the flexibility required by structural changes while limiting the transaction costs commonly associated with constant model updates. The performance of the algorithm is compared with four alternatives: the standard static approach; a sliding window-based generation of trading rules that are used for a single time period, and two ensemble-based strategies. The experimental results, based on market data, show that the suggested approach beats the rest. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T09:27:23Z No. of bitstreams: 1 ijimai_6_6_11.pdf: 907215 bytes, checksum: 59cf972c00417070817819dee6853ca6 (MD5); Made available in DSpace on 2022-04-29T09:27:23Z (GMT). No. of bitstreams: 1 ijimai_6_6_11.pdf: 907215 bytes, checksum: 59cf972c00417070817819dee6853ca6 (MD5)

Automated Detection of COVID-19 using Chest X-Ray Images and CT Scans through Multilayer- Spatial Convolutional Neural Networks

Automated Detection of COVID-19 using Chest X-Ray Images and CT Scans through Multilayer- Spatial Convolutional Neural Networks Khattak, Muhammad Irfan; Al-Hasan, Mu’ath; Jan, Atif; Saleem, Nasir; Verdú, Elena; Khurshid, Numan The novel coronavirus-2019 (Covid-19), a contagious disease became a pandemic and has caused overwhelming effects on the human lives and world economy. The detection of the contagious disease is vital to avert further spread and to promptly treat the infected people. The need of automated scientific assisting diagnostic methods to identify Covid-19 in the infected people has increased since less accurate automated diagnostic methods are available. Recent studies based on the radiology imaging suggested that the imaging patterns on X-ray images and Computed Tomography (CT) scans contain leading information about Covid-19 and is considered as a potential automated diagnosis method. Machine learning and deep learning techniques combined with radiology imaging can be helpful for accurate detection of the disease. A deep learning approach based on the multilayer-Spatial Convolutional Neural Network for automatic detection of Covid-19 using chest X-ray images and CT scans is proposed in this paper. The proposed model, named as the Multilayer Spatial Covid Convolutional Neural Network (MSCovCNN), provides an automated accurate diagnostics for Covid-19 detection. The proposed model showed 93.63% detection accuracy and 97.88% AUC (Area Under Curve) for chest x-ray images and 91.44% detection accuracy and 95.92% AUC for chest CT scans, respectively. We have used 5-tiered 2D-CNN frameworks followed by the Artificial Neural Network (ANN) and softmax classifier. In the CNN each convolution layer is followed by an activation function and a Maxpooling layer. The proposed model can be used to assist the radiologists in detecting the Covid-19 and confirming their initial screening. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T09:03:57Z No. of bitstreams: 1 ijimai_6_6_2.pdf: 1585230 bytes, checksum: 48ce6957e4c5c2a1f6b3eeb0aab59ae4 (MD5); Made available in DSpace on 2022-04-29T09:03:57Z (GMT). No. of bitstreams: 1 ijimai_6_6_2.pdf: 1585230 bytes, checksum: 48ce6957e4c5c2a1f6b3eeb0aab59ae4 (MD5)

COVID-19 Mortality Risk Prediction Using X-Ray Images

COVID-19 Mortality Risk Prediction Using X-Ray Images Prada, J.; Gala, Y.; Sierra, A. L. The pandemic caused by coronavirus COVID-19 has already had a massive impact in our societies in terms of health, economy, and social distress. One of the most common symptoms caused by COVID-19 are lung problems like pneumonia, which can be detected using X-ray images. On the other hand, the popularity of Machine Learning models has grown exponentially in recent years and Deep Learning techniques have become the state-of-the-art for image classification tasks and is widely used in the healthcare sector nowadays as support for clinical decisions. This research aims to build a prediction model based on Machine Learning, including Deep Learning, techniques to predict the mortality risk of a particular patient given an X-ray and some basic demographic data. Keeping this in mind, this paper has three goals. First, we use Deep Learning models to predict the mortality risk of a patient based on this patient X-ray images. For this purpose, we apply Convolutional Neural Networks as well as Transfer Learning techniques to mitigate the effect of the reduced amount of COVID19 data available. Second, we propose to combine the prediction of this Convolutional Neural Network with other patient data, like gender and age, as input features of a final Machine Learning model, that will act as second and final layer. This second model layer will aim to improve the goodness of fit and prediction power of our first layer. Finally, and in accordance with the principle of reproducible research, the data used for the experiments is publicly available and we make the implementations developed easily accessible via public repositories. Experiments over a real dataset of COVID-19 patients yield high AUROC values and show our two-layer framework to obtain better results than a single Convolutional Neural Network (CNN) model, achieving close to perfect classification. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T08:33:44Z No. of bitstreams: 1 ijimai_6_6_1.pdf: 939424 bytes, checksum: 6025ed2d968c7b8d55675ed8f445b809 (MD5); Made available in DSpace on 2022-04-29T08:33:44Z (GMT). No. of bitstreams: 1 ijimai_6_6_1.pdf: 939424 bytes, checksum: 6025ed2d968c7b8d55675ed8f445b809 (MD5)

Machine Learning Classifier Approach with Gaussian Process, Ensemble boosted Trees, SVM, and Linear Regression for 5G Signal Coverage Mapping

Machine Learning Classifier Approach with Gaussian Process, Ensemble boosted Trees, SVM, and Linear Regression for 5G Signal Coverage Mapping Gupta, Akansha; Ghanshala, Kamal; Joshi, R. C. This article offers a thorough analysis of the machine learning classifiers approaches for the collected Received Signal Strength Indicator (RSSI) samples which can be applied in predicting propagation loss, used for network planning to achieve maximum coverage. We estimated the RMSE of a machine learning classifier on multivariate RSSI data collected from the cluster of 6 Base Transceiver Stations (BTS) across a hilly terrain of Uttarakhand-India. Variable attributes comprise topology, environment, and forest canopy. Four machine learning classifiers have been investigated to identify the classifier with the least RMSE: Gaussian Process, Ensemble Boosted Tree, SVM, and Linear Regression. Gaussian Process showed the lowest RMSE, R- Squared, MSE, and MAE of 1.96, 0.98, 3.8774, and 1.3202 respectively as compared to other classifiers. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T08:27:49Z No. of bitstreams: 1 ijimai_6_6_16.pdf: 1680641 bytes, checksum: 670693000493b4e0a77b5ea2cbd27745 (MD5); Made available in DSpace on 2022-04-29T08:27:49Z (GMT). No. of bitstreams: 1 ijimai_6_6_16.pdf: 1680641 bytes, checksum: 670693000493b4e0a77b5ea2cbd27745 (MD5)

A Smart Collaborative Educational Game with Learning Analytics to Support English Vocabulary Teaching

A Smart Collaborative Educational Game with Learning Analytics to Support English Vocabulary Teaching Tlili, Ahmed; Hattab, Sarra; Essalmi, Fathi; Chen, Nian-Shing; Huang, Ronghuai; Kinshuk; Chang, Maiga; Burgos, Daniel Learning Analytics (LA) approaches have proved to be able to enhance learning process and learning performance. However, little is known about applying these approaches for second language acquisition using educational games. Therefore, this study applied LA approaches to design a smart collaborative educational game, to enhance primary school children learning English vocabularies. Specifically, the game provided dashboards to the teachers about their students in a real-time manner. A pilot experiment was conducted in a public primary school where the students’ data from experimental and control groups, namely learning and motivation test scores, interview and observation, were collected and analyzed. The obtained results showed that the experimental group (who used the smart game with LA) had significantly higher motivation and performance for learning English vocabularies than the control group (who used the smart game without LA). The findings of this study can help researchers and practitioners incorporate LA in their educational games to help students enhance language acquisition. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T08:22:06Z No. of bitstreams: 1 ijimai_6_6_22.pdf: 817089 bytes, checksum: 20fbc2f29f5b6ea1a2615094fd41a6f8 (MD5); Made available in DSpace on 2022-04-29T08:22:06Z (GMT). No. of bitstreams: 1 ijimai_6_6_22.pdf: 817089 bytes, checksum: 20fbc2f29f5b6ea1a2615094fd41a6f8 (MD5)

BILROST: Handling Actuators of the Internet of Things through Tweets on Twitter using a Domain- Specific Language

BILROST: Handling Actuators of the Internet of Things through Tweets on Twitter using a Domain- Specific Language Meana-Llorián, Daniel; González García, Cristian; Pelayo García-Bustelo, B. Cristina; Cueva-Lovelle, Juan Manuel In recent years, many investigations have appeared that combine the Internet of Things and Social Networks. Some of them addressed the interconnection of objects as Social Networks interconnect people, and others addressed the connection between objects and people. However, they usually used interfaces created for that purpose instead of using familiar interfaces for users. Why not integrate Smart Objects in traditional Social Networks? Why not control Smart Objects through natural interactions in Social Networks? The goal of this paper is to make easier to create applications that allow non-experts users to control Smart Objects actuators through Social Networks through the proposal of a novel approach to connect objects and people using Social Networks. This proposal will address how to use Twitter so that objects could perform actions based on Twitter users’ posts. Moreover, it will be presented a Domain-Specific language that could help in the task of defining the actions that objects could perform when people publish specific content on Twitter. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T08:13:49Z No. of bitstreams: 1 ijimai_6_6_14.pdf: 1246461 bytes, checksum: 5542390fb3da78d960a26cf9bd55b936 (MD5); Made available in DSpace on 2022-04-29T08:13:49Z (GMT). No. of bitstreams: 1 ijimai_6_6_14.pdf: 1246461 bytes, checksum: 5542390fb3da78d960a26cf9bd55b936 (MD5)

Motivic Pattern Classification of Music Audio Signals Combining Residual and LSTM Networks

Motivic Pattern Classification of Music Audio Signals Combining Residual and LSTM Networks Arronte Alvarez, Aitor; Gómez, Francisco Motivic pattern classification from music audio recordings is a challenging task. More so in the case of a cappella flamenco cantes, characterized by complex melodic variations, pitch instability, timbre changes, extreme vibrato oscillations, microtonal ornamentations, and noisy conditions of the recordings. Convolutional Neural Networks (CNN) have proven to be very effective algorithms in image classification. Recent work in large-scale audio classification has shown that CNN architectures, originally developed for image problems, can be applied successfully to audio event recognition and classification with little or no modifications to the networks. In this paper, CNN architectures are tested in a more nuanced problem: flamenco cantes intra-style classification using small motivic patterns. A new architecture is proposed that uses the advantages of residual CNN as feature extractors, and a bidirectional LSTM layer to exploit the sequential nature of musical audio data. We present a full end-to-end pipeline for audio music classification that includes a sequential pattern mining technique and a contour simplification method to extract relevant motifs from audio recordings. Mel-spectrograms of the extracted motifs are then used as the input for the different architectures tested. We investigate the usefulness of motivic patterns for the automatic classification of music recordings and the effect of the length of the audio and corpus size on the overall classification accuracy. Results show a relative accuracy improvement of up to 20.4% when CNN architectures are trained using acoustic representations from motivic patterns. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T07:23:45Z No. of bitstreams: 1 ijimai_6_6_21.pdf: 811793 bytes, checksum: 7b2f371b80260dbb4db6c5483b1d4980 (MD5); Made available in DSpace on 2022-04-29T07:23:45Z (GMT). No. of bitstreams: 1 ijimai_6_6_21.pdf: 811793 bytes, checksum: 7b2f371b80260dbb4db6c5483b1d4980 (MD5)

An Effective Tool for the Experts' Recommendation Based on PROMETHEE II and Negotiation: Application to the Industrial Maintenance

An Effective Tool for the Experts' Recommendation Based on PROMETHEE II and Negotiation: Application to the Industrial Maintenance Houari, Nawal Sad; Taghezout, Noria In this article, we propose an expert recommendation tool that relies on the skills of experts and their interventions in collaboration. This tool provides us with a list of the most appropriate (effective) experts to solve business problems in the field of industrial maintenance. The proposed system recommends experts using an unsupervised classification algorithm that takes into account the competences of the experts, their preferences and the stored information in previous collaborative sessions. We have tested the performance of the system with K-means and C-means algorithms. To fix the inconsistencies detected in business rules, the PROMETHEE II multi-criteria decision support method is integrated into the extended CNP negotiation protocol in order to classify the experts from best to worst. The study is supported by the well known petroleum company in Algeria namely SONATRACH where the experimentations are operated on maintenance domain. Experiments results show the effectiveness of our approach, obtaining a recall of 86%, precision of 92% and F-measure of 89%. Also, the proposed approach offers very high results and improvement, in terms of response time (154.28 ms), space memory (9843912 bytes) and negotiation rounds. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T06:50:21Z No. of bitstreams: 1 ijimai_6_6_7.pdf: 875379 bytes, checksum: 9eb309c53b8e247d9edca06143403261 (MD5); Made available in DSpace on 2022-04-29T06:50:21Z (GMT). No. of bitstreams: 1 ijimai_6_6_7.pdf: 875379 bytes, checksum: 9eb309c53b8e247d9edca06143403261 (MD5)

Does a presentation Media Influence the Evaluation of Consumer Products? A Comparative Study to Evaluate Virtual Reality, Virtual Reality with Passive Haptics and a Real Setting

Does a presentation Media Influence the Evaluation of Consumer Products? A Comparative Study to Evaluate Virtual Reality, Virtual Reality with Passive Haptics and a Real Setting Galán, Julia; García-García, Carlos; Felip, Francisco; Contero, Manuel Technologies based on image offer a high potential to present consumers with products by focusing on their visual characteristics, but lack the capacity to physically interact with an object, which can compromise how consumer products are evaluated. The present study aims to analyse the influence of different presentation media on how users perceive the product by comparing the evaluation of a piece of furniture made by a sample of 203 users, which was presented in three different settings: a real setting (R), a Virtual Reality setting (VR) and a Virtual Reality with Passive Haptics setting (VRPH). To evaluate the product in the different settings, a semantic differential scale was built that comprised 12 bipolar pairs of adjectives. To study the results, the descriptive statistics for the semantic differential scales were analysed, a study about the frequency of repetition was conducted of each evaluation, a Kruskal-Wallis test was conducted and Dunn’s post hoc tests were performed. The results showed that the presentation media of a piece of furniture influenced the evaluation of how users perceived it. These results also revealed that the haptic interaction with a product influenced how users perceived it compared to an exclusively visual interaction. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T06:45:50Z No. of bitstreams: 1 ijimai_6_6_20.pdf: 1021591 bytes, checksum: 35be3ab88dc1b0d7a61aae9cbb6892bd (MD5); Made available in DSpace on 2022-04-29T06:45:50Z (GMT). No. of bitstreams: 1 ijimai_6_6_20.pdf: 1021591 bytes, checksum: 35be3ab88dc1b0d7a61aae9cbb6892bd (MD5)

What Causes the Dependency between Perceived Aesthetics and Perceived Usability?

What Causes the Dependency between Perceived Aesthetics and Perceived Usability? Schrepp, Martin; Otten, Raphael; Blum, Kerstin; Thomaschewski, Jörg Several studies reported a dependency between perceived beauty and perceived usability of a user interface. But it is still not fully clear which psychological mechanism is responsible for this dependency. We suggest a new explanation based on the concept of visual clarity. This concept describes the perception of order, alignment and visual complexity. A high visual clarity supports a fast orientation on an interface and creates an impression of simplicity. Thus, visual clarity will impact usability dimensions, like efficiency and learnability. Visual clarity is also related to classical aesthetics and the fluency effect, thus an impact on the perception of aesthetics is plausible. We present two large studies that show a strong mediator effect of visual clarity on the dependency between perceived aesthetics and perceived usability. These results support the proposed explanation. In addition, we show how visual clarity of a user interface can be evaluated by a new scale embedded in the UEQ+ framework. Construction and first evaluation results of this new scale are described. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T08:42:38Z No. of bitstreams: 1 ijimai_6_6_8.pdf: 1347702 bytes, checksum: 8429bd93ebee30b2c886f79235fd468f (MD5); Made available in DSpace on 2022-04-28T08:42:38Z (GMT). No. of bitstreams: 1 ijimai_6_6_8.pdf: 1347702 bytes, checksum: 8429bd93ebee30b2c886f79235fd468f (MD5)

The Application of Artificial Intelligence in Project Management Research: A Review

The Application of Artificial Intelligence in Project Management Research: A Review Gil, Jesús; Martínez Torres, Javier; González-Crespo, Rubén The field of artificial intelligence is currently experiencing relentless growth, with innumerable models emerging in the research and development phases across various fields, including science, finance, and engineering. In this work, the authors review a large number of learning techniques aimed at project management. The analysis is largely focused on hybrid systems, which present computational models of blended learning techniques. At present, these models are at a very early stage and major efforts in terms of development is required within the scientific community. In addition, we provide a classification of all the areas within project management and the learning techniques that are used in each, presenting a brief study of the different artificial intelligence techniques used today and the areas of project management in which agents are being applied. This work should serve as a starting point for researchers who wish to work in the exciting world of artificial intelligence in relation to project leadership and management. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T08:36:59Z No. of bitstreams: 1 ijimai_6_6_6.pdf: 789648 bytes, checksum: f48d708495661640a57dcc5467899211 (MD5); Made available in DSpace on 2022-04-28T08:36:59Z (GMT). No. of bitstreams: 1 ijimai_6_6_6.pdf: 789648 bytes, checksum: f48d708495661640a57dcc5467899211 (MD5)

Video Data Compression by Progressive Iterative Approximation

Video Data Compression by Progressive Iterative Approximation Ebadi, M. J.; Ebrahimi, A. In the present paper, the B-spline curve is used for reducing the entropy of video data. We consider the color or luminance variations of a spatial position in a series of frames as input data points in Euclidean space R or R3. The progressive and iterative approximation (PIA) method is a direct and intuitive way of generating curve series of high and higher fitting accuracy. The video data points are approximated using progressive and iterative approximation for least square (LSPIA) fitting. The Lossless video data compression is done through storing the B-spline curve control points (CPs) and the difference between fitted and original video data. The proposed method is applied to two classes of synthetically produced and naturally recorded video sequences and makes a reduction in the entropy of both. However, this reduction is higher for syntactically created than those naturally produced. The comparative analysis of experiments on a variety of video sequences suggests that the entropy of output video data is much less than that of input video data. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T08:31:18Z No. of bitstreams: 1 ijimai_6_6_19.pdf: 1544242 bytes, checksum: 488c0981b7182ecf93ffd699ea5fde42 (MD5); Made available in DSpace on 2022-04-28T08:31:18Z (GMT). No. of bitstreams: 1 ijimai_6_6_19.pdf: 1544242 bytes, checksum: 488c0981b7182ecf93ffd699ea5fde42 (MD5)

Optimal Parameter Estimation of Solar PV Panel Based on Hybrid Particle Swarm and Grey Wolf Optimization Algorithms

Optimal Parameter Estimation of Solar PV Panel Based on Hybrid Particle Swarm and Grey Wolf Optimization Algorithms Rezk, Hegazy; Arfaoui, Jouda; Gomaa, Mohamed R. The performance of a solar photovoltaic (PV) panel is examined through determining its internal parameters based on single and double diode models. The environmental conditions such as temperature and the level of radiation also influence the output characteristics of solar panel. In this research work, the parameters of solar PV panel are identified for the first time, as far as the authors know, using hybrid particle swarm optimization (PSO) and grey wolf optimizer (WGO) based on experimental datasets of I-V curves. The main advantage of hybrid PSOGWO is combining the exploitation ability of the PSO with the exploration ability of the GWO. During the optimization process, the main target is minimizing the root mean square error (RMSE) between the original experimental data and the estimated data. Three different solar PV modules are considered to prove the superiority of the proposed strategy. Three different solar PV panels are used during the evaluation of the proposed strategy. A comparison of PSOGWO with other state-of-the-art methods is made. The obtained results confirmed that the least RMSE values are achieved using PSOGWO for all case studies compared with PSO and GWO optimizers. Almost a perfect agreement between the estimated data and experimental data set is achieved by PSOGWO. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T08:13:19Z No. of bitstreams: 1 ijimai_6_6_15.pdf: 1592860 bytes, checksum: ba41698fc48f67f9dbc1ecb7cc33f714 (MD5); Made available in DSpace on 2022-04-28T08:13:19Z (GMT). No. of bitstreams: 1 ijimai_6_6_15.pdf: 1592860 bytes, checksum: ba41698fc48f67f9dbc1ecb7cc33f714 (MD5)

DeepFair: Deep Learning for Improving Fairness in Recommender Systems

DeepFair: Deep Learning for Improving Fairness in Recommender Systems Bobadilla, Jesús; Lara-Cabrera, Raúl; González-Prieto, Ángel; Ortega, Fernando The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet both criteria. Here we propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy. Furthermore, in the recommendation stage, this balance does not require an initial knowledge of the users’ demographic information. The proposed architecture incorporates four abstraction levels: raw ratings and demographic information, minority indexes, accurate predictions, and fair recommendations. Last two levels use the classical Probabilistic Matrix Factorization (PMF) model to obtain users and items hidden factors, and a Multi-Layer Network (MLN) to combine those factors with a ‘fairness’ (ß) parameter. Several experiments have been conducted using two types of minority sets: gender and age. Experimental results show that it is possible to make fair recommendations without losing a significant proportion of accuracy. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T08:01:38Z No. of bitstreams: 1 ijimai_6_6_9_0.pdf: 1368857 bytes, checksum: de147688d06e532da619d5e1821d9651 (MD5); Made available in DSpace on 2022-04-28T08:01:38Z (GMT). No. of bitstreams: 1 ijimai_6_6_9_0.pdf: 1368857 bytes, checksum: de147688d06e532da619d5e1821d9651 (MD5)

An Empiric Analysis of Wavelet-Based Feature Extraction on Deep Learning and Machine Learning Algorithms for Arrhythmia Classification

An Empiric Analysis of Wavelet-Based Feature Extraction on Deep Learning and Machine Learning Algorithms for Arrhythmia Classification Singh, Ritu; Rajpal, Navin; Mehta, Rajesh The aberration in human electrocardiogram (ECG) affects cardiovascular events that may lead to arrhythmias. Many automation systems for ECG classification exist, but the ambiguity to wisely employ the in-built feature extraction or expert based manual feature extraction before classification still needs recognition. The proposed work compares and presents the enactment of using machine learning and deep learning classification on time series sequences. The two classifiers, namely the Support Vector Machine (SVM) and the Bi-directional Long Short-Term Memory (BiLSTM) network, are separately trained by direct ECG samples and extracted feature vectors using multiresolution analysis of Maximal Overlap Discrete Wavelet Transform (MODWT). Single beat segmentation with R-peaks and QRS detection is also involved with 6 morphological and 12 statistical feature extraction. The two benchmark datasets, multi-class, and binary class, are acquired from the PhysioNet database. For the binary dataset, BiLSTM with direct samples and with feature extraction gives 58.1% and 80.7% testing accuracy, respectively, whereas SVM outperforms with 99.88% accuracy. For the multi-class dataset, BiLSTM classification accuracy with the direct sample and the extracted feature is 49.6% and 95.4%, whereas SVM shows 99.44%. The efficient statistical workout depicts that the extracted feature-based selection of data can deliver distinguished outcomes compared with raw ECG data or in-built automatic feature extraction. The machine learning classifiers like SVM with knowledge-based feature extraction can equally or better perform than Bi-LSTM network for certain datasets. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:48:37Z No. of bitstreams: 1 ijimai_6_6_3.pdf: 1652687 bytes, checksum: 49e79fe25dd77e54c9e2bb1a1286ec90 (MD5); Made available in DSpace on 2022-04-28T07:48:37Z (GMT). No. of bitstreams: 1 ijimai_6_6_3.pdf: 1652687 bytes, checksum: 49e79fe25dd77e54c9e2bb1a1286ec90 (MD5)

Promising Deep Semantic Nuclei Segmentation Models for Multi-Institutional Histopathology Images of Different Organs

Promising Deep Semantic Nuclei Segmentation Models for Multi-Institutional Histopathology Images of Different Organs Hassan, Loay; Saleh, Adel; Abdel-Nasser, Mohamed; Omer, Osama A.; Puig, Domenec Nuclei segmentation in whole-slide imaging (WSI) plays a crucial role in the field of computational pathology. It is a fundamental task for different applications, such as cancer cell type classification, cancer grading, and cancer subtype classification. However, existing nuclei segmentation methods face many challenges, such as color variation in histopathological images, the overlapping and clumped nuclei, and the ambiguous boundary between different cell nuclei, that limit their performance. In this paper, we present promising deep semantic nuclei segmentation models for multi-institutional WSI images (i.e., collected from different scanners) of different organs. Specifically, we study the performance of pertinent deep learning-based models with nuclei segmentation in WSI images of different stains and various organs. We also propose a feasible deep learning nuclei segmentation model formed by combining robust deep learning architectures. A comprehensive comparative study with existing software and related methods in terms of different evaluation metrics and the number of parameters of each model, emphasizes the efficacy of the proposed nuclei segmentation models. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:42:59Z No. of bitstreams: 1 ijimai_6_6_4.pdf: 1262221 bytes, checksum: 55709fdab7acf58482eb320e00e33189 (MD5); Made available in DSpace on 2022-04-28T07:42:59Z (GMT). No. of bitstreams: 1 ijimai_6_6_4.pdf: 1262221 bytes, checksum: 55709fdab7acf58482eb320e00e33189 (MD5)

NSL-BP: A Meta Classifier Model Based Prediction of Amazon Product Reviews

NSL-BP: A Meta Classifier Model Based Prediction of Amazon Product Reviews Kumar, Pravin; Dayal, Mohit; Khari, Manju; Fenza, Giuseppe; Gallo, Mariacristina In machine learning, the product rating prediction based on the semantic analysis of the consumers' reviews is a relevant topic. Amazon is one of the most popular online retailers, with millions of customers purchasing and reviewing products. In the literature, many research projects work on the rating prediction of a given review. In this research project, we introduce a novel approach to enhance the accuracy of rating prediction by machine learning methods by processing the reviewed text. We trained our model by using many methods, so we propose a combined model to predict the ratings of products corresponding to a given review content. First, using k-means and LDA, we cluster the products and topics so that it will be easy to predict the ratings having the same kind of products and reviews together. We trained low, neutral, and high models based on clusters and topics of products. Then, by adopting a stacking ensemble model, we combine Naïve Bayes, Logistic Regression, and SVM to predict the ratings. We will combine these models into a two-level stack. We called this newly introduced model, NSL model, and compared the prediction performance with other methods at state of the art. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:35:14Z No. of bitstreams: 1 ijimai_6_6_10.pdf: 1918604 bytes, checksum: f6fbe7f06eb20d287f632d22bb5f9daa (MD5); Made available in DSpace on 2022-04-28T07:35:14Z (GMT). No. of bitstreams: 1 ijimai_6_6_10.pdf: 1918604 bytes, checksum: f6fbe7f06eb20d287f632d22bb5f9daa (MD5)

A Word Embedding Based Approach for Focused Web Crawling Using the Recurrent Neural Network

A Word Embedding Based Approach for Focused Web Crawling Using the Recurrent Neural Network Dhanith, P. R. Joe; Surendiran, B.; Raja, S. P. Learning-based focused crawlers download relevant uniform resource locators (URLs) from the web for a specific topic. Several studies have used the term frequency-inverse document frequency (TF-IDF) weighted cosine vector as an input feature vector for learning algorithms. TF-IDF-based crawlers calculate the relevance of a web page only if a topic word co-occurs on the said page, failing which it is considered irrelevant. Similarity is not considered even if a synonym of a term co-occurs on a web page. To resolve this challenge, this paper proposes a new methodology that integrates the Adagrad-optimized Skip Gram Negative Sampling (A-SGNS)-based word embedding and the Recurrent Neural Network (RNN).The cosine similarity is calculated from the word embedding matrix to form a feature vector that is given as an input to the RNN to predict the relevance of the website. The performance of the proposed method is evaluated using the harvest rate (hr) and irrelevance ratio (ir). The proposed methodology outperforms existing methodologies with an average harvest rate of 0.42 and irrelevance ratio of 0.58. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:29:30Z No. of bitstreams: 1 ijimai_6_6_13.pdf: 1266941 bytes, checksum: 08f79b1ecc9b9c8f7c4a575d89faf9c2 (MD5); Made available in DSpace on 2022-04-28T07:29:30Z (GMT). No. of bitstreams: 1 ijimai_6_6_13.pdf: 1266941 bytes, checksum: 08f79b1ecc9b9c8f7c4a575d89faf9c2 (MD5)

A Hybrid Approach for Android Malware Detection and Family Classification

A Hybrid Approach for Android Malware Detection and Family Classification Dhalaria, Meghna; Gandotra, Ekta With the increase in the popularity of mobile devices, malicious applications targeting Android platform have greatly increased. Malware is coded so prudently that it has become very complicated to identify. The increase in the large amount of malware every day has made the manual approaches inadequate for detecting the malware. Nowadays, a new malware is characterized by sophisticated and complex obfuscation techniques. Thus, the static malware analysis alone is not enough for detecting it. However, dynamic malware analysis is appropriate to tackle evasion techniques but incapable to investigate all the execution paths and also it is very time consuming. So, for better detection and classification of Android malware, we propose a hybrid approach which integrates the features obtained after performing static and dynamic malware analysis. This approach tackles the problem of analyzing, detecting and classifying the Android malware in a more efficient manner. In this paper, we have used a robust set of features from static and dynamic malware analysis for creating two datasets i.e. binary and multiclass (family) classification datasets. These are made publically available on GitHub and Kaggle with the aim to help researchers and anti-malware tool creators for enhancing or developing new techniques and tools for detecting and classifying Android malware. Various machine learning algorithms are employed to detect and classify malware using the features extracted after performing static and dynamic malware analysis. The experimental outcomes indicate that hybrid approach enhances the accuracy of detection and classification of Android malware as compared to the case when static and dynamic features are considered alone. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:24:13Z No. of bitstreams: 1 ijimai_6_6_18.pdf: 1394422 bytes, checksum: f2b6587a369f4cb2b5cef17c4e46925c (MD5); Made available in DSpace on 2022-04-28T07:24:13Z (GMT). No. of bitstreams: 1 ijimai_6_6_18.pdf: 1394422 bytes, checksum: f2b6587a369f4cb2b5cef17c4e46925c (MD5)

Attention-based Multi-modal Sentiment Analysis and Emotion Detection in Conversation using RNN

Attention-based Multi-modal Sentiment Analysis and Emotion Detection in Conversation using RNN Huddar, Mahesh G.; Sannakki, Sanjeev S.; Rajpurohit, Vijay S. The availability of an enormous quantity of multimodal data and its widespread applications, automatic sentiment analysis and emotion classification in the conversation has become an interesting research topic among the research community. The interlocutor state, context state between the neighboring utterances and multimodal fusion play an important role in multimodal sentiment analysis and emotion detection in conversation. In this article, the recurrent neural network (RNN) based method is developed to capture the interlocutor state and contextual state between the utterances. The pair-wise attention mechanism is used to understand the relationship between the modalities and their importance before fusion. First, two-two combinations of modalities are fused at a time and finally, all the modalities are fused to form the trimodal representation feature vector. The experiments are conducted on three standard datasets such as IEMOCAP, CMU-MOSEI, and CMU-MOSI. The proposed model is evaluated using two metrics such as accuracy and F1-Score and the results demonstrate that the proposed model performs better than the standard baselines. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:20:14Z No. of bitstreams: 1 ijimai_6_6_12.pdf: 1521302 bytes, checksum: e5032e24c7880cae152f0b23b2530372 (MD5); Made available in DSpace on 2022-04-28T07:20:14Z (GMT). No. of bitstreams: 1 ijimai_6_6_12.pdf: 1521302 bytes, checksum: e5032e24c7880cae152f0b23b2530372 (MD5)

Satisfiability Logic Analysis Via Radial Basis Function Neural Network with Artificial Bee Colony Algorithm

Satisfiability Logic Analysis Via Radial Basis Function Neural Network with Artificial Bee Colony Algorithm Kasihmuddin, Mohd Shareduwan Bin Mohd; Mansor, Mohd Asyraf Bin; Abdulhabib Alzaeemi, Shehab; Sathasivam, Saratha Radial Basis Function Neural Network (RBFNN) is a variant of artificial neural network (ANN) paradigm, utilized in a plethora of fields of studies such as engineering, technology and science. 2 Satisfiability (2SAT) programming has been coined as a prominent logical rule that defines the identity of RBFNN. In this research, a swarm-based searching algorithm namely, the Artificial Bee Colony (ABC) will be introduced to facilitate the training of RBFNN. Worth mentioning that ABC is a new population-based metaheuristics algorithm inspired by the intelligent comportment of the honey bee hives. The optimization pattern in ABC was found fruitful in RBFNN since ABC reduces the complexity of the RBFNN in optimizing important parameters. The effectiveness of ABC in RBFNN has been examined in terms of various performance evaluations. Therefore, the simulation has proved that the ABC complied efficiently in tandem with the Radial Basis Neural Network with 2SAT according to various evaluations such as the Root Mean Square Error (RMSE), Sum of Squares Error (SSE), Mean Absolute Percentage Error (MAPE), and CPU Time. Overall, the experimental results have demonstrated the capability of ABC in enhancing the learning phase of RBFNN-2SAT as compared to the Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:10:04Z No. of bitstreams: 1 ijimai_6_6_17.pdf: 765951 bytes, checksum: 6a4766f4022778bd5a16faac29e622be (MD5); Made available in DSpace on 2022-04-28T07:10:04Z (GMT). No. of bitstreams: 1 ijimai_6_6_17.pdf: 765951 bytes, checksum: 6a4766f4022778bd5a16faac29e622be (MD5)

Editor's Note

Editor's Note Alonso-Betanzos, Amparo; Cabalar, Pedro; Dimuro, Gracaliz P.; García, Marcos; Hernández-Orallo, José; Hervás, Raquel; Manjarés, Ángeles; Martínez-Plumed, Fernado; Mora-Jiménez, Inmaculada; Sànchez-Marrè, Miquel Artificial Intelligence has become nowadays one of the main relevant technologies that is driven us to a new revolution, a change in society, just as well as other human inventions, such as navigation, steam machines, or electricity did in our past. There are several ways in which AI might be developed, and the European Union has chosen a path, a way to transit through this revolution, in which Artificial Intelligence will be a tool at the service of Humanity. That was precisely the motto of the 2020 European Conference on Artificial Intelligence (“Paving the way towards Human-Centric AI”), of which these special issue is a selection of the best papers selected by the organizers of some of the Workshops in ECAI 2020. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T09:38:07Z No. of bitstreams: 1 ijimai_6_5_0.pdf: 497691 bytes, checksum: ed2c7b345639776423b1760faae18f47 (MD5); Made available in DSpace on 2022-04-25T09:38:07Z (GMT). No. of bitstreams: 1 ijimai_6_5_0.pdf: 497691 bytes, checksum: ed2c7b345639776423b1760faae18f47 (MD5)

Towards Multi-perspective Conformance Checking with Fuzzy Sets

Towards Multi-perspective Conformance Checking with Fuzzy Sets Zhang, Sicui; Genga, Laura; Yan, Hui; Nie, Hongchao; Lu, Xudong; Kaymak, Uzay Nowadays organizations often need to employ data-driven techniques to audit their business processes and ensure they comply with laws and internal/external regulations. Failing in complying with the expected process behavior can indeed pave the way to inefficiencies or, worse, to frauds or abuses. An increasingly popular approach to automatically assess the compliance of the executions of organization processes is represented by alignment-based conformance checking. These techniques are able to compare real process executions with models representing the expected behaviors, providing diagnostics able to pinpoint possible discrepancies. However, the diagnostics generated by state of the art techniques still suffer from some limitations. They perform a crisp evaluation of process compliance, marking process behavior either as compliant or deviant, without taking into account the severity of the identified deviation. This hampers the accuracy of the obtained diagnostics and can lead to misleading results, especially in contexts where there is some tolerance with respect to violations of the process guidelines. In the present work, we discuss the impact and the drawbacks of a crisp deviation assessment approach. Then, we propose a novel conformance checking approach aimed at representing actors’ tolerance with respect to process deviations, taking it into account when assessing the severity of the deviations. As a proof of concept, we performed a set of synthetic experiments to assess the approach. The obtained results point out the potential of the usage of a more flexible evaluation of process deviations, and its impact on the quality and the interpretation of the obtained diagnostics. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T09:29:59Z No. of bitstreams: 1 ijimai_6_5_13.pdf: 1071887 bytes, checksum: 32b33cd6018b9ff2afe672e989a3c615 (MD5); Made available in DSpace on 2022-04-25T09:29:59Z (GMT). No. of bitstreams: 1 ijimai_6_5_13.pdf: 1071887 bytes, checksum: 32b33cd6018b9ff2afe672e989a3c615 (MD5)

Antimicrobial Resistance Prediction in Intensive Care Unit for Pseudomonas Aeruginosa using Temporal Data-Driven Models

Antimicrobial Resistance Prediction in Intensive Care Unit for Pseudomonas Aeruginosa using Temporal Data-Driven Models Hernàndez-Carnerero, Àlvar; Sànchez-Marrè, Miquel; Mora-Jiménez, Inmaculada; Soguero-Ruiz, Cristina; Martínez-Agüero, Sergio; Álvarez-Rodríguez, Joaquín One threatening medical problem for human beings is the increasing antimicrobial resistance of some microorganisms. This problem is especially difficult in Intensive Care Units (ICUs) of hospitals due to the vulnerable state of patients. Knowing in advance whether a concrete bacterium is resistant or susceptible to an antibiotic is a crux step for clinicians to determine an effective antibiotic treatment. This usual clinical procedure takes approximately 48 hours and it is named antibiogram. It tests the bacterium resistance to one or more antimicrobial families (six of them considered in this work). This article focuses on cultures of the Pseudomonas Aeruginosa bacterium because is one of the most dangerous in the ICU. Several temporal data-driven models are proposed and analyzed to predict the resistance or susceptibility to a determined antibiotic family previously to know the antibiogram result and only using the available past information from a data set. This data set is formed by anonymized electronic health records data from more than 3300 ICU patients during 15 years. Several data-driven classifier methods are used in combination with several temporal modeling approaches. The results show that our predictions are reasonably accurate for some antimicrobial families, and could be used by clinicians to determine the best antibiotic therapy in advance. This early prediction can save valuable time to start the adequate treatment for an ICU patient. This study corroborates the results of a previous work pointing that the antimicrobial resistance of bacteria in the ICU is related to other recent resistance tests of ICU patients. This information is very valuable for making accurate antimicrobial resistance predictions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T09:24:37Z No. of bitstreams: 1 ijimai_6_5_12_0.pdf: 1767884 bytes, checksum: 2e4cbefa7dbbdd510a6f3472de7e0bc5 (MD5); Made available in DSpace on 2022-04-25T09:24:37Z (GMT). No. of bitstreams: 1 ijimai_6_5_12_0.pdf: 1767884 bytes, checksum: 2e4cbefa7dbbdd510a6f3472de7e0bc5 (MD5)

Artificial Canaries: Early Warning Signs for Anticipatory and Democratic Governance of AI

Artificial Canaries: Early Warning Signs for Anticipatory and Democratic Governance of AI Zoe Cremer, Carla; Whittlestone, Jess We propose a method for identifying early warning signs of transformative progress in artificial intelligence (AI), and discuss how these can support the anticipatory and democratic governance of AI. We call these early warning signs ‘canaries’, based on the use of canaries to provide early warnings of unsafe air pollution in coal mines. Our method combines expert elicitation and collaborative causal graphs to identify key milestones and identify the relationships between them. We present two illustrations of how this method could be used: to identify early warnings of harmful impacts of language models; and of progress towards high-level machine intelligence. Identifying early warning signs of transformative applications can support more efficient monitoring and timely regulation of progress in AI: as AI advances, its impacts on society may be too great to be governed retrospectively. It is essential that those impacted by AI have a say in how it is governed. Early warnings can give the public time and focus to influence emerging technologies using democratic, participatory technology assessments. We discuss the challenges in identifying early warning signals and propose directions for future work. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T09:18:24Z No. of bitstreams: 1 ijimai_6_5_8.pdf: 811659 bytes, checksum: effdef00ca187bdcd3b848a656965925 (MD5); Made available in DSpace on 2022-04-25T09:18:24Z (GMT). No. of bitstreams: 1 ijimai_6_5_8.pdf: 811659 bytes, checksum: effdef00ca187bdcd3b848a656965925 (MD5)

Improving Asynchronous Interview Interaction with Follow-up Question Generation

Improving Asynchronous Interview Interaction with Follow-up Question Generation Rao S B, Pooja; Agnihotri, Manish; Babu Jayagopi, Dinesh The user experience of an asynchronous video interview system, conventionally is not reciprocal or conversational. Interview applicants expect that, like a typical face-to-face interview, they are innate and coherent. We posit that the planned adoption of limited probing through follow-up questions is an important step towards improving the interaction. We propose a follow-up question generation model (followQG) capable of generating relevant and diverse follow-up questions based on the previously asked questions, and their answers. We implement a 3D virtual interviewing system, Maya, with capability of follow-up question generation. Existing asynchronous interviewing systems are not dynamic with scripted and repetitive questions. In comparison, Maya responds with relevant follow-up questions, a largely unexplored feature of irtual interview systems. We take advantage of the implicit knowledge from deep pre-trained language models to generate rich and varied natural language follow-up questions. Empirical results suggest that followQG generates questions that humans rate as high quality, achieving 77% relevance. A comparison with strong baselines of neural network and rule-based systems show that it produces better quality questions. The corpus used for fine-tuning is made publicly available. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T09:02:55Z No. of bitstreams: 1 ijimai_6_5_8.pdf: 811659 bytes, checksum: effdef00ca187bdcd3b848a656965925 (MD5); Made available in DSpace on 2022-04-25T09:02:55Z (GMT). No. of bitstreams: 1 ijimai_6_5_8.pdf: 811659 bytes, checksum: effdef00ca187bdcd3b848a656965925 (MD5)

Efficient and Robust Model Benchmarks with Item Response Theory and Adaptive Testing

Efficient and Robust Model Benchmarks with Item Response Theory and Adaptive Testing Song, Hao; Flach, Peter Progress in predictive machine learning is typically measured on the basis of performance comparisons on benchmark datasets. Traditionally these kinds of empirical evaluation are carried out on large numbers of datasets, but this is becoming increasingly hard due to computational requirements and the often large number of alternative methods to compare against. In this paper we investigate adaptive approaches to achieve better efficiency on model benchmarking. For a large collection of datasets, rather than training and testing a given approach on every individual dataset, we seek methods that allow us to pick only a few representative datasets to quantify the model’s goodness, from which to extrapolate to performance on other datasets. To this end, we adapt existing approaches from psychometrics: specifically, Item Response Theory and Adaptive Testing. Both are well-founded frameworks designed for educational tests. We propose certain modifications following the requirements of machine learning experiments, and present experimental results to validate the approach. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T08:26:43Z No. of bitstreams: 1 ijimai_6_5_11_0.pdf: 1553782 bytes, checksum: 3902c228651abcbb54eedf8f00911863 (MD5); Made available in DSpace on 2022-04-25T08:26:43Z (GMT). No. of bitstreams: 1 ijimai_6_5_11_0.pdf: 1553782 bytes, checksum: 3902c228651abcbb54eedf8f00911863 (MD5)

Attesting Digital Discrimination Using Norms

Attesting Digital Discrimination Using Norms Criado, Natalia; Ferrer, Xavier; Such, José M. More and more decisions are delegated to Machine Learning (ML) and automatic decision systems recently. Despite initial misconceptions considering these systems unbiased and fair, recent cases such as racist algorithms being used to inform parole decisions in the US, low-income neighborhood's targeted with high-interest loans and low credit scores, and women being undervalued by online marketing, fueled public distrust in machine learning. This poses a significant challenge to the adoption of ML by companies or public sector organisations, despite ML having the potential to lead to significant reductions in cost and more efficient decisions, and is motivating research in the area of algorithmic fairness and fair ML. Much of that research is aimed at providing detailed statistics, metrics and algorithms which are difficult to interpret and use by someone without technical skills. This paper tries to bridge the gap between lay users and fairness metrics by using simpler notions and concepts to represent and reason about digital discrimination. In particular, we use norms as an abstraction to communicate situations that may lead to algorithms committing discrimination. In particular, we formalise non-discrimination norms in the context of ML systems and propose an algorithm to attest whether ML systems violate these norms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T08:22:14Z No. of bitstreams: 1 ijimai_6_5_2_0.pdf: 1547136 bytes, checksum: ae3fea797aa35805462bbd0ce54b2d59 (MD5); Made available in DSpace on 2022-04-25T08:22:14Z (GMT). No. of bitstreams: 1 ijimai_6_5_2_0.pdf: 1547136 bytes, checksum: ae3fea797aa35805462bbd0ce54b2d59 (MD5)

Achieving Fair Inference Using Error-Prone Outcomes

Achieving Fair Inference Using Error-Prone Outcomes Boeschoten, Laura; van Kesteren, Erik-Jan; Bagheri, Ayoub; Oberski, Daniel L. Recently, an increasing amount of research has focused on methods to assess and account for fairness criteria when predicting ground truth targets in supervised learning. However, recent literature has shown that prediction unfairness can potentially arise due to measurement error when target labels are error prone. In this study we demonstrate that existing methods to assess and calibrate fairness criteria do not extend to the true target variable of interest, when an error-prone proxy target is used. As a solution to this problem, we suggest a framework that combines two existing fields of research: fair ML methods, such as those found in the counterfactual fairness literature and measurement models found in the statistical literature. Firstly, we discuss these approaches and how they can be combined to form our framework. We also show that, in a healthcare decision problem, a latent variable model to account for measurement error removes the unfairness detected previously. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T09:51:33Z No. of bitstreams: 1 ijimai_6_5_1_0.pdf: 1416724 bytes, checksum: 5967c9d0ef87c8fe583c0a5ce961bd2d (MD5); Made available in DSpace on 2022-04-21T09:51:33Z (GMT). No. of bitstreams: 1 ijimai_6_5_1_0.pdf: 1416724 bytes, checksum: 5967c9d0ef87c8fe583c0a5ce961bd2d (MD5)

Assessing Lexical-Semantic Regularities in Portuguese Word Embeddings

Assessing Lexical-Semantic Regularities in Portuguese Word Embeddings Gonçalo Oliveira, Hugo; Sousa, Tiago; Alves, Ana Models of word embeddings are often assessed when solving syntactic and semantic analogies. Among the latter, we are interested in relations that one would find in lexical-semantic knowledge bases like WordNet, also covered by some analogy test sets for English. Briefly, this paper aims to study how well pretrained Portuguese word embeddings capture such relations. For this purpose, we created a new test, dubbed TALES, with an exclusive focus on Portuguese lexical-semantic relations, acquired from lexical resources. With TALES, we analyse the performance of methods previously used for solving analogies, on different models of Portuguese word embeddings. Accuracies were clearly below the state of the art in analogies of other kinds, which shows that TALES is a challenging test, mainly due to the nature of lexical-semantic relations, i.e., there are many instances sharing the same argument, thus allowing for several correct answers, sometimes too many to be all included in the dataset. We further inspect the results of the best performing combination of method and model to find that some acceptable answers had been considered incorrect. This was mainly due to the lack of coverage by the source lexical resources and suggests that word embeddings may be a useful source of information for enriching those resources, something we also discuss. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T09:40:33Z No. of bitstreams: 1 ijimai_6_5_4.pdf: 740435 bytes, checksum: 4dc5d11063ba9a0dead09ca7c9ec2f31 (MD5); Made available in DSpace on 2022-04-21T09:40:33Z (GMT). No. of bitstreams: 1 ijimai_6_5_4.pdf: 740435 bytes, checksum: 4dc5d11063ba9a0dead09ca7c9ec2f31 (MD5)

No App is an Island: Collective Action and Sustainable Development Goal-Sensitive Design

No App is an Island: Collective Action and Sustainable Development Goal-Sensitive Design Pitt, Steph; van Meelis Lacey, Marlína; Scaife, Ed; Pitt, Jeremy The transformation to the Digital Society presents a challenge to engineer ever more complex socio-technical systems in order to address wicked societal problems. Therefore, it is essential that these systems should be engineered with respect not just to conventional functional and non-functional requirements, but also with respect to satisfying qualitative human values, and assessing their impact on global challenges, such as those expressed by the UN sustainable development goals (SDGs). In this paper, we present a set of sets of design principles and an associated meta-platform, which focus design of socio-technical systems on the potential interaction of human and artificial intelligence with respect to three aspects: firstly, decision-support with respect to the codification of deep social knowledge; secondly, visualisation of community contribution to successful collective action; and thirdly, systemic improvement with respect to the SDGs through impact assessment and measurement. This methodology, of SDG-Sensitive Design, is illustrated through the design of two collective action apps, one for encouraging plastic re-use and reducing plastic waste, and the other for addressing redistribution of surplus food. However, as with the inter-connectedness of the SDGs, we conclude by arguing that the inter-connectedness of the Digital Society implies that system development cannot be undertaken in isolation from other systems. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T09:07:05Z No. of bitstreams: 1 ijimai_6_5_3.pdf: 1257169 bytes, checksum: 49b23252669790ab29a032be89e83f70 (MD5); Made available in DSpace on 2022-04-21T09:07:05Z (GMT). No. of bitstreams: 1 ijimai_6_5_3.pdf: 1257169 bytes, checksum: 49b23252669790ab29a032be89e83f70 (MD5)

Neural Scoring of Logical Inferences from Data using Feedback

Neural Scoring of Logical Inferences from Data using Feedback Susaiyah, Allmin; Härmä, Aki; Reiter, Ehud; Petković, Milan Insights derived from wearable sensors in smartwatches or sleep trackers can help users in approaching their healthy lifestyle goals. These insights should indicate significant inferences from user behaviour and their generation should adapt automatically to the preferences and goals of the user. In this paper, we propose a neural network model that generates personalised lifestyle insights based on a model of their significance, and feedback from the user. Simulated analysis of our model shows its ability to assign high scores to a) insights with statistically significant behaviour patterns and b) topics related to simple or complex user preferences at any given time. We believe that the proposed neural networks model could be adapted for any application that needs user feedback to score logical inferences from data. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T08:51:32Z No. of bitstreams: 1 ijimai_6_5_9.pdf: 1542068 bytes, checksum: 0b7f144dee74038863f4319af98fcb00 (MD5); Made available in DSpace on 2022-04-21T08:51:32Z (GMT). No. of bitstreams: 1 ijimai_6_5_9.pdf: 1542068 bytes, checksum: 0b7f144dee74038863f4319af98fcb00 (MD5)

Smoke Test Planning using Answer Set Programming

Smoke Test Planning using Answer Set Programming Philipp, Tobias; Roland, Valentin; Schweizer, Lukas Smoke testing is an important method to increase stability and reliability of hardware- gramming, Testing depending systems. Due to concurrent access to the same physical resource and the impracticality of the use of virtualization, smoke testing requires some form of planning. In this paper, we propose to decompose test cases in terms of atomic actions consisting of preconditions and effects. We present a solution based on answer set programming with multi-shot solving that automatically generates short parallel test plans. Experiments suggest that the approach is feasible for non-inherently sequential test cases and scales up to thousands of test cases. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T08:44:55Z No. of bitstreams: 1 ijimai_6_5_6.pdf: 1421291 bytes, checksum: dd56f403e28117df1c8d95bf34057b55 (MD5); Made available in DSpace on 2022-04-21T08:44:55Z (GMT). No. of bitstreams: 1 ijimai_6_5_6.pdf: 1421291 bytes, checksum: dd56f403e28117df1c8d95bf34057b55 (MD5)

An Application of Declarative Languages in Distributed Architectures: ASP and DALI Microservices

An Application of Declarative Languages in Distributed Architectures: ASP and DALI Microservices Costantini, Stefania; De Gasperis, Giovanni; De Lauretis, Lorenzo In this paper we introduce an approach to the possible adoption of Answer Set Programming (ASP) for the definition of microservices, which are a successful abstraction for designing distributed applications as suites of independently deployable interacting components. Such ASP-based components might be employed in distributed architectures related to Cloud Computing or to the Internet of Things (IoT), where the ASP microservices might be usefully coordinated with intelligent logic-based agents. We develop a case study where we consider ASP microservices in synergy with agents defined in DALI, a well-known logic-based agent-oriented programming language developed by our research group. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T08:25:49Z No. of bitstreams: 1 ijimai_6_5_7.pdf: 798062 bytes, checksum: abbe89b464aadcbe68089ed6649c2a0b (MD5); Made available in DSpace on 2022-04-21T08:25:49Z (GMT). No. of bitstreams: 1 ijimai_6_5_7.pdf: 798062 bytes, checksum: abbe89b464aadcbe68089ed6649c2a0b (MD5)

The Semantics of History. Interdisciplinary Categories and Methods for Digital Historical Research

The Semantics of History. Interdisciplinary Categories and Methods for Digital Historical Research Travé Allepuz, Esther; del Fresno Bernal, Pablo; Mauri Martí, Alfred; Medina Gordo, Sonia This paper aims at introducing and discussing the data modelling and labelling methods for interdisciplinary and digital research in History developed and used by the authors. Our approach suggests the development of a conceptual framework for interdisciplinary research in history as a much-needed strategy to ensure that historians use all vestiges from the past regardless of their origin or support for the construction of historical discourse. By labelling Units of Topography and Actors in a wide range of historical sources and exploiting the obtained data, we use the Monastery of Sant Genís de Rocafort (Martorell, Spain) as a lab example of our method. This should lead researchers to the development of an integrated historical discourse maximizing the potential of interdisciplinary and fair research and minimizing the risks of bias. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T08:11:57Z No. of bitstreams: 1 ijimai_6_5_5_0.pdf: 2719671 bytes, checksum: 6892ed37b8ab2c895160236b59719993 (MD5); Made available in DSpace on 2022-04-21T08:11:57Z (GMT). No. of bitstreams: 1 ijimai_6_5_5_0.pdf: 2719671 bytes, checksum: 6892ed37b8ab2c895160236b59719993 (MD5)

Editor’s Note

Editor’s Note García, Vicente; Wu, Shaofei The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T09:56:59Z No. of bitstreams: 1 ijimai_6_4_0.pdf: 490509 bytes, checksum: 85e87cc3d0eb1adf225a8da55469b83c (MD5); Made available in DSpace on 2022-04-08T09:56:59Z (GMT). No. of bitstreams: 1 ijimai_6_4_0.pdf: 490509 bytes, checksum: 85e87cc3d0eb1adf225a8da55469b83c (MD5)

Deraining and Desnowing Algorithm on Adaptive Tolerance and Dual-tree Complex Wavelet Fusion

Deraining and Desnowing Algorithm on Adaptive Tolerance and Dual-tree Complex Wavelet Fusion Zang, Jingfeng; Xu, Ningxue; Liu, Riu; Shi, Yuhuan Severe weather conditions such as rain and snow often reduce the visual perception quality of the video image system, the traditional methods of deraining and desnowing usually rarely consider adaptive parameters. In order to enhance the effect of video deraining and desnowing, this paper proposes a video deraining and desnowing algorithm based on adaptive tolerance and dual-tree complex wavelet. This algorithm can be widely used in security surveillance, military defense, biological monitoring, remote sensing and other fields. First, this paper introduces the main work of the adaptive tolerance method for the video of dynamic scenes. Second, the algorithm of dual-tree complex wavelet fusion is analyzed and introduced. Using principal component analysis fusion rules to process low-frequency sub-bands, the fusion rule of local energy matching is used to process the high-frequency sub-bands. Finally, this paper used various rain and snow videos to verify the validity and superiority of image reconstruction. Experimental results show that the algorithm has achieved good results in improving the image clarity and restoring the image details obscured by raindrops and snows. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T09:52:20Z No. of bitstreams: 1 ijimai_6_4_14.pdf: 1449944 bytes, checksum: 78c7983b43bb2495d53e4e396d24648e (MD5); Made available in DSpace on 2022-04-08T09:52:20Z (GMT). No. of bitstreams: 1 ijimai_6_4_14.pdf: 1449944 bytes, checksum: 78c7983b43bb2495d53e4e396d24648e (MD5)

The Construction Site Management of Concrete Prefabricated Buildings by ISM-ANP Network Structure Model and BIM under Big Data Text Mining Analytic Network Process (ANP)

The Construction Site Management of Concrete Prefabricated Buildings by ISM-ANP Network Structure Model and BIM under Big Data Text Mining Analytic Network Process (ANP) Xu, Guiming In the construction industry, prefabricated buildings have developed rapidly in recent years due to their various excellent properties. To expand the application of big data text mining and Building Information Model (BIM) in prefabricated building construction, with concrete as a form of expression, the construction management of concrete prefabricated buildings is discussed. Based on the Interpretative Structural Model (ISM) and Analytic Network Process (ANP), the importance of the safety factors on the construction sites of concrete prefabricated buildings are assessed. Based on BIM, an optimized construction management platform for concrete prefabricated buildings is built, whose realization effects are characterized. The results show that prefabricated buildings have developed rapidly from 2017 to 2019. Compared with traditional buildings, they can significantly reduce the waste of resources and energy, and the savings of water resource utilization can reach 80%. Among the various safety impact elements, construction management has the greatest impact on construction safety, and the corresponding weight value is 0.3653. The corresponding weight of construction personnel is 0.2835, the corresponding weight of construction objects is 0.1629, the corresponding weight of construction technology is 0.1436, and the corresponding weight of construction environment is 0.0448. This building construction management platform is able to control the construction progress in real-time and avoid the occurrence of construction safety accidents. The final layout of the construction site shows a good effect, and the deviation between the actual construction schedule and the expected construction schedule is small, which is of great significance for the smooth development of concrete prefabricated buildings. This is a catalyst for the future development of concrete prefabricated buildings and the application of big data technology. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T09:46:27Z No. of bitstreams: 1 ijimai_6_4_15.pdf: 979715 bytes, checksum: 14f5a152ff648b1f2ce6d7e6728b1d72 (MD5); Made available in DSpace on 2022-04-08T09:46:27Z (GMT). No. of bitstreams: 1 ijimai_6_4_15.pdf: 979715 bytes, checksum: 14f5a152ff648b1f2ce6d7e6728b1d72 (MD5)

Function Analysis of Industrial Robot under Cubic Polynomial Interpolation in Animation Simulation Environment

Function Analysis of Industrial Robot under Cubic Polynomial Interpolation in Animation Simulation Environment Li, You; Wang, Juan; Ji, Yiming In order to study the effect of cubic polynomial interpolation in the trajectory planning of polishing robot manipulator, firstly, the articular robot operating arm is taken as the research object, and the overall system of polishing robot operating arm with 7 degrees of freedom is constructed. Then through the transformation of space motion and pose coordinate system, Denavit-Hartenberg (D-H) Matrix is introduced to describe the coordinate direction and parameters of the adjacent connecting rod of the polishing robot, and the kinematic model of the robot is built, and the coordinate direction and parameters of its adjacent link are described. A multi-body Dynamic simulation software, Automatic Dynamic Analysis of Mechanical Systems (ADAMS), is used to analyze the kinematic simulation of the robot operating arm system. Finally, the trajectory of the robot manipulator is planned based on the cubic polynomial difference method, and the simulation is verified by Matrix Laboratory (MATLAB). Through calculation, it is found that the kinematic model of polishing robot operating arm constructed in this study is in line with the reality; ADAMS software is used to generate curves of the rotation angles of different joint axes and the displacement of end parts of the polishing robot operating arm changing with time. After obtaining relevant parameters, they are put into the kinematic equation constructed in this study, and the calculated position coordinates are consistent with the detection results; moreover, the polishing robot constructed in this study can realize the functions of deburring, polishing, trimming, and turning table. MATLAB software is used to generate the simulation of the movement trajectory of the polishing robot operating arm, which can show the change curve of angle and angular velocity. The difference between the angle at which the polishing robot reaches the polishing position, the change curve of angular velocity, and the time spent before and after the path optimization is compared. It is found that after path optimization based on cubic polynomial, the change curve of the polishing robot's angle and angular velocity is smoother, and the time is shortened by 17.21s. It indicates that the cubic polynomial interpolation method can realize the trajectory planning of the polishing robot operating arm, moreover, the optimized polishing robot has a continuous and smooth trajectory, which can improve the working efficiency of the robot. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T09:23:42Z No. of bitstreams: 1 ijimai_6_4_11.pdf: 1076311 bytes, checksum: 37abc286f9822efb9dcabe5238f06721 (MD5); Made available in DSpace on 2022-04-08T09:23:42Z (GMT). No. of bitstreams: 1 ijimai_6_4_11.pdf: 1076311 bytes, checksum: 37abc286f9822efb9dcabe5238f06721 (MD5)

Solving an Optimal Control Problem of Cancer Treatment by Artificial Neural Networks

Solving an Optimal Control Problem of Cancer Treatment by Artificial Neural Networks Heydarpour, F.; Abbasi, E.; Ebadi, M. J.; Karbassi, S. M. Cancer is an uncontrollable growth of abnormal cells in any tissue of the body. Many researchers have focused on machine learning and artificial intelligence (AI) based on approaches for cancer treatment. Dissimilar to traditional methods, these approaches are efficient and are able to find the optimal solutions of cancer chemotherapy problems. In this paper, a system of ordinary differential equations (ODEs) with the state variables of immune cells, tumor cells, healthy cells and drug concentration is proposed to anticipate the tumor growth and to show their interactions in the body. Then, an artificial neural network (ANN) is applied to solve the ODEs system through minimizing the error function and modifying the parameters consisting of weights and biases. The mean square errors (MSEs) between the analytical and ANN results corresponding to four state variables are 1.54e-06, 6.43e-07, 6.61e-06, and 3.99e-07, respectively. These results show the good performance and efficiency of the proposed method. Moreover, the optimal dose of chemotherapy drug and the amount of drug needed to continue the treatment process are achieved. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T09:17:48Z No. of bitstreams: 1 ijimai_6_4_2.pdf: 1191973 bytes, checksum: 343ea97ecbb1518af534e6b5dccfdb70 (MD5); Made available in DSpace on 2022-04-08T09:17:48Z (GMT). No. of bitstreams: 1 ijimai_6_4_2.pdf: 1191973 bytes, checksum: 343ea97ecbb1518af534e6b5dccfdb70 (MD5)

The Possibilities of Classification of Emotional States Based on User Behavioral Characteristics

The Possibilities of Classification of Emotional States Based on User Behavioral Characteristics Magdin, Martin; Držík, D.; Reichel, J.; Koprda, S . The classification of user's emotions based on their behavioral characteristic, namely their keyboard typing and mouse usage pattern is an effective and non-invasive way of gathering user's data without imposing any limitations on their ability to perform tasks. To gather data for the classifier we used an application, the Emotnizer, which we had developed for this purpose. The output of the classification is categorized into 4 emotional categories from Russel's complex circular model - happiness, anger, sadness and the state of relaxation. The sample of the reference database consisted of 50 students. Multiple regression analyses gave us a model, that allowed us to predict the valence and arousal of the subject based on the input from the keyboard and mouse. Upon re-testing with another test group of 50 students and processing the data we found out our Emotnizer program can classify emotional states with an average success rate of 82.31%. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T08:58:38Z No. of bitstreams: 1 ijimai_6_4_10.pdf: 1062017 bytes, checksum: b37a6b2c6c7d6758145e11aa052a7150 (MD5); Made available in DSpace on 2022-04-08T08:58:38Z (GMT). No. of bitstreams: 1 ijimai_6_4_10.pdf: 1062017 bytes, checksum: b37a6b2c6c7d6758145e11aa052a7150 (MD5)

Assessed by Machines: Development of a TAM-Based Tool to Measure AI-based Assessment Acceptance Among Students

Assessed by Machines: Development of a TAM-Based Tool to Measure AI-based Assessment Acceptance Among Students Sánchez-Prieto, José Carlos; Cruz-Benito, Juan; Therón, Roberto; García-Peñalvo, Francisco In recent years, the use of more and more technology in education has been a trend. The shift of traditional learning procedures into more online and tech-ish approaches has contributed to a context that can favor integrating Artificial-Intelligence-based or algorithm-based assessment of learning. Even more, with the current acceleration because of the COVID-19 pandemic, more and more learning processes are becoming online and are incorporating technologies related to automatize assessment or help instructors in the process. While we are in an initial stage of that integration, it is the moment to reflect on the students' perceptions of being assessed by a non-conscious software entity like a machine learning model or any other artificial intelligence application. As a result of the paper, we present a TAM-based model and a ready-to-use instrument based on five aspects concerning understanding technology adoption like the AI-based assessment on education. These aspects are perceived usefulness, perceived ease of use, attitude towards use, behavioral intention, and actual use. The paper's outcomes can be relevant to the research community since there is a lack of this kind of proposal in the literature. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T08:40:35Z No. of bitstreams: 1 ijimai_6_4_8.pdf: 674624 bytes, checksum: 4af5d563c8d56c0f37fb2189ae2ccb1d (MD5); Made available in DSpace on 2022-04-08T08:40:36Z (GMT). No. of bitstreams: 1 ijimai_6_4_8.pdf: 674624 bytes, checksum: 4af5d563c8d56c0f37fb2189ae2ccb1d (MD5)

Data Science Techniques for COVID-19 in Intensive Care Units

Data Science Techniques for COVID-19 in Intensive Care Units Muñoz Lezcano, Sergio; López Hernández, Fernando Carlos; Corbi, Alberto Data scientists aim to provide techniques and tools to the clinicians to manage the new coronavirus disease. Nowadays, new emerging tools based on Artificial Intelligence (AI), Image Processing (IP) and Machine Learning (ML) are contributing to the improvement of healthcare and treatments of different diseases. This paper reviews the most recent research efforts and approaches related to these new data driven techniques and tools in combination with the exploitation of the already available COVID-19 datasets. The tools can assist clinicians and nurses in efficient decision making with complex and heavily heterogeneous data, even in hectic and overburdened Intensive Care Units (ICU) scenarios. The datasets and techniques underlying these tools can help finding a more correct diagnosis. The paper also describes how these innovative AI+IP+ML-based methods (e.g., conventional X-ray imaging, clinical laboratory data, respiratory monitoring and automatic adjustments, etc.) can assist in the process of easing both the care of infected patients in ICUs and Emergency Rooms and the discovery of new treatments (drugs). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T08:26:44Z No. of bitstreams: 1 ijimai_6_4_1_0.pdf: 1529347 bytes, checksum: 4fa2a340718d14f8bdf39735c1674fc7 (MD5); Made available in DSpace on 2022-04-08T08:26:44Z (GMT). No. of bitstreams: 1 ijimai_6_4_1_0.pdf: 1529347 bytes, checksum: 4fa2a340718d14f8bdf39735c1674fc7 (MD5)

Deep Learning-based Side Channel Attack on HMAC SM3

Deep Learning-based Side Channel Attack on HMAC SM3 Jin, Xin; Xiao, Yong; Li, Shiqi; Wang, Suying SM3 is a Chinese hash standard. HMAC SM3 uses a secret key to encrypt the input text and gives an output as the HMAC of the input text. If the key is recovered, adversaries can easily forge a valid HMAC. We can choose different methods, such as traditional side channel analysis, template attack-based side channel analysis to recover the secret key. Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis. In this paper, we try to recover the secret key with deep learning-based side channel analysis. We should train the network recursively for different parameters by using the same dataset and attack the target dataset with the trained network to recover different parameters. The experiment results show that the secret key can be recovered with deep learning-based side channel analysis. This work demonstrates the interests of this new method and show that this attack can be performed in practice. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-08T08:18:18Z No. of bitstreams: 1 ijimai_6_4_12.pdf: 1220803 bytes, checksum: be1a251509cad3220ce9b37f8440a5e1 (MD5); Made available in DSpace on 2022-04-08T08:18:18Z (GMT). No. of bitstreams: 1 ijimai_6_4_12.pdf: 1220803 bytes, checksum: be1a251509cad3220ce9b37f8440a5e1 (MD5)

A Feature Extraction Method Based on Feature Fusion and its Application in the Text-Driven Failure Diagnosis Field

A Feature Extraction Method Based on Feature Fusion and its Application in the Text-Driven Failure Diagnosis Field Zhou, Shenghan; Chen, Bang; Zhang, Yue; Liu, HouXiang; Xiao, Yiyong; Pan, Xing As a basic task in NLP (Natural Language Processing), feature extraction directly determines the quality of text clustering and text classification. However, the commonly used TF-IDF (Term Frequency & Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) text feature extraction methods have shortcomings in not considering the text’s context and blindness to the topic of the corpus. This study builds a feature extraction algorithm and application scenarios in the field of failure diagnosis. A text-driven failure diagnosis model is designed to classify and automatically judge which failure mode the failure described in the text belongs to once a failure-description text is entered. To verify the effectiveness of the proposed feature extraction algorithm and failure diagnosis model, a long-term accumulated failure description text of an aircraft maintenance and support system was used as a subject to conduct an empirical study. The final experimental results also show that the proposed feature extraction method can effectively improve the effect of clustering, and the proposed failure diagnosis model achieves high accuracies and low false alarm rates. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T10:29:29Z No. of bitstreams: 1 ijimai_6_4_13.pdf: 1302128 bytes, checksum: d2a3fbe28ce1fbeef4eeca8c4caacbc6 (MD5); Made available in DSpace on 2022-04-05T10:29:29Z (GMT). No. of bitstreams: 1 ijimai_6_4_13.pdf: 1302128 bytes, checksum: d2a3fbe28ce1fbeef4eeca8c4caacbc6 (MD5)

Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model

Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model García Nieto, Paulino José; García-Gonzalo, Esperanza; Sánchez Lasheras, Fernando; Bernardo Sánchez, Antonio The purpose of chromium plating is the creation of a hard and wear-resistant layer of chromium over a metallic surface. The principal feature of chromium plating is its endurance in the face of the wear and corrosion. This industrial process has a vast range of applications in many different areas. In the performance of this process, some difficulties can be found. Some of the most common are melt deposition, milky white chromium deposition, rough or sandy chromium deposition and lack of toughness of the layer or wear and lack of thickness of the layer deposited. This study builds a novel nonparametric method relied on the statistical machine learning that employs a hybrid support vector machines (SVMs) model for the hard chromium layer thickness forecast. The SVM hyperparameters optimization was made with the help of the Particle Swarm Optimizer (PSO). The outcomes indicate that PSO/SVM–based model together with radial basis function (RBF) kernel has permitted to foretell the thickness of the chromium layer created in this industrial process satisfactorily. Thus, two kinds of outcomes have been obtained: firstly, this model permits to determine the ranking of relevance of the seven independent input variables investigated in this industrial process. Finally, the high achievement and lack of complexity of the model indicate that the PSO/SVM method is very interesting compared to other conventional foretelling techniques, since a coefficient of determination of 0.9952 is acquired. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T10:04:50Z No. of bitstreams: 1 ijimai_6_4_4.pdf: 1114067 bytes, checksum: d4b8380c297df951f27b5f54089ec2f8 (MD5); Made available in DSpace on 2022-04-05T10:04:50Z (GMT). No. of bitstreams: 1 ijimai_6_4_4.pdf: 1114067 bytes, checksum: d4b8380c297df951f27b5f54089ec2f8 (MD5)

Multi Layered Multi Task Marker Based Interaction in Information Rich Virtual Environments

Multi Layered Multi Task Marker Based Interaction in Information Rich Virtual Environments Rehman, I; Ullah, S; Khan, Dawar Simple and cheap interaction has a key role in the operation and exploration of any Virtual Environment (VE). In this paper, we propose an interaction technique that provides two different ways of interaction (information and control) on complex objects in a simple and computationally cheap way. The interaction is based on the use of multiple embedded markers in a specialized manner. The proposed marker like an interaction peripheral works just like a touch paid which can perform any type of interaction in a 3D VE. The proposed marker is not only used for interaction with Augmented Reality (AR), but also with Mixed Reality. A biological virtual learning application is developed which is used for evaluation and experimentation. We conducted our experiments in two phases. First, we compared a simple VE with the proposed layered VE. Second, a comparative study is conducted between the proposed marker, a simple layered marker, and multiple single markers. We found the proposed marker with improved learning, easiness in interaction, and comparatively less task execution time. The results gave improved learning for layered VE as compared to simple VE. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T09:59:03Z No. of bitstreams: 1 ijimai_6_4_6.pdf: 1665600 bytes, checksum: 49124bc089a60d2f9492c14012ecbe4f (MD5); Made available in DSpace on 2022-04-05T09:59:03Z (GMT). No. of bitstreams: 1 ijimai_6_4_6.pdf: 1665600 bytes, checksum: 49124bc089a60d2f9492c14012ecbe4f (MD5)

Rumour Source Detection Using Game Theory

Rumour Source Detection Using Game Theory Jain, Minni; Jaswani, Aman; Mehra, Ankita; Mudgal, Laqshay Social networks have become a critical part of our lives as they enable us to interact with a lot of people. These networks have become the main sources for creating, sharing and also extracting information regarding various subjects. But all this information may not be true and may contain a lot of unverified rumours that have the potential of spreading incorrect information to the masses, which may even lead to situations of widespread panic. Thus, it is of great importance to identify those nodes and edges that play a crucial role in a network in order to find the most influential sources of rumour spreading. Generally, the basic idea is to classify the nodes and edges in a network with the highest criticality. Most of the existing work regarding the same focuses on using simple centrality measures which focus on the individual contribution of a node in a network. Game-theoretic approaches such as Shapley Value (SV) algorithms suggest that individual marginal contribution should be measured for a given player as the weighted average marginal increase in the yield of any coalition that this player might join. For our experiment, we have played five SV-based games to find the top 10 most influential nodes on three network datasets (Enron, USAir97 and Les Misérables). We have compared our results to the ones obtained by using primitive centrality measures. Our results show that SVbased approach is better at understanding the marginal contribution, and therefore the actual influence, of each node to the entire network. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T09:54:02Z No. of bitstreams: 1 ijimai_6_4_5.pdf: 883619 bytes, checksum: 28c8eb329098e5d62a8071fbf03b8175 (MD5); Made available in DSpace on 2022-04-05T09:54:02Z (GMT). No. of bitstreams: 1 ijimai_6_4_5.pdf: 883619 bytes, checksum: 28c8eb329098e5d62a8071fbf03b8175 (MD5)

TD2SecIoT: Temporal, Data-Driven and Dynamic Network Layer Based Security Architecture for Industrial IoT

TD2SecIoT: Temporal, Data-Driven and Dynamic Network Layer Based Security Architecture for Industrial IoT Dejene, Dawit; Tiwari, Basant; Tiwari, Vivek The Internet of Things (IoT) is an emerging technology, which comprises wireless smart sensors and actuators. Nowadays, IoT is implemented in different areas such as Smart Homes, Smart Cities, Smart Industries, Military, eHealth, and several real-world applications by connecting domain-specific sensors. Designing a security model for these applications is challenging for researchers since attacks (for example, zero-day) are increasing tremendously. Several security methods have been developed to ensure the CIA (Confidentiality, Integrity, and Availability) for Industrial IoT (IIoT). Though these methods have shown promising results, there are still some security issues that are open. Thus, the security and authentication of IoT based applications become quite significant. In this paper, we propose TD2SecIoT (Temporal, Data-Driven and Dynamic Network Layer Based Security Architecture for Industrial IoT), which incorporates Elliptic Curve Cryptography (ECC) and Nth-degree Truncated Polynomial Ring Units (NTRU) methods to ensure confidentiality and integrity. The proposed method has been evaluated against different attacks and performance measures (quantitative and qualitative) using the Cooja network simulator with Contiki-OS. The TD2SecIoT has shown a higher security level with reduced computational cost and time. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T09:40:11Z No. of bitstreams: 1 ijimai_6_4_16.pdf: 1476946 bytes, checksum: 16dcdb17bb3d3585f2a3c21f1d4786ac (MD5); Made available in DSpace on 2022-04-05T09:40:11Z (GMT). No. of bitstreams: 1 ijimai_6_4_16.pdf: 1476946 bytes, checksum: 16dcdb17bb3d3585f2a3c21f1d4786ac (MD5)

A Fine-Grained Model to Assess Learner-Content and Methodology Satisfaction in Distance Education

A Fine-Grained Model to Assess Learner-Content and Methodology Satisfaction in Distance Education Cantabella, Magdalena; Martínez-España, Raquel; López, Belén; Muñoz, Andrés Learning Management System (LMS) platforms have led to a transformation in Universities in the last decade, helping them to adapt and expand their services to new technological challenges. These platforms have made possible the expansion of distance education. A current trend in this area is focused on the evaluation and improvement of the students’ satisfaction. In this work a new tool to assess student satisfaction using emoticons (smileys) is proposed to evaluate the quality of the learning content and the methodology at unit level for any course and at any time. The results indicate that the assessment of student satisfaction is sensitive to the period when the survey is performed and to the student’s study level. Moreover, the results of this new proposal are compared to the satisfaction results using traditional surveys, showing different results due to a more accuracy and flexibility when using the tool proposed in this work. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T09:13:52Z No. of bitstreams: 1 ijimai_6_4_9.pdf: 844527 bytes, checksum: 5678413b7c54c73f979a94a5d43c68e4 (MD5); Made available in DSpace on 2022-04-05T09:13:52Z (GMT). No. of bitstreams: 1 ijimai_6_4_9.pdf: 844527 bytes, checksum: 5678413b7c54c73f979a94a5d43c68e4 (MD5)

Machine Learning Based Method for Estimating Energy Losses in Large-Scale Unbalanced Distribution Systems with Photovoltaics

Machine Learning Based Method for Estimating Energy Losses in Large-Scale Unbalanced Distribution Systems with Photovoltaics Mahmoud, Karar; Abdel-Nasser, Mohamed; Kashef, Heba; Puig, Domenec; Lehtonen, Matti In the recent years, the penetration of photovoltaics (PV) has obviously been increased in unbalanced power distribution systems. Driven by this trend, comprehensive simulation tools are required to accurately analyze large-scale distribution systems with a fast-computational speed. In this paper, we propose an efficient method for performing time-series simulations for unbalanced power distribution systems with PV. Unlike the existing iterative methods, the proposed method is based on machine learning. Specifically, we propose a fast, reliable and accurate method for determining energy losses in distribution systems with PV. The proposed method is applied to a large-scale unbalanced distribution system (the IEEE 906 Bus European LV Test Feeder) with PV grid-connected units. The method is validated using OpenDSS software. The results demonstrate the high accuracy and computational performance of the proposed method. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T09:08:16Z No. of bitstreams: 1 ijimai_6_4_17.pdf: 963471 bytes, checksum: 044ebe7277c34bcd250cf9b400fdfc0a (MD5); Made available in DSpace on 2022-04-05T09:08:16Z (GMT). No. of bitstreams: 1 ijimai_6_4_17.pdf: 963471 bytes, checksum: 044ebe7277c34bcd250cf9b400fdfc0a (MD5)

An Elitist Non-Dominated Multi-Objective Genetic Algorithm Based Temperature Aware Circuit Synthesis

An Elitist Non-Dominated Multi-Objective Genetic Algorithm Based Temperature Aware Circuit Synthesis Das, Apangshu; Pradhan, Sambhu Nath At sub-nanometre technology, temperature is one of the important design parameters to be taken care of during the target implementation for the circuit for its long term and reliable operation. High device package density leads to high power density that generates high temperatures. The temperature of a chip is directly proportional to the power density of the chip. So, the power density of a chip can be minimized to reduce the possibility of the high temperature generation. Temperature minimization approaches are generally addressed at the physical design level but it incurs high cooling cost. To reduce the cooling cost, the temperature minimization approaches can be addressed at the logic level. In this work, the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) based multi-objective heuristic approach is proposed to select the efficient input variable polarity of Mixed Polarity Reed-Muller (MPRM) expansion for simultaneous optimization of area, power, and temperature. A Pareto optimal solution set is obtained from the vast solution set of 3n (‘n’ is the number of input variables) different polarities of MPRM. Tabular technique is used for input polarity conversion from Sum-of-Product (SOP) form to MPRM form. Finally, using CADENCE and HotSpot tool absolute temperature, silicon area and power consumption of the synthesized circuits are calculated and are reported. The proposed algorithm saves around 76.20% silicon area, 29.09% power dissipation and reduces 17.06% peak temperature in comparison with the reported values in the literature. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T09:01:12Z No. of bitstreams: 1 ijimai_6_4_3.pdf: 1460901 bytes, checksum: 851af4d29c4bcbb38d17213b7b1d39a7 (MD5); Made available in DSpace on 2022-04-05T09:01:12Z (GMT). No. of bitstreams: 1 ijimai_6_4_3.pdf: 1460901 bytes, checksum: 851af4d29c4bcbb38d17213b7b1d39a7 (MD5)

Multi-sense Embeddings Using Synonym Sets and Hypernym Information from Wordnet

Multi-sense Embeddings Using Synonym Sets and Hypernym Information from Wordnet Prasad Mudigonda, Krishna Siva; Sharma, Poonam Word embedding approaches increased the efficiency of natural language processing (NLP) tasks. Traditional word embeddings though robust for many NLP activities, do not handle polysemy of words. The tasks of semantic similarity between concepts need to understand relations like hypernymy and synonym sets to produce efficient word embeddings. The outcomes of any expert system are affected by the text representation. Systems that understand senses, context, and definitions of concepts while deriving vector representations handle the drawbacks of single vector representations. This paper presents a novel idea for handling polysemy by generating Multi-Sense Embeddings using synonym sets and hypernyms information of words. This paper derives embeddings of a word by understanding the information of a word at different levels, starting from sense to context and definitions. Proposed sense embeddings of words obtained prominent results when tested on word similarity tasks. The proposed approach is tested on nine benchmark datasets, which outperformed several state-of-the-art systems. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-05T08:56:48Z No. of bitstreams: 1 ijimai_6_4_7.pdf: 691431 bytes, checksum: ce25fc0f7aa8a30f00141ace94a4ce6a (MD5); Made available in DSpace on 2022-04-05T08:56:48Z (GMT). No. of bitstreams: 1 ijimai_6_4_7.pdf: 691431 bytes, checksum: ce25fc0f7aa8a30f00141ace94a4ce6a (MD5)

Editor’s Note. Towards Blockchain Intelligence

Editor’s Note. Towards Blockchain Intelligence Baldominos Gómez, Alejandro; Mochón, Francisco In this special issue, we want to gather some innovative applications that are currently pushing forward the research on Blockchain technologies. In particular, we are interested also in those applications that put the focus on the data, enabling new processes that are able to leverage relevant knowledge from the data. This special issue will be successful if readers gain a better understanding on how Blockchain can be applied to very diverse areas, and might even be interested in designing, implementing and deploying an innovative solution to a completely different field of knowledge. We hope this Special Issue can provide a better understanding and key insights to readers on how Blockchain and artificial intelligence are cross-fertilizing to revolutionize many aspects in our societies. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-01T08:12:58Z No. of bitstreams: 1 ijimai_6_3_0.pdf: 602540 bytes, checksum: cf06c948841e77b6fd71225a7c1b83de (MD5); Made available in DSpace on 2022-04-01T08:12:58Z (GMT). No. of bitstreams: 1 ijimai_6_3_0.pdf: 602540 bytes, checksum: cf06c948841e77b6fd71225a7c1b83de (MD5)

Blockchain-Enabled Platforms: Challenges and Recommendations

Blockchain-Enabled Platforms: Challenges and Recommendations García Sáez, M. Inmaculada Not even a tenth of blockchain-enabled platforms survive their first anniversary. The volatility of cryptomarkets has brought negative attention and led some to question the applicability of blockchain technology. This paper argues that the challenges for startups and incumbents behind these platforms are numerous, and that the speculative bubble around cryptocurrencies is only one of them. Blockchain still needs to demonstrate fully its disruptive potential and so far, entrepreneurs have not managed to significantly impact incumbents’ market shares. This transitory period requires incumbents to let go of traditional control mechanisms, and startups to scale down their global decentralised hopes. Indeed, whilst the technology can indeed scale fast, starting in a controlled market and managing growth is a counterintuitive but essential strategy for blockchain-enabled platforms to implement. Given the diverging nature of the technology, at present at least, the combined shortage of skills in blockchain and security, and the trust blockchain is built on, rushing to the global market is high risk. Nonetheless, given the potential returns, the risk appetite is high and both entrepreneurs and corporate executives share unrealistic expectations about a technology they cannot fully understand since it has not yet converged. In light of the above, this article identifies the main challenges faced when building blockchainenabled platforms and provides recommendations for startups and incumbents to overcome these. In order to reach these conclusions, the information obtained from twenty semi-structured interviews with leading actors in the field has been fundamental. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-01T07:59:45Z No. of bitstreams: 1 ijimai_6_3_9_0.pdf: 941493 bytes, checksum: d38fa9842b639738ec58d30a2a7a4e0f (MD5); Made available in DSpace on 2022-04-01T07:59:45Z (GMT). No. of bitstreams: 1 ijimai_6_3_9_0.pdf: 941493 bytes, checksum: d38fa9842b639738ec58d30a2a7a4e0f (MD5)

Traceable Ecosystem and Strategic Framework for the Creation of an Integrated Pest Management System for Intensive Farming

Traceable Ecosystem and Strategic Framework for the Creation of an Integrated Pest Management System for Intensive Farming Lopez, Miguel Angel; Lombardo, Juan Manuel; López, Mabel; Álvarez, David; Velasco, Susana; Terrón, Sara The appearance of pests is one of the major problems that exist in the growth of crops, as they can damage the production if the appropriate measures are not taken. Within the framework of the Integrated Pest Management strategy (IPM), early detection of pests is an essential step in order to provide the most appropriate treatment and avoid losses. This paper proposes the architecture of a system intensive farming in greenhouses featuring the ability to detect environmental variations that may favour the appearance of pests. This system can suggest a plan or treatment that will help mitigate the effects that the identified pest would produce otherwise. Furthermore, the system will learn from the actions carried out by the humans throughout the different stages of crop growing and will add it as knowledge for the prediction of future actions. The data collected from sensors, through computer vision, or the experiences provided by the experts, along with the historical data related to the crop, will allow for the development of a model that contrasts the predictions of the actions that could be implemented with those already performed by technicians. Within the technological ecosystems in which the Integrated Pest Management systems develop their action, traceability models must be incorporated. This will guarantee that the data used for the exploitation of the information and, therefore for the parameterization of the predictive models, are adequate. Thus, the integration of blockchain technologies is considered key to provide them with security and confidence. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-01T07:55:33Z No. of bitstreams: 1 ijimai_6_3_6.pdf: 1120024 bytes, checksum: 0861caab5862d06336738d0136b9b5c8 (MD5); Made available in DSpace on 2022-04-01T07:55:33Z (GMT). No. of bitstreams: 1 ijimai_6_3_6.pdf: 1120024 bytes, checksum: 0861caab5862d06336738d0136b9b5c8 (MD5)

Intelligent Detection and Recovery from Cyberattacks for Small and Medium-Sized Enterprises

Intelligent Detection and Recovery from Cyberattacks for Small and Medium-Sized Enterprises Lopez, Miguel Angel; Lombardo, Juan Manuel; López, Mabel; Alba, Carmen María; Velasco, Susana; Braojos, Manuel Alonso; Fuentes-García, Marta Cyberattacks threaten continuously computer security in companies. These attacks evolve everyday, being more and more sophisticated and robust. In addition, they take advantage of security breaches in organizations and companies, both public and private. Small and Medium-sized Enterprises (SME), due to their structure and economic characteristics, are particularly damaged when a cyberattack takes place. Although organizations and companies put lots of efforts in implementing security solutions, they are not always effective. This is specially relevant for SMEs, which do not have enough economic resources to introduce such solutions. Thus, there is a need of providing SMEs with affordable, intelligent security systems with the ability of detecting and recovering from the most detrimental attacks. In this paper, we propose an intelligent cybersecurity platform, which has been designed with the objective of helping SMEs to make their systems and network more secure. The aim of this platform is to provide a solution optimizing detection and recovery from attacks. To do this, we propose the application of proactive security techniques in combination with both Machine Learning (ML) and blockchain. Our proposal is enclosed in the IASEC project, which allows providing security in each of the phases of an attack. Like this, we help SMEs in prevention, avoiding systems and network from being attacked; detection, identifying when there is something potentially harmful for the systems; containment, trying to stop the effects of an attack; and response, helping to recover the systems to a normal state. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-01T07:44:29Z No. of bitstreams: 1 ijimai_6_3_7.pdf: 1126197 bytes, checksum: 374045403446b2cd93a125c9b7731874 (MD5); Made available in DSpace on 2022-04-01T07:44:29Z (GMT). No. of bitstreams: 1 ijimai_6_3_7.pdf: 1126197 bytes, checksum: 374045403446b2cd93a125c9b7731874 (MD5)

Efficient Method Based on Blockchain Ensuring Data Integrity Auditing with Deduplication in Cloud

Efficient Method Based on Blockchain Ensuring Data Integrity Auditing with Deduplication in Cloud El Ghazouani, Mohamed; El kiram, My Ahmed; Latifa, ER-RAJY; El Khanboubi, Yassine With the rapid development of cloud storage, more and more cloud clients can store and access their data anytime, from anywhere and using any device. Data deduplication may be considered an excellent choice to ensure data storage efficiency. Although cloud technology offers many advantages for storage service, it also introduces security challenges, especially with regards to data integrity, which is one of the most critical elements in any system. A data owner should thus enable data integrity auditing mechanisms. Much research has recently been undertaken to deal with these issues. In this paper, we propose a novel blockchain-based method, which can preserve cloud data integrity checking with data deduplication. In our method, a mediator performs data deduplication on the client side, which permits a reduction in the amount of outsourced data and a decrease in the computation time and the bandwidth used between the enterprise and the cloud service provider. This method supports private and public auditability. Our method also ensures the confidentiality of a client's data against auditors during the auditing process. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-01T07:33:14Z No. of bitstreams: 1 ijimai_6_3_4.pdf: 1512817 bytes, checksum: 6c6412d5010d5d959b75a218d8c2c4f3 (MD5); Made available in DSpace on 2022-04-01T07:33:14Z (GMT). No. of bitstreams: 1 ijimai_6_3_4.pdf: 1512817 bytes, checksum: 6c6412d5010d5d959b75a218d8c2c4f3 (MD5)

Smart Contracts with Blockchain in the Public Sector

Smart Contracts with Blockchain in the Public Sector Triana Casallas, Jenny Alexandra; Cueva-Lovelle, Juan Manuel; Rodríguez Molano, José Ignacio The appearance of so-called block chains or Blockchain with the promise of transforming trust and the way value is exchanged, joins the expansion of the technological capabilities of organizations to achieve higher levels of productivity and innovation. This is how Blockchain-based techniques are being applied to many fields, focusing in this article on the public sector, as a possible solution to the demands for transparency, participation and citizen cooperation that society demands; due to the possibility of disintermediation based on automated transactions and on the responsibility and security in the management of official blockchain records. This could obstruct corruption and make government services more transparent and efficient. Although, it investigates about applications in the public sector under the Blockchain system, such as transactions, agreements, property registries and innovations, developments and other assets; Special emphasis is placed on the possibility of implementing Smart Contracts (mechanisms that aim to eliminate intermediaries to simplify processes) in public procurement procedures, given that it is in this type of activity where high levels of corruption are generated. It is concluded then that Europe has the largest number of blockchain initiatives worldwide, while Latin America, except for the case of Peru, lacks this type of applications, being this continent exactly where there are the countries with the highest levels of corruption. It concludes with a recommendation to use blockchain along with smart contracts through platforms such as Ethereum or Lisk, mainly given its flexibility and current development on topics with similar functionalities. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-31T12:31:30Z No. of bitstreams: 1 ijimai_6_3_8.pdf: 881822 bytes, checksum: 45fc900fa9d5b9959ed57b1094cf0f6f (MD5); Made available in DSpace on 2022-03-31T12:31:30Z (GMT). No. of bitstreams: 1 ijimai_6_3_8.pdf: 881822 bytes, checksum: 45fc900fa9d5b9959ed57b1094cf0f6f (MD5)

Blockchain for Healthcare: Securing Patient Data and Enabling Trusted Artificial Intelligence

Blockchain for Healthcare: Securing Patient Data and Enabling Trusted Artificial Intelligence Jennath, H. S.; Anoop, V S; Asharaf, S Advances in information technology are digitizing the healthcare domain with the aim of improved medical services, diagnostics, continuous monitoring using wearables, etc., at reduced costs. This digitization improves the ease of computation, storage and access of medical records which enables better treatment experiences for patients. However, it comes with a risk of cyber attacks and security and privacy concerns on this digital data. In this work, we propose a Blockchain based solution for healthcare records to address the security and privacy concerns which are currently not present in existing e-Health systems. This work also explores the potential of building trusted Artificial Intelligence models over Blockchain in e-Health, where a transparent platform for consent-based data sharing is designed. Provenance of the consent of individuals and traceability of data sources used for building and training the AI model is captured in an immutable distributed data store. The audit trail of the data access captured using Blockchain provides the data owner to understand the exposure of the data. It also helps the user to understand the revenue models that could be built on top of this framework for commercial data sharing to build trusted AI models. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-30T10:09:51Z No. of bitstreams: 1 ijimai_6_3_2.pdf: 897690 bytes, checksum: 94df0f314329e49d43e51a06d8407c92 (MD5); Made available in DSpace on 2022-03-30T10:09:51Z (GMT). No. of bitstreams: 1 ijimai_6_3_2.pdf: 897690 bytes, checksum: 94df0f314329e49d43e51a06d8407c92 (MD5)

Tracking News Stories Using Blockchain to Guarantee their Traceability and Information Analysis

Tracking News Stories Using Blockchain to Guarantee their Traceability and Information Analysis Jurado, Francisco; Delgado, Oscar; Ortigosa, Álvaro Nowadays, having a mechanism to guarantee the traceability of the information and to monitor the evolution of the news from its origin, and having elements to know the reputation and credibility of the media, analyze the news as well as its evolution and possible manipulation, etc. is becoming increasingly significant. Transparency in journalism is currently a key element in performing serious and rigorous journalism. End-users and fact-checking agencies need to be able to check and verify the information published in different media. This transparency principle enables the tracking of news stories and allows direct access to the source of essential content to contrast the information it contains and to know whether it has been manipulated. Additionally, the traceability of news constitutes another instrument in the fight against the lack of credibility, the manipulation of information, misinformation campaigns and the propagation of fake news. This article aims to show how to use Blockchain to facilitate the tracking and traceability of news so that it can provide support to the automatic indexing and extraction of relevant information from newspaper articles to facilitate the monitoring of the news story and allows users to verify the veracity of what they are reading. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-30T09:45:47Z No. of bitstreams: 1 ijimai_6_3_5.pdf: 1192296 bytes, checksum: c9b57310b6c1319658160073c28c039b (MD5); Made available in DSpace on 2022-03-30T09:45:47Z (GMT). No. of bitstreams: 1 ijimai_6_3_5.pdf: 1192296 bytes, checksum: c9b57310b6c1319658160073c28c039b (MD5)

Blockverse: A Cloud Blockchain-based Platform for Tracking in Affiliate Systems

Blockverse: A Cloud Blockchain-based Platform for Tracking in Affiliate Systems Baldominos Gómez, Alejandro; López-Sánchez, J. L.; Acevedo-Aguilar, M. Affiliate systems are a crucial piece of today’s online advertising. In affiliate systems, web traffic is directed from certain sites displaying ads to the websites of those company whose products or services are advertised. The way in which these ads are monetized is diverse and can respond to different models. In many cases, affiliates establish a cost based on impressions (displays of the ad) or on clicks. However, more intricate models are becoming widespread, such as the cost per action, where the affiliate incomes are due to the users performing certain actions in the target website. In particular, in the world of iGaming, it is frequent that affiliates charges are based on registrations, deposits or money lost on bets. In this scenario, Blockverse is a tool whose objective is to record transactions occurring in affiliate systems at large scale, using a permissioned blockchain implemented atop state-of-the-art cloud technology. Additionally, the system will be able to execute smart deals that generate income for affiliates based on the agreed conditions, and to provide real-time analytics in the context of the affiliate system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-30T09:40:34Z No. of bitstreams: 1 ijimai_6_3_3.pdf: 1247230 bytes, checksum: ebfcf1ff751be66d4c81cdd1a227531d (MD5); Made available in DSpace on 2022-03-30T09:40:34Z (GMT). No. of bitstreams: 1 ijimai_6_3_3.pdf: 1247230 bytes, checksum: ebfcf1ff751be66d4c81cdd1a227531d (MD5)

Innovation and Challenges of Blockchain in Banking: A Scientometric View

Innovation and Challenges of Blockchain in Banking: A Scientometric View Arjun, R; Suprabha, K R Blockchain has been gaining focus in research and development for diverse industries in recent years. Nevertheless, innovations that impact to the banking nurture a potential for disruptive impact globally for economic reasons; however it has received less scholarly attention. Hence the effect of blockchain technologies on banking industry is systematically reviewed. The relevant literature is extracted from Scopus, Web of Science and bibliometric techniques are applied. While a bulk of earlier papers focuses only on bit coins, a broader framework is envisaged that synthesizes interdisciplinary thematic areas for advancement; hence novelty in current work. A few practical and theoretical implications for stakeholders in view of technology, law and management are discussed. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-30T09:24:44Z No. of bitstreams: 1 ijimai_6_3_1.pdf: 1058825 bytes, checksum: 4d30739e16aa26da9722d9b2b738e812 (MD5); Made available in DSpace on 2022-03-30T09:24:44Z (GMT). No. of bitstreams: 1 ijimai_6_3_1.pdf: 1058825 bytes, checksum: 4d30739e16aa26da9722d9b2b738e812 (MD5)

Editor's Note

Editor's Note Verdú, Elena The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-30T09:05:50Z No. of bitstreams: 1 ijimai_6_2_0_0.pdf: 218730 bytes, checksum: fb02c3bff097ce53b5a827ec0f0ad6e5 (MD5); Made available in DSpace on 2022-03-30T09:05:50Z (GMT). No. of bitstreams: 1 ijimai_6_2_0_0.pdf: 218730 bytes, checksum: fb02c3bff097ce53b5a827ec0f0ad6e5 (MD5)

Guidelines for performing Systematic Research Projects Reviews

Guidelines for performing Systematic Research Projects Reviews García-Holgado, Alicia; Marcos-Pablos, Samuel; García-Peñalvo, Francisco There are different methods and techniques to carry out systematic reviews in order to address a set of research questions or getting the state of the art of a particular topic, but there is no a method to carry out a systematic analysis of research projects not only based on scientific publications. The main challenge is the difference between research projects and scientific literature. Research projects are a collection of information in different formats and available in different places. Even projects from the same funding call follow a different structure in most of the cases, despite there were some requirements that they should meet at the end of the funding period. Furthermore, the sources in which the scientific literature is available provide metadata and powerful search tools, meanwhile most of the research projects are not stored in public and accessible databases, or the databases usually do not provide enough information and tools to conduct a systematic search. For this reason, this work provides the guidelines to support systematics reviews of research projects following the method called Systematic Research Projects Review (SRPR). This methodology is based on the Kitchenham’s adaptation of the systematic literature review. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-30T09:02:48Z No. of bitstreams: 1 ijimai_6_2_14.pdf: 440071 bytes, checksum: a53edc965492366b973371efa1c5b57a (MD5); Made available in DSpace on 2022-03-30T09:02:48Z (GMT). No. of bitstreams: 1 ijimai_6_2_14.pdf: 440071 bytes, checksum: a53edc965492366b973371efa1c5b57a (MD5)

Time-Dependent Performance Prediction System for Early Insight in Learning Trends

Time-Dependent Performance Prediction System for Early Insight in Learning Trends Villagrá-Arnedo, Carlos; Gallego-Durán, Francisco; Llorens-Largo, Faraón; Satorre-Cuerda, Rosana; Compañ-Rosique, Patricia; Molina-Carmona, Rafael Performance prediction systems allow knowing the learning status of students during a term and produce estimations on future status, what is invaluable information for teachers. The majority of current systems statically classify students once in time and show results in simple visual modes. This paper presents an innovative system with progressive, time-dependent and probabilistic performance predictions. The system produces by-weekly probabilistic classifications of students in three groups: high, medium or low performance. The system is empirically tested and data is gathered, analysed and presented. Predictions are shown as point graphs over time, along with calculated learning trends. Summary blocks are with latest predictions and trends are also provided for teacher efficiency. Moreover, some methods for selecting best moments for teacher intervention are derived from predictions. Evidence gathered shows potential to give teachers insights on students' learning trends, early diagnose learning status and selecting best moment for intervention. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-30T08:55:35Z No. of bitstreams: 1 ijimai_6_2_12.pdf: 1394113 bytes, checksum: cb7d0a7cbf4cf8d8122808ff1f837de4 (MD5); Made available in DSpace on 2022-03-30T08:55:35Z (GMT). No. of bitstreams: 1 ijimai_6_2_12.pdf: 1394113 bytes, checksum: cb7d0a7cbf4cf8d8122808ff1f837de4 (MD5)

A Holistic Methodology for Improved RFID Network Lifetime by Advanced Cluster Head Selection using Dragonfly Algorithm

A Holistic Methodology for Improved RFID Network Lifetime by Advanced Cluster Head Selection using Dragonfly Algorithm Rathore, Pramod Singh; Kumar, Abhishek; García-Díaz, Vicente Radio Frequency Identification (RFID) networks usually require many tags along with readers and computation facilities. Those networks have limitations with respect to computing power and energy consumption. Thus, for saving energy and to make the best use of the resources, networks should operate and be able to recover in an efficient way. This will also reduce the energy expenditure of RFID readers. In this work, the RFID network life span will be enlarged through an energy-efficient cluster-based protocol used together with the Dragonfly algorithm. There are two stages in the processing of the clustering system: the cluster formation from the whole structure and the election of a cluster leader. After completing those procedures, the cluster leader controls the other nodes that are not leaders. The system works with a large energy node that provides an amount of energy while transmitting aggregated data near a base station. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-30T08:31:00Z No. of bitstreams: 1 ijimai_6_2_6.pdf: 599844 bytes, checksum: f53bc1f2b8cd1e0072373f85570ad7cf (MD5); Made available in DSpace on 2022-03-30T08:31:00Z (GMT). No. of bitstreams: 1 ijimai_6_2_6.pdf: 599844 bytes, checksum: f53bc1f2b8cd1e0072373f85570ad7cf (MD5)

An Experimental Study on Microarray Expression Data from Plants under Salt Stress by using Clustering Methods

An Experimental Study on Microarray Expression Data from Plants under Salt Stress by using Clustering Methods Fyad, Houda; Barigou, Fatiha; Bouamrane, Karim Current Genome-wide advancements in Gene chips technology provide in the “Omics (genomics, proteomics and transcriptomics) research”, an opportunity to analyze the expression levels of thousand of genes across multiple experiments. In this regard, many machine learning approaches were proposed to deal with this deluge of information. Clustering methods are one of these approaches. Their process consists of grouping data (gene profiles) into homogeneous clusters using distance measurements. Various clustering techniques are applied, but there is no consensus for the best one. In this context, a comparison of seven clustering algorithms was performed and tested against the gene expression datasets of three model plants under salt stress. These techniques are evaluated by internal and relative validity measures. It appears that the AGNES algorithm is the best one for internal validity measures for the three plant datasets. Also, K-Means profiles a trend for relative validity measures for these datasets. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-29T12:25:31Z No. of bitstreams: 1 ijimai_6_2_5.pdf: 436078 bytes, checksum: f887b8f6a1bb40e3b530450ae5972038 (MD5); Made available in DSpace on 2022-03-29T12:25:31Z (GMT). No. of bitstreams: 1 ijimai_6_2_5.pdf: 436078 bytes, checksum: f887b8f6a1bb40e3b530450ae5972038 (MD5)

Adjectives Grouping in a Dimensionality Affective Clustering Model for Fuzzy Perceptual Evaluation

Adjectives Grouping in a Dimensionality Affective Clustering Model for Fuzzy Perceptual Evaluation Huang, Wenlin; Wu, Qun; Dey, Nilanjan; Ashour, Amira; Fong, Simon James; González-Crespo, Rubén More and more products are no longer limited to the satisfaction of the basic needs, but reflect the emotional interaction between people and environment. The characteristics of user emotions and their evaluation scales are relatively simple. This paper proposes a three-dimensional space model valence-arousal-dominance (VAD) based on the theory of psychological dimensional emotions. It studies the clustering and evaluation of emotional phrases, called VAdC (VAD-dimensional clustering), which is a kind of the affective computing technology. Firstly, a Gaussian Mixture Model (GMM) based information presentation system was introduced, including the type of the presentation, such as single point, plain, and sphere. Subsequently, the border of the presentation was defined. To increase the ability of the proposed algorithm to handle a high dimensional affective space, the distance and inference mechanics were addressed to avoid lacking of local measurement by using fuzzy perceptual evaluation. By comparing the performance of the proposed method with fuzzy c-mean (FCM), k-mean, hard -c-mean (HCM), extra fuzzy c-mean (EFCM), the proposed VADdC performs high effectiveness in fitness, inter-distance, intra-distance, and accuracy. The results were based on the dataset created from a questionnaire on products of the Ming style chairs online evaluation system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-29T12:19:04Z No. of bitstreams: 1 ijimai_6_2_4.pdf: 1578366 bytes, checksum: b6f221007c63f054582f09f1b62d94a5 (MD5); Made available in DSpace on 2022-03-29T12:19:04Z (GMT). No. of bitstreams: 1 ijimai_6_2_4.pdf: 1578366 bytes, checksum: b6f221007c63f054582f09f1b62d94a5 (MD5)

NFC and VLC based Mobile Business Information System for Registering Class Attendance

NFC and VLC based Mobile Business Information System for Registering Class Attendance Rios-Aguilar, Sergio; Sarría, Íñigo; Beltrán Pardo, Marta This work proposes a Mobile Information System for class attendance control using Visible Light Communications (VLC), and the students’ own mobile devices for automatic clocking in and clocking out. The proposed information system includes (a) VLC physical infrastructure, (b) native Android and iOS apps for the students, and (c) a web application for classroom attendance management. A proof of concept has been developed, setting up a testbed representing a real-world classroom environment for experimentation, using two VLC-enabled LED lighting sources. After three rounds of testing (n=225) under different conditions, it has been concluded that the system is viable and shows consistent positive detections when the smartphones are on the classroom desk within non-overlapped areas of the light circles generated by the LED lighting sources on the table surface. The performed tests also show that if mobile devices are placed within those overlapping areas, the likelihood of a detection error could increase up to nearly 10%, due to multipath effects, and actions can be taken should it happen. Finally, it has to be highlighted that the proposed autonomous class attendance system allows lecturers to focus on making the most of their time in class, transferring knowledge instead of spending time in attendance management task. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-29T11:43:05Z No. of bitstreams: 1 ijimai_6_2_8.pdf: 453041 bytes, checksum: 89edf5d7abd49f4725ef9b1d6e30995a (MD5); Made available in DSpace on 2022-03-29T11:43:05Z (GMT). No. of bitstreams: 1 ijimai_6_2_8.pdf: 453041 bytes, checksum: 89edf5d7abd49f4725ef9b1d6e30995a (MD5)

COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach

COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach Saiz, Fátima; Barandiaran, Iñigo The Corona Virus Disease (COVID-19) is an infectious disease caused by a new virus that has not been detected in humans before. The virus causes a respiratory illness like the flu with various symptoms such as cough or fever that, in severe cases, may cause pneumonia. The COVID-19 spreads so quickly between people, affecting to 1,200,000 people worldwide at the time of writing this paper (April 2020). Due to the number of contagious and deaths are continually growing day by day, the aim of this study is to develop a quick method to detect COVID-19 in chest X-ray images using deep learning techniques. For this purpose, an object detection architecture is proposed, trained and tested with a public available dataset composed with 1500 images of non-infected patients and infected with COVID-19 and pneumonia. The main goal of our method is to classify the patient status either negative or positive COVID-19 case. In our experiments using SDD300 model we achieve a 94.92% of sensibility and 92.00% of specificity in COVID-19 detection, demonstrating the usefulness application of deep learning models to classify COVID-19 in X-ray images. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-29T11:32:54Z No. of bitstreams: 1 ijimai_6_2_2.pdf: 562218 bytes, checksum: b0cd720d8a9ec1a16263f8044cd49428 (MD5); Made available in DSpace on 2022-03-29T11:32:54Z (GMT). No. of bitstreams: 1 ijimai_6_2_2.pdf: 562218 bytes, checksum: b0cd720d8a9ec1a16263f8044cd49428 (MD5)

Two-Stage Human Activity Recognition Using 2D-ConvNet

Two-Stage Human Activity Recognition Using 2D-ConvNet Verma, Kamal Kant; Singh, Brij Mohan; Mandoria, H L; Chauhan, Prachi There is huge requirement of continuous intelligent monitoring system for human activity recognition in various domains like public places, automated teller machines or healthcare sector. Increasing demand of automatic recognition of human activity in these sectors and need to reduce the cost involved in manual surveillance have motivated the research community towards deep learning techniques so that a smart monitoring system for recognition of human activities can be designed and developed. Because of low cost, high resolution and ease of availability of surveillance cameras, the authors developed a new two-stage intelligent framework for detection and recognition of human activity types inside the premises. This paper, introduces a novel framework to recognize single-limb and multi-limb human activities using a Convolution Neural Network. In the first phase single-limb and multi-limb activities are separated. Next, these separated single and multi-limb activities have been recognized using sequence-classification. For training and validation of our framework we have used the UTKinect-Action Dataset having 199 actions sequences performed by 10 users. We have achieved an overall accuracy of 97.88% in real-time recognition of the activity sequences. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T09:36:45Z No. of bitstreams: 1 ijimai_6_2_13.pdf: 1246366 bytes, checksum: 03e24ca5fdcf5ed6e210eb3694602aa7 (MD5); Made available in DSpace on 2022-03-28T09:36:45Z (GMT). No. of bitstreams: 1 ijimai_6_2_13.pdf: 1246366 bytes, checksum: 03e24ca5fdcf5ed6e210eb3694602aa7 (MD5)

Transmission Dynamics Model of Coronavirus COVID-19 for the Outbreak in Most Affected Countries of the World

Transmission Dynamics Model of Coronavirus COVID-19 for the Outbreak in Most Affected Countries of the World Dur-e-Ahmad, Muhammad; Imran, Mudassar The wide spread of coronavirus (COVID-19) has threatened millions of lives and damaged the economy worldwide. Due to the severity and damage caused by the disease, it is very important to fore-tell the epidemic lifetime in order to take timely actions. Unfortunately, the lack of accurate information and unavailability of large amount of data at this stage make the task more difficult. In this paper, we used the available data from the mostly affected countries by COVID-19, (China, Iran, South Korea and Italy) and fit this with the SEIR type model in order to estimate the basic reproduction number R_0. We also discussed the development trend of the disease. Our model is quite accurate in predicting the current pattern of the infected population. We also performed sensitivity analysis on all the parameters used that are affecting the value of R0. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T09:19:04Z No. of bitstreams: 1 ijimai_6_2_1.pdf: 642181 bytes, checksum: 767b7c1683e6d15fc221c4904e39ffd5 (MD5); Made available in DSpace on 2022-03-28T09:19:04Z (GMT). No. of bitstreams: 1 ijimai_6_2_1.pdf: 642181 bytes, checksum: 767b7c1683e6d15fc221c4904e39ffd5 (MD5)

Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models

Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models Kamel, Salah; Jurado, Francisco; Sultan, Hamdy; Menesy, Ahmed Demonstrating an accurate mathematical model is a mandatory issue for realistic simulation, optimization and performance evaluation of proton exchange membrane fuel cells (PEMFCs). The main goal of this study is to demonstrate a precise mathematical model of PEMFCs through estimating the optimal values of the unknown parameters of these cells. In this paper, an efficient optimization technique, namely, Tree Growth Algorithm (TGA) is applied for extracting the optimal parameters of different PEMFC stacks. The total of the squared deviations (TSD) between the experimentally measured data and the estimated ones is adopted as the objective function. The effectiveness of the developed parameter identification algorithm is validated through four case studies of commercial PEMFC stacks under various operating conditions. Moreover, comprehensive comparisons with other optimization algorithms under the same study cases are demonstrated. Statistical analysis is presented to evaluate the accuracy and reliability of the developed algorithm in solving the studied optimization problem. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T09:00:58Z No. of bitstreams: 1 ijimai_6_2_11.pdf: 1414303 bytes, checksum: 346adb416c4a4a7d1eacf7846db92438 (MD5); Made available in DSpace on 2022-03-28T09:00:58Z (GMT). No. of bitstreams: 1 ijimai_6_2_11.pdf: 1414303 bytes, checksum: 346adb416c4a4a7d1eacf7846db92438 (MD5)

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups Bobadilla, Jesús; Gutiérrez, Abraham; Alonso, Santiago; Hurtado, Remigio In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, colleagues, etc. Our proposal deals with large numbers of automatically detected groups. Marketing and electronic commerce are typical targets of large homogenous groups. Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations. We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings. Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation. Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim. We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information. Results show an improvement on recommendations made to large homogeneous groups. It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T08:37:43Z No. of bitstreams: 1 ijimai_6_2_10.pdf: 1543399 bytes, checksum: 2cb0399275af9245af7cd3074d62a74a (MD5); Made available in DSpace on 2022-03-28T08:37:43Z (GMT). No. of bitstreams: 1 ijimai_6_2_10.pdf: 1543399 bytes, checksum: 2cb0399275af9245af7cd3074d62a74a (MD5)

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data Núñez-Valdez, Edward; Solanki, Vijender Kumar; Balakrishna, Sivadi; Thirumaran, M In the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHCAA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely- Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T08:10:35Z No. of bitstreams: 1 ijimai_6_2_7_0.pdf: 1930171 bytes, checksum: fd01fa306746a128013500d6eadbf064 (MD5); Made available in DSpace on 2022-03-28T08:10:35Z (GMT). No. of bitstreams: 1 ijimai_6_2_7_0.pdf: 1930171 bytes, checksum: fd01fa306746a128013500d6eadbf064 (MD5)

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System Shikha, B; Gitanjali, P; Kumar, D. Pawan The process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system. In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image like texture, color and shape are extracted by using Gray level co-occurrence matrix (GLCM), color moment and various region props procedure respectively. A hybrid feature matrix or vector (HFV) is formed by an integration of feature vectors belonging to three individual visual attributes. This HFV is given as an input to an Extreme learning machine (ELM) classifier which is based on a solitary hidden layer of neurons and also is a type of feed-forward neural system. ELM performs efficient class prediction of the query image based on the pre-trained data. Lastly, to capture the high level human semantic information, Relevance feedback (RF) is utilized to retrain or reformulate the training of ELM. The advantage of the proposed system is that a combination of an ELM-RF framework leads to an evolution of a modified learning and intelligent classification system. To measure the efficiency of the proposed system, various parameters like Precision, Recall and Accuracy are evaluated. Average precision of 93.05%, 81.03%, 75.8% and 90.14% is obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM-10 benchmark datasets. The experimental analysis portrays that the implemented technique outmatches many state-of-the-art related approaches depicting varied hybrid CBIR system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T08:04:31Z No. of bitstreams: 1 ijimai_6_2_3.pdf: 1610842 bytes, checksum: 966a7ce3d029e4a6814811094f4f3b41 (MD5); Made available in DSpace on 2022-03-28T08:04:31Z (GMT). No. of bitstreams: 1 ijimai_6_2_3.pdf: 1610842 bytes, checksum: 966a7ce3d029e4a6814811094f4f3b41 (MD5)

On Improvement of Speech Intelligibility and Quality: A Survey of Unsupervised Single Channel Speech Enhancement Algorithms

On Improvement of Speech Intelligibility and Quality: A Survey of Unsupervised Single Channel Speech Enhancement Algorithms Saleem, Nasir; Khattak, Muhammad Irfan; Verdú, Elena Many forms of human communication exist; for instance, text and nonverbal based. Speech is, however, the most powerful and dexterous form for the humans. Speech signals enable humans to communicate and this usefulness of the speech signals has led to a variety of speech processing applications. Successful use of these applications is, however, significantly aggravated in presence of the background noise distortions. These noise signals overlap and mask the target speech signals. To deal with these overlapping background noise distortions, a speech enhancement algorithm at front end is crucial in order to make noisy speech intelligible and pleasant. Speech enhancement has become a very important research and engineering problem for the last couple of decades. In this paper, we present an all-inclusive survey on unsupervised single-channel speech enhancement (U-SCSE) algorithms. A taxonomy based review of the U-SCSE algorithms is presented and the associated studies regarding improving the intelligibility and quality are outlined. The studies on the speech enhancement algorithms in unsupervised perspective are presented. Objective experiments have been performed to evaluate the potential of the U-SCSE algorithms in terms of improving the speech intelligibility and quality. It is found that unsupervised speech enhancement improves the speech quality but the speech intelligibility improvement is deprived. To finish, several research problems are identified that require further research. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T07:57:47Z No. of bitstreams: 1 ijimai_6_2_9.pdf: 775025 bytes, checksum: 8c9cc953150755cb001aceebac2501f5 (MD5); Made available in DSpace on 2022-03-28T07:57:47Z (GMT). No. of bitstreams: 1 ijimai_6_2_9.pdf: 775025 bytes, checksum: 8c9cc953150755cb001aceebac2501f5 (MD5)

Fuzzy C-Means Clustering with Histogram based Cluster Selection for Skin Lesion Segmentation using Non-Dermoscopic Images

Fuzzy C-Means Clustering with Histogram based Cluster Selection for Skin Lesion Segmentation using Non-Dermoscopic Images Devi, Salam Shuleenda; Laskar, Rabul Hussain; Singh, Ngangbam Herojit Purpose – Pre-screening of skin lesion for malignancy is highly demanded as melanoma being a life-threatening skin cancer due to unpaired DNA damage. In this paper, lesion segmentation based on Fuzzy C-Means clustering using non-dermoscopic images has been proposed. Design/methodology/approach – The proposed methodology consists of automatic cluster selection for FCM using the histogram property. The system used the local maxima along with Euclidean distance to detect the binomial distribution property of the image histogram, to segment the melanoma from normal skin. As the Value channel of HSV color image provides better and distinct histogram distribution based on the entropy, it has been used for segmentation purpose. Findings – The proposed system can effectively segment the lesion region from the normal skin. The system provides a segmentation accuracy of 95.69 % and the comparative analysis has been performed with various segmentation methods. From the analysis, it has been observed that the proposed system can effectively segment the lesion region from normal skin automatically. Originality/Value – This paper suggests a new approach for skin lesion segmentation based on FCM with automatic cluster selection. Here, different color channel has also been analyzed using entropy to select the better channel for segmentation. In future, the classification of melanoma from benign naevi can be performed. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T13:34:44Z No. of bitstreams: 1 ijimai20206_1_3_pdf_17885.pdf: 1265459 bytes, checksum: 62187387480274425fa7ca793d82bbb9 (MD5); Made available in DSpace on 2022-03-24T13:34:44Z (GMT). No. of bitstreams: 1 ijimai20206_1_3_pdf_17885.pdf: 1265459 bytes, checksum: 62187387480274425fa7ca793d82bbb9 (MD5)

Learning Models for Semantic Classification of Insufficient Plantar Pressure Images

Learning Models for Semantic Classification of Insufficient Plantar Pressure Images Dey, Nilanjan; Wu, Yao; Wu, Qun; Sherratt, Simon Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)- based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T13:07:50Z No. of bitstreams: 1 ijimai20206_1_6_pdf_49479.pdf: 1043257 bytes, checksum: c2abae0a912570173755f564dba3b789 (MD5); Made available in DSpace on 2022-03-24T13:07:50Z (GMT). No. of bitstreams: 1 ijimai20206_1_6_pdf_49479.pdf: 1043257 bytes, checksum: c2abae0a912570173755f564dba3b789 (MD5)

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems Bobadilla, Jesús; Ortega, Fernando; Gutiérrez, Abraham; Alonso, Santiago This paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ratings dataset. The learning process is based on the binary relevant/non-relevant vote and the binary voted/non-voted item information. This data reduction provides a new level of abstraction and it makes possible to design the classification-based architecture. In addition to the original architecture, its prediction process has a novel approach: it does not need to make a large number of predictions to get recommendations. Instead to run forward the neural network for each prediction, our approach runs forward the neural network just once to get a set of probabilities in its categorical output layer. The proposed neural architecture has been tested by using the MovieLens and FilmTrust datasets. A state-of-the-art baseline that outperforms current competitive approaches has been used. Results show a competitive recommendation quality and an interesting quality improvement on large number of recommendations, consistent with the architecture design. The architecture originality makes it possible to address a broad range of future works. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T12:48:31Z No. of bitstreams: 1 ijimai20206_1_8_pdf_28842.pdf: 2786476 bytes, checksum: b9e4a8262fddfe10d36c1e971d60bd68 (MD5); Made available in DSpace on 2022-03-24T12:48:31Z (GMT). No. of bitstreams: 1 ijimai20206_1_8_pdf_28842.pdf: 2786476 bytes, checksum: b9e4a8262fddfe10d36c1e971d60bd68 (MD5)

Voltage Stability Assessment of Radial Distribution Systems Including Optimal Allocation of Distributed Generators

Voltage Stability Assessment of Radial Distribution Systems Including Optimal Allocation of Distributed Generators Selim, Ali; Kamel, Salah; Jurado, Francisco; Nasrat, Loai Assessment of power systems voltage stability is considered an important assignment for the operation and planning of power system. In this paper, a voltage stability study using Continuous Power Flow (CPF) is introduced to evaluate the impact of Distribution Generator (DG) on radial distribution systems. On the way to allocate the DG, a hybrid between the Voltage Stability Index (VSI) and Whale Optimization Algorithm (WOA) is developed. The main purpose of using VSI is to find the most sensitive buses for allocating the DG in the system. Hence, Fuzzy logic control with the Normalized VSI (NVSI) and the voltage magnitude at each bus are used to determine the candidate buses. However, the best DG size is calculated using WOA. Four standard radial distribution systems are used in this paper; 12, 33, 69, and 85-bus. The developed hybrid optimization method is compared with other existing analytical and metaheuristic optimization techniques to prove its efficiency. The results prove the ability of the developed method in the allocation of DG. In addition, the influence of the DG integration on enhancing the voltage stability through injecting the proper active and reactive powers is studied. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T11:11:45Z No. of bitstreams: 1 ijimai20206_1_4_pdf_13565.pdf: 1437028 bytes, checksum: 7532b8f58bfe4e38be53eab06767b5ca (MD5); Made available in DSpace on 2022-03-24T11:11:45Z (GMT). No. of bitstreams: 1 ijimai20206_1_4_pdf_13565.pdf: 1437028 bytes, checksum: 7532b8f58bfe4e38be53eab06767b5ca (MD5)

Editor's Note

Editor's Note Morente-Molinera, Juan Antonio Soft Computing is an AI branch that focuses on solving problems that have incomplete, inexact or fuzzy information. In other words, Soft Computing area includes algorithms and methods that are typically used when the imprecision or lack of the dealt data make other type of methods to become useless. Deep Learning, Machine learning and Fuzzy Systems related methods have achieved really good results even when the available data is not as good as desired. This success has converted the Soft Computing area in one of the most important ones inside the AI field. This special issue’s goal is to reunite some of the most recent research on the Soft Computing area. The selected research covers different aspects and problems on the AI area in an effort to provide a clear overview of the state of the art on the topic. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T10:37:50Z No. of bitstreams: 0; Made available in DSpace on 2022-03-24T10:37:50Z (GMT). No. of bitstreams: 0

Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak

Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak González-Crespo, Rubén; Herrera-Viedma, Enrique; Dey, Nilanjan; Fong, Simon James; Li, Gloria Epidemic is a rapid and wide spread of infectious disease threatening many lives and economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and remedic actions. These measures include closing borders, schools, suspending community services and commuters. Resuming such curfews depends on the momentum of the outbreak and its rate of decay. Being able to accurately forecast the fate of an epidemic is an extremely important but difficult task. Due to limited knowledge of the novel disease, the high uncertainty involved and the complex societal-political factors that influence the widespread of the new virus, any forecast is anything but reliable. Another factor is the insufficient amount of available data. Data samples are often scarce when an epidemic just started. With only few training samples on hand, finding a forecasting model which offers forecast at the best efforts is a big challenge in machine learning. In the past, three popular methods have been proposed, they include 1) augmenting the existing little data, 2) using a panel selection to pick the best forecasting model from several models, and 3) fine-tuning the parameters of an individual forecasting model for the highest possible accuracy. In this paper, a methodology that embraces these three virtues of data mining from a small dataset is proposed. An experiment that is based on the recent coronavirus outbreak originated from Wuhan is conducted by applying this methodology. It is shown that an optimized forecasting model that is constructed from a new algorithm, namely polynomial neural network with corrective feedback (PNN+cf) is able to make a forecast that has relatively the lowest prediction error. The results showcase that the newly proposed methodology and PNN+cf are useful in generating acceptable forecast upon the critical time of disease outbreak when the samples are far from abundant. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T10:21:50Z No. of bitstreams: 1 ijimai20206_1_15_pdf_18033.pdf: 1038427 bytes, checksum: 72331956a944f7572b62fd73c1db177b (MD5); Made available in DSpace on 2022-03-24T10:21:50Z (GMT). No. of bitstreams: 1 ijimai20206_1_15_pdf_18033.pdf: 1038427 bytes, checksum: 72331956a944f7572b62fd73c1db177b (MD5)

An Intelligent Technique for Grape Fanleaf Virus Detection

An Intelligent Technique for Grape Fanleaf Virus Detection Mohammadpoor, Mojtaba; Nooghabi, Mohadese Gerami; Ahmedi, Zahra Grapevine Fanleaf Virus (GFLV) is one of the most important viral diseases of grapes, which can damage up to 85% of the crop, if not treated at the right time. The aim of this study is to identify infected leaves with GFLV using artificial intelligent methods using an accessible database. To do this, some pictures are taken from infected and healthy leaves of grapes and labeled by technical specialists using conventional laboratory methods. In order to provide an intelligent method for distinguishing infected leaves from healthy ones, the area of unhealthy parts of each leaf is highlighted using Fuzzy C-mean Algorithm (FCM), and then the percentages of the first two segments area are fed to a Support Vector Machines (SVM). To increase the diagnostic reliability of the system, K-fold cross validation method with k = 3 and k =5 is applied. After applying the proposed method over all images using K-fold validation technique, average confusion matrix is extracted to show the True Positive, True Negative, False Positive and False Negative percentages of classification. The results show that specificity, as the ability of the algorithm to really detect healthy images, is 100%, and sensitivity, as the ability of the algorithm to correctly detect infected images is around 97.3%. The average accuracy of the system is around 98.6%. The results imply the ability of the proposed method compared to previous methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T10:15:16Z No. of bitstreams: 1 ijimai20206_1_7_pdf_85267.pdf: 775171 bytes, checksum: f558ff64eeb7c4e5b697209e58a9f338 (MD5); Made available in DSpace on 2022-03-24T10:15:16Z (GMT). No. of bitstreams: 1 ijimai20206_1_7_pdf_85267.pdf: 775171 bytes, checksum: f558ff64eeb7c4e5b697209e58a9f338 (MD5)

Comparative Study on Ant Colony Optimization (ACO) and K-Means Clustering Approaches for Jobs Scheduling and Energy Optimization Model in Internet of Things (IoT)

Comparative Study on Ant Colony Optimization (ACO) and K-Means Clustering Approaches for Jobs Scheduling and Energy Optimization Model in Internet of Things (IoT) Kumar, Sumit; Kumar-Solanki, Vijender; Kumar Choudhary, Saket; Selamat, Ali; González-Crespo, Rubén The concept of Internet of Things (IoT) was proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology (MIT) in 1999. IoT is an environment that people understand in many different ways depending on their requirement, point of view and purpose. When transmitting data in IoT environment, distribution of network traffic fluctuates frequently. If links of the network or nodes fail randomly, then automatically new nodes get added frequently. Heavy network traffic affects the response time of all system and it consumes more energy continuously. Minimization the network traffic/ by finding the shortest path from source to destination minimizes the response time of all system and also reduces the energy consumption cost. The ant colony optimization (ACO) and K-Means clustering algorithms characteristics conform to the auto-activator and optimistic response mechanism of the shortest route searching from source to destination. In this article, ACO and K-Means clustering algorithms are studied to search the shortest route path from source to destination by optimizing the Quality of Service (QoS) constraints. Resources are assumed in the active and varied IoT network atmosphere for these two algorithms. This work includes the study and comparison between ant colony optimization (ACO) and K-Means algorithms to plan a response time aware scheduling model for IoT. It is proposed to divide the IoT environment into various areas and a various number of clusters depending on the types of networks. It is noticed that this model is more efficient for the suggested routing algorithm in terms of response time, point-to-point delay, throughput and overhead of control bits. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T09:55:24Z No. of bitstreams: 1 ijimai20206_1_12_pdf_15248.pdf: 1138748 bytes, checksum: 3677e79d590223438312a4e59f5b9c40 (MD5); Made available in DSpace on 2022-03-24T09:55:24Z (GMT). No. of bitstreams: 1 ijimai20206_1_12_pdf_15248.pdf: 1138748 bytes, checksum: 3677e79d590223438312a4e59f5b9c40 (MD5)

A Multicriteria Optimization for Flight Route Networks in Large-Scale Airlines Using Intelligent Spatial Information

A Multicriteria Optimization for Flight Route Networks in Large-Scale Airlines Using Intelligent Spatial Information Borhani, Mostafa; Akbari, Kamal; Matkan, Aliakbar; Tanasan, Mohammad Air route network optimization, one of the airspace planning challenges, effectively manages airspace resources toward increasing airspace capacity and reducing air traffic congestion. In this paper, the structure of the flight network in air transport is analyzed with a multi-objective genetic algorithm regarding Geographic Information System (GIS) which is used to optimize this Iran airlines topology to reduce the number of airways and the aggregation of passengers in aviation industries organization and also to reduce changes in airways and the travel time for travelers. The proposed model of this study is based on the combination of two topologies – point-to-point and Hub-and-spoke – with multiple goals for causing a decrease in airways and travel length per passenger and also to reach the minimum number of air stops per passenger. The proposed Multi-objective Genetic Algorithm (MOGA) is tested and assessed in data of the Iran airlines industry in 2018, as an example to real-world applications, to design Iran airline topology. MOGA is proven to be effective in general to solve a network-wide flight trajectory planning. Using the combination of point-to-point and Hub-and-spoke topologies can improve the performance of the MOGA algorithm. Based on Iran airline traffic patterns in 2018, the proposed model successfully decreased 50.8% of air routes (184 air routes) compared to the current situations while the average travel length and the average changes in routes were increased up to 13.8% (about 100 kilometers) and up to 18%, respectively. The proposed algorithm also suggests that the current air routes of Iran can be decreased up to 24.7% (89 airways) if the travel length and the number of changes increase up to 4.5% (32 kilometers) and 5%, respectively. Two intermediate airports were supposed for these experiments. The computational results show the potential benefits of the proposed model and the advantage of the algorithm. The structure of the flight network in air transport can significantly reduce operational cost while ensuring the operation safety. According to the results, this intelligent multi-object optimization model would be able to be successfully used for a precise design and efficient optimization of existing and new airline topologies. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T09:07:19Z No. of bitstreams: 1 IJIMAI20206_1_14.pdf: 3851211 bytes, checksum: 94e85c4b574e6f1eb76e0df81d928791 (MD5); Made available in DSpace on 2022-03-24T09:07:19Z (GMT). No. of bitstreams: 1 IJIMAI20206_1_14.pdf: 3851211 bytes, checksum: 94e85c4b574e6f1eb76e0df81d928791 (MD5)

Multilayer Feedforward Neural Network for Internet Traffic Classification

Multilayer Feedforward Neural Network for Internet Traffic Classification Harish, B S; Nagadarshan, N; Manju, N Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows between the classes. In this paper, we propose a multilayer feedforward neural network architecture to handle the high imbalanced dataset. In the proposed model, we used a variation of multilayer perceptron with 4 hidden layers (called as mountain mirror networks) which does the feature transformation effectively. To check the efficacy of the proposed model, we used Cambridge dataset which consists of 248 features spread across 10 classes. Experimentation is carried out for two variants of the same dataset which is a standard one and a derived subset. The proposed model achieved an accuracy of 99.08% for highly imbalanced dataset (standard). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-21T11:33:54Z No. of bitstreams: 1 ijimai20206_1_13_pdf_16647.pdf: 369125 bytes, checksum: 5d56354cb826788e282d3f6cb037a7be (MD5); Made available in DSpace on 2022-03-21T11:33:54Z (GMT). No. of bitstreams: 1 ijimai20206_1_13_pdf_16647.pdf: 369125 bytes, checksum: 5d56354cb826788e282d3f6cb037a7be (MD5)

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection Hans, Rahul; Kaur, Harjot Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-21T11:10:28Z No. of bitstreams: 1 ijimai20206_1_11_pdf_31520.pdf: 937506 bytes, checksum: 4883488ef6b19fedd05372ccf2eecd10 (MD5); Made available in DSpace on 2022-03-21T11:10:28Z (GMT). No. of bitstreams: 1 ijimai20206_1_11_pdf_31520.pdf: 937506 bytes, checksum: 4883488ef6b19fedd05372ccf2eecd10 (MD5)

Mamdani Fuzzy Expert System Based Directional Relaying Approach for Six-Phase Transmission Line

Mamdani Fuzzy Expert System Based Directional Relaying Approach for Six-Phase Transmission Line Kumar, Naresh; Sanjay, Ch.; Chakravarthy, M Traditional directional relaying methods for 6-phase transmission lines have complex effort, and so there is still a need for novel direction relaying estimation scheme. This study presents a Mamdani-fuzzy expert system (MFES) approach for discriminating faulty section/zone, classifying faults and locating faults in 6-phase transmission lines. The 6-phase fundamental component of currents, voltages and phase angles are captured at single bus and are used in the protection scheme. Simulation results substantiate that the protection scheme is very successful against many parameters such as different fault types, fault resistances, transmission line fault locations and inception angles. A large number of fault case studies have been carried out to evaluate reach setting and % error of proposed method. It provides primary protection to transmission line length and also offers backup protection for a reverse section of transmission line. The experimental results show that the scheme performs better than the other schemes. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-21T10:44:28Z No. of bitstreams: 1 ijimai20206_1_5_pdf_10077.pdf: 957893 bytes, checksum: e61637c9f5939a170ec2f079a9975fba (MD5); Made available in DSpace on 2022-03-21T10:44:28Z (GMT). No. of bitstreams: 1 ijimai20206_1_5_pdf_10077.pdf: 957893 bytes, checksum: e61637c9f5939a170ec2f079a9975fba (MD5)

Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments

Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments Saleem, Nasir; Khattak, Muhammad Irfan In this paper, we considered the problem of the speech enhancement similar to the real-world environments where several complex noise sources simultaneously degrade the quality and intelligibility of a target speech. The existing literature on the speech enhancement principally focuses on the presence of one noise source in mixture signals. However, in real-world situations, we generally face and attempt to improve the quality and intelligibility of speech where various complex stationary and nonstationary noise sources are simultaneously mixed with the target speech. Here, we have used deep learning for speech enhancement in complex-noisy environments and used ideal binary mask (IBM) as a binary classification function by using deep neural networks (DNNs). IBM is used as a target function during training and the trained DNNs are used to estimate IBM during enhancement stage. The estimated target function is then applied to the complex-noisy mixtures to obtain the target speech. The mean square error (MSE) is used as an objective cost function at various epochs. The experimental results at different input signal-to-noise ratio (SNR) showed that DNN-based complex-noisy speech enhancement outperformed the competing methods in terms of speech quality by using perceptual evaluation of speech quality (PESQ), segmental signal-to-noise ratio (SNRSeg), log-likelihood ratio (LLR), weighted spectral slope (WSS). Moreover, short-time objective intelligibility (STOI) reinforced the better speech intelligibility. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-21T09:41:51Z No. of bitstreams: 1 ijimai20206_1_10_pdf_20073.pdf: 1358294 bytes, checksum: 34c71933dfa29a7278a5ce6308de80aa (MD5); Made available in DSpace on 2022-03-21T09:41:51Z (GMT). No. of bitstreams: 1 ijimai20206_1_10_pdf_20073.pdf: 1358294 bytes, checksum: 34c71933dfa29a7278a5ce6308de80aa (MD5)

A Convolution Neural Network Engine for Sclera Recognition

A Convolution Neural Network Engine for Sclera Recognition Harish, B S; Maheshan, M S; Nagadarshan, N The world is shifting to the digital era in an enormous pace. This rise in the digital technology has created plenty of applications in the digital space, which demands a secured environment for transacting and authenticating the genuineness of end users. Biometric systems and its applications has seen great potentials in its usability in the tech industries. Among various biometric traits, sclera trait is attracting researchers from experimenting and exploring its characteristics for recognition systems. This paper, which is first of its kind, explores the power of Convolution Neural Network (CNN) for sclera recognition by developing a neural model that trains its neural engine for a recognition system. To do so, the proposed work uses the standard benchmark dataset called Sclera Segmentation and Recognition Benchmarking Competition (SSRBC 2015) dataset, which comprises of 734 images which are captured at different viewing angles from 30 different classes. The proposed methodology results showcases the potential of neural learning towards sclera recognition system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-21T09:18:49Z No. of bitstreams: 1 ijimai20206_1_9_pdf_52009.pdf: 558901 bytes, checksum: 43647f9ab1756054fb1233e9b5648f2f (MD5); Made available in DSpace on 2022-03-21T09:18:49Z (GMT). No. of bitstreams: 1 ijimai20206_1_9_pdf_52009.pdf: 558901 bytes, checksum: 43647f9ab1756054fb1233e9b5648f2f (MD5)

Soft Computing Modelling of Urban Evolution: Tehran Metropolis

Soft Computing Modelling of Urban Evolution: Tehran Metropolis Borhani, Mostafa; Ghasemloo, Nima Exploring computational intelligence, geographic information systems and statistical information, a creative and innovative model for urban evolution is presented in this paper. The proposed model employs fuzzy logic and artificial neural network as forecasting tools for describing the urban growth. This dynamic urban evolution model considers the spatial data of population, as well as its time changes and the building usage patterns. For clustering the spatial features, fuzzy algorithms were implemented to represent different levels of urban growth and development. Then, these fuzzy clusters were modeled by the multi-layer neural networks to estimate the urban growth. Based on this novel intelligent model, the current state of development of Tehran’s population and the future of this urban evolution were evaluated by empirical data and the achieved outcomes were detailed in qualitative charts. The input data-set includes four censuses with five-year intervals. Tehran's demographic evolution model forecasts the next five years with an overall accuracy of 81% and Cohen's kappa coefficient up to 74% beside the qualitative charts. These performance indicators are higher than the previous advanced models. The primary objective of this proposed model is to aid planners and decision makers to predict the development trend of urban population. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-18T13:13:49Z No. of bitstreams: 1 ijimai20206_1_1_pdf_16471.pdf: 757262 bytes, checksum: 8ecbe1ebc84543f5409a143f7c009930 (MD5); Made available in DSpace on 2022-03-18T13:13:49Z (GMT). No. of bitstreams: 1 ijimai20206_1_1_pdf_16471.pdf: 757262 bytes, checksum: 8ecbe1ebc84543f5409a143f7c009930 (MD5)

Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network Harish, B S; Roopa, C K Cardio-vascular diseases are one of the foremost causes of mortality in today’s world. The prognosis for cardiovascular diseases is usually done by ECG signal, which is a simple 12-lead Electrocardiogram (ECG) that gives complete information about the function of the heart including the amplitude and time interval of P-QRST-U segment. This article recommends a novel approach to identify the location of thrombus in culprit artery using the Information Fuzzy Network (IFN). Information Fuzzy Network, being a supervised machine learning technique, takes known evidences based on rules to create a predicted classification model with thrombus location obtained from the vast input ECG data. These rules are well-defined procedures for selecting hypothesis that best fits a set of observations. Results illustrate that the recommended approach yields an accurateness of 92.30%. This novel approach is shown to be a viable ECG analysis approach for identifying the culprit artery and thus localizing the thrombus. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-18T13:07:26Z No. of bitstreams: 1 ijimai20206_1_2_pdf_13999.pdf: 771524 bytes, checksum: 891c8db38b0693dd2735f81ba123140f (MD5); Made available in DSpace on 2022-03-18T13:07:26Z (GMT). No. of bitstreams: 1 ijimai20206_1_2_pdf_13999.pdf: 771524 bytes, checksum: 891c8db38b0693dd2735f81ba123140f (MD5)

Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education

Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education Gaona-García, Paulo Alonso; Montenegro-Marin, Carlos Enrique; Sarría, Íñigo; Restrepo Rodríguez, Andrés Ovidio; Ariza Riaño, Maddyzeth Fine motor skills allow to carry out the execution of crucial tasks in people's daily lives, increasing their independence and self-esteem. Among the alternatives for working these skills, immersive environments are found providing a set of elements arranged to have a haptic experience through gestural control devices. However, generally, these environments do not have a mechanism for evaluation and feedback of the exercise performed, which does not easily identify the objective's fulfillment. For this reason, this study aims to carry out a comparison of image recognition methods such as Convolutional Neural Network (CNN), K-Nearest Neighbor (K-NN), Support Vector Machine (SVM) and Decision Tree (DT), for the purpose of performing an evaluation and feedback of exercises. The assessment of the techniques is carried out using images captured from an immersive environment, calculating metrics such as confusion matrix, cross validation and classification report. As a result of this process, it was obtained that the CNN model has a better supported performance in 82.5% accuracy, showing an increase of 23.5% compared to SVM, 30% compared to K-NN and 25% compared to DT. Finally, it is concluded that in order to implement a method of evaluation and feedback in an immersive environment for academic training in the first school years, a low margin of error must be taken in the percentage of successes of the image recognition technique implemented, to ensure the proper development of these skills considering their great importance in childhood. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T12:35:13Z No. of bitstreams: 1 ijimai20195_7_15_pdf_81774.pdf: 1395952 bytes, checksum: 737430441ba8cd3fad81fd43e759b5ae (MD5); Made available in DSpace on 2022-03-17T12:35:13Z (GMT). No. of bitstreams: 1 ijimai20195_7_15_pdf_81774.pdf: 1395952 bytes, checksum: 737430441ba8cd3fad81fd43e759b5ae (MD5)

Voltage Stability Enhancement Based on Optimal Allocation of Shunt Compensation Devices Using Lightning attachment procedure optimization

Voltage Stability Enhancement Based on Optimal Allocation of Shunt Compensation Devices Using Lightning attachment procedure optimization Kamel, Salah; Youssef, Heba This paper proposes a combined approach to determine the optimal allocation of different shunt compensation devices (shunt capacitor, static var compensator, and static synchronous compensator) in power systems. The developed approach is a combination between Lightning Attachment Procedure Optimization (LAPO) and loss sensitivity indices (LSIs). Different objective functions such as enhancement of voltage stability index, improvement of voltage profile and minimization of total power losses are considered. Two loss sensitivity indices (LSIs) are developed to reduce the search space in all buses and the total computation time. The developed algorithm is validated using standard IEEE 14-bus and IEEE 30-bus test systems. The developed algorithm successes to achieve the objective functions with the better performance compared with other wellknown optimization techniques such as Teaching learning-based optimization (TLBO), genetic algorithm (GA) and particle swarm optimization (PSO). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T12:15:19Z No. of bitstreams: 1 ijimai20195_7_13_pdf_13640.pdf: 2038527 bytes, checksum: 1aa38449cb5373ae44a1b27163519f2c (MD5); Made available in DSpace on 2022-03-17T12:15:19Z (GMT). No. of bitstreams: 1 ijimai20195_7_13_pdf_13640.pdf: 2038527 bytes, checksum: 1aa38449cb5373ae44a1b27163519f2c (MD5)

Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems

Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems Mohamed, Emad; Mohamed, Al-Attar Ali; Mitani, Yasunori This paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the electrical power loss within the framework of system operation and under security constraints is the main objective of this study. In the proposed technique, the global search ability has been regulated by the incorporation of GA operations with adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in terms of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. The developed GMSA has been applied on different scales of standard RDN of the (33 and 69-bus) power systems. Firstly, the most adequate buses for installing DGs are suggested using Voltage Stability Index (VSI). Then the proposed GMSA is applied to reduce real power generation, power loss, and total system cost, in addition, to improve the minimum bus voltage and the annual net saving by selecting the DGs size and their locations. Furthermore, GMSA is compared with other literature methods under several power system constraints and conditions, in single and multi-objective optimization space. The computational results prove the effectiveness and superiority of the GMSA with respect to power loss reduction and voltage profile enhancement using a minimum size of renewable DG units. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T11:43:19Z No. of bitstreams: 1 ijimai20195_7_11_pdf_41934.pdf: 2511912 bytes, checksum: 4e25f277a837c0beccc0363d053f0aa6 (MD5); Made available in DSpace on 2022-03-17T11:43:19Z (GMT). No. of bitstreams: 1 ijimai20195_7_11_pdf_41934.pdf: 2511912 bytes, checksum: 4e25f277a837c0beccc0363d053f0aa6 (MD5)

SOLAM: A Novel Approach of Spatial Aggregation in SOLAP Systems

SOLAM: A Novel Approach of Spatial Aggregation in SOLAP Systems Djamila, Hamdadou; Zemri, Farah Amina In the context of a data driven approach aimed to detect the real and responsible factors of the transmission of diseases and explaining its emergence or re-emergence, we suggest SOLAM (Spatial on Line Analytical Mining) system, an extension of Spatial On Line Analytical Processing (SOLAP) with Spatial Data Mining (SDM) techniques. Our approach consists of integrating EPISOLAP system, tailored for epidemiological surveillance, with spatial generalization method allowing the predictive evaluation of health risk in the presence of hazards and awareness of the vulnerability of the exposed population. The proposed architecture is a single integrated decision-making platform of knowledge discovery from spatial databases. Spatial generalization methods allow exploring the data at different semantic and spatial scales while reducing the unnecessary dimensions. The principle of the method is selecting and deleting attributes of low importance in data characterization, thus produces zones of homogeneous characteristics that will be merged. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T10:54:44Z No. of bitstreams: 1 ijimai20195_7_10_pdf_39275.pdf: 924775 bytes, checksum: ddbd11984bcd529d622c6ead4fdb90cd (MD5); Made available in DSpace on 2022-03-17T10:54:44Z (GMT). No. of bitstreams: 1 ijimai20195_7_10_pdf_39275.pdf: 924775 bytes, checksum: ddbd11984bcd529d622c6ead4fdb90cd (MD5)

"Hello, is There Anybody Who Reads Me?" Radio Programs and Popular Facebook Posts

"Hello, is There Anybody Who Reads Me?" Radio Programs and Popular Facebook Posts Laor, Tal Radio stations are increasingly active on social networks, as radio continues to adjust and adapt to online spaces. This research is intended to conceptualize and characterize the success of radio programs beyond their native FM environment, focusing on their attempts at achieving popularity on social networks. Success on social networks is measured by user involvement and interaction with posted content and comments. This study looked at the activity of leading Israeli radio programs on Facebook pages and user engagement, evaluating highly involved posts by coding. It was found that radio program activity on social networks expands the reach of radio stations and promotes higher levels of interaction with listeners beyond broadcast schedule. In addition, integration of various media forms such as videos or images increases the likelihood of a post becoming popular. This research presents the convergence of radio programs in accordance with the theoretical framework of technological determinism. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T10:50:43Z No. of bitstreams: 1 ijimai20195_7_8_pdf_91929.pdf: 331269 bytes, checksum: e530b48a2143243f9d097b44732b2ea7 (MD5); Made available in DSpace on 2022-03-17T10:50:43Z (GMT). No. of bitstreams: 1 ijimai20195_7_8_pdf_91929.pdf: 331269 bytes, checksum: e530b48a2143243f9d097b44732b2ea7 (MD5)

Editor’s Note

Editor’s Note González-Crespo, Rubén; Verdú, Elena The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. After its recent tenth anniversary, the journal has achieved an important milestone. From 2015 to 2018 IJIMAI was indexed at Web of Science through Emerging Science Citation Index. This meant a great increase in visibility and number of received papers. This year, Clarivate Analytics has accepted the inclusion of IJIMAI in the Journal Citation Reports. Specifically, IJIMAI is being indexed and abstracted in Science Citation Index Expanded, Journal Citation Reports/Science Edition and Current Contents®/Engineering Computing and Technology. The Web of Science Categories in which IJIMAI is included are “Computer Science, Artificial Intelligence” and “Computer Science, Interdisciplinary Applications”. This way, IJIMAI is indexed in Science Citation Index Expanded beginning with vol.4(3) March 2017 so that the journal will be listed in the 2019 Journal Citation Reports with a Journal Impact Factor when released in June 2020. Given this great achievement, IJIMAI Editorial Board has to thank authors for all the papers sent and all the papers published, as well as reviewers for their support to obtain high-quality in papers, and specially our readers because without them this milestone would not have been possible. The present regular issue includes research works based on different AI methods such as convolutional neural networks, genetic algorithms, lightning attachment procedure optimization, or those of multi-agent systems. These methods are applied into various fields as video surveillance, gesture recognition, sentiment analysis, territory planning, search engines, epidemiological surveillance or robotics. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T10:46:28Z No. of bitstreams: 1 ijimai20195_7_0_pdf_15825.pdf: 109872 bytes, checksum: 30d22789583811a00c389e334fd851c4 (MD5); Made available in DSpace on 2022-03-17T10:46:28Z (GMT). No. of bitstreams: 1 ijimai20195_7_0_pdf_15825.pdf: 109872 bytes, checksum: 30d22789583811a00c389e334fd851c4 (MD5)

Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks

Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks González-Crespo, Rubén; Verdú, Elena; Khari, Manju; Garg, Aditya Kumar In this era, the interaction between Human and Computers has always been a fascinating field. With the rapid development in the field of Computer Vision, gesture based recognition systems have always been an interesting and diverse topic. Though recognizing human gestures in the form of sign language is a very complex and challenging task. Recently various traditional methods were used for performing sign language recognition but achieving high accuracy is still a challenging task. This paper proposes a RGB and RGB-D static gesture recognition method by using a fine-tuned VGG19 model. The fine-tuned VGG19 model uses a feature concatenate layer of RGB and RGB-D images for increasing the accuracy of the neural network. Finally, on an American Sign Language (ASL) Recognition dataset, the authors implemented the proposed model. The authors achieved 94.8% recognition rate and compared the model with other CNN and traditional algorithms on the same dataset. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T10:41:21Z No. of bitstreams: 1 ijimai20195_7_2_pdf_18405.pdf: 804428 bytes, checksum: 8fbd87fdbc446269ca004823bbe73efe (MD5); Made available in DSpace on 2022-03-17T10:41:21Z (GMT). No. of bitstreams: 1 ijimai20195_7_2_pdf_18405.pdf: 804428 bytes, checksum: 8fbd87fdbc446269ca004823bbe73efe (MD5)

Social Seducement: Empowering Social Economy Entrepreneurship. The Training Approach

Social Seducement: Empowering Social Economy Entrepreneurship. The Training Approach Burgos, Daniel; Padilla-Zea, Natalia; Aceto, Stefania Long-term unemployment is a persistent problem in Europe, following the economic crisis suffered in 2008. This situation reveals self-employment as a good option for becoming re-involved in the working life. In this context, this paper presents a gamified educational platform to empower social economy entrepreneurship skills in long-term unemployed people. In particular, we present the training approach underpinning the motivational process supported by gamification, which has been developed using the ADDIE model. The learning path is developed according to a story that guides the work throughout the training process. It is based on the premises of alignment with reality, instruction from didactic material and real-life stories, in-game practice, work in groups and assistance from a facilitator. This approach covers the competence needs identified in a previous study and includes gamification techniques to improve motivation and engagement. Therefore, the training program comprises: (1) a set of materials and real social economy enterprise experiences, which are the basis for learning and getting inspiration; (2) activities to develop a business plan based on concepts learned from the learning materials and from real-life stories, as well as the help of a facilitator who walks with trainees during the process; (3) a set of individual and group, mandatory and optional assessment activities to evaluate the learning achieved; and (4) a three-views scoring system that shows learning progress for individuals and groups, and gives players the opportunity to exchange gamification points for benefits in the game. The results presented in this paper are based on a sample of two pilots run in Italy and Spain and involving five facilitators working with around 60 learners. About 60% of participants indicated their intention to apply knowledge obtained in a real-life entrepreneurship initiative. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T10:00:18Z No. of bitstreams: 1 ijimai20195_7_14_pdf_17956.pdf: 1146968 bytes, checksum: f84cdd111f796e007016a5385ab48743 (MD5); Made available in DSpace on 2022-03-17T10:00:18Z (GMT). No. of bitstreams: 1 ijimai20195_7_14_pdf_17956.pdf: 1146968 bytes, checksum: f84cdd111f796e007016a5385ab48743 (MD5)

Genetic Operators Applied to Symmetric Cryptography

Genetic Operators Applied to Symmetric Cryptography Rodríguez, Jefferson; Corredor, Brayan; Suárez, César In this article, a symmetric-key cryptographic algorithm for text is proposed, which applies Genetic Algorithms philosophy, entropy and modular arithmetic. An experimental methodology is used over a deterministic system, which redistributes and modifies the parameters and phases of the genetic algorithm that directly affect its behavior, carrying out a constant evaluation using the fitness function, in order to optimize the results. An independent encryption is established for the auxiliary key, using a main key, in charge of increasing security. The tests are performed over different text sizes, manipulating the parameters and criteria proposed to obtain their appropriate values. Finally, a comparison is presented against the following cryptographic algorithms DES (Data Encryption Standard), RSA (Rivest, Shamir and Adleman) and AES (Advanced Encryption Standard), exposing factors such as processing time, scalability, key size, etc. It is shown that the proposed algorithm has a better performance. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T09:32:46Z No. of bitstreams: 1 ijimai20195_7_4_pdf_53926.pdf: 976073 bytes, checksum: 3b679d987a5f013782acb8188f2ae9e9 (MD5); Made available in DSpace on 2022-03-17T09:32:46Z (GMT). No. of bitstreams: 1 ijimai20195_7_4_pdf_53926.pdf: 976073 bytes, checksum: 3b679d987a5f013782acb8188f2ae9e9 (MD5)

Automatic Irony Detection using Feature Fusion and Ensemble Classifier

Automatic Irony Detection using Feature Fusion and Ensemble Classifier Harish, B S; Kumar, Keerthi With the advent of micro-blogging sites, users are pioneer in expressing their sentiments and emotions on global issues through text. Automatic detection and classification of sentiments like sarcastic or ironic content in microblogging reviews is a challenging task. It requires a system that manages some kind of knowledge to interpret the sentiment expressed in text. The available approaches are quite limited in their capabilities and scope to detect ironic utterances present in the text. In this regards, the paper propose feature fusion to provide knowledge to the system by alternative sets of features obtained using linguistic and content based text features. The proposed work extracts five sets of linguistic features and fuses with features selected using two stages of a feature selection method. In order to demonstrate the effectiveness of the proposed method, we conduct extensive experimentation by selecting different feature subsets. The performances of the proposed method are evaluated using Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and ensemble classifiers. The experimental result shows the proposed approach significantly out-performs the conventional methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T09:20:48Z No. of bitstreams: 1 ijimai20195_7_7_pdf_17438.pdf: 709467 bytes, checksum: 981c45b73fefee6cba736a86cc0cb17a (MD5); Made available in DSpace on 2022-03-17T09:20:48Z (GMT). No. of bitstreams: 1 ijimai20195_7_7_pdf_17438.pdf: 709467 bytes, checksum: 981c45b73fefee6cba736a86cc0cb17a (MD5)

Design and Validation of a Framework for the Creation of User Experience Questionnaires

Design and Validation of a Framework for the Creation of User Experience Questionnaires Schrepp, Martin; Thomaschewski, Jörg Existing user experience questionnaires have a fixed number of scales. Each of these scales measures a distinct aspect of user experience. These questionnaires can be used with little effort and provide a number of useful support materials that make the application of such a questionnaire quite easy. However, in practical evaluation scenarios it can happen that none of the existing questionnaires contains all scales necessary to answer the research question. It is of course possible to combine several UX questionnaires in such cases, but due to the variations of item formats this is also not an optimal solution. In this paper, we describe the development and first validation studies of a modular framework that allows the creation of user experience questionnaires that fit perfectly to a given research question. The framework contains several scales that measure different UX aspects. These scales can be combined to cover the relevant research questions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T08:59:38Z No. of bitstreams: 1 ijimai20195_7_9_pdf_19082.pdf: 524794 bytes, checksum: c85ff8af2e38703e807aacd0d00a4f60 (MD5); Made available in DSpace on 2022-03-17T08:59:38Z (GMT). No. of bitstreams: 1 ijimai20195_7_9_pdf_19082.pdf: 524794 bytes, checksum: c85ff8af2e38703e807aacd0d00a4f60 (MD5)

Performance Enhancement of Wind Farms Using Tuned SSSC Based on Artificial Neural Network

Performance Enhancement of Wind Farms Using Tuned SSSC Based on Artificial Neural Network Kamel, Salah; Jurado, Francisco; Rashad, Ahmed; Ibrahim, Yousry; Nasrat, Loai Recently, power systems are confronting a lot of challenges. Increasing the dependence on renewable energy sources especially wind energy and its impact on the stability of electrical systems are the most important challenges. Flexible alternating current transmission systems (FACTS) can be used to improve the relationship between wind farms and electrical grids. The performance of these FACTS depends on the parameters of its control system. These parameters can be tuned using modern methods like Artificial Neural Network (ANN). In this paper, ANN is used to improve the performance of static synchronous series compensator (SSSC) integrated into combined wind farm (CWF). This CWF is composed of squirrel cage induction generators (SCIG) and doubly fed induction generators (DFIG) wind turbines. This wind farm is collecting the advantage of SCIG and DFIG wind turbines. To view out the motivation of this paper, a comparison is done among the performances of combined wind farm (CWF) with ANN-SSSC, CWF with ordinary SSSC and CWF with SSSC tune by Multi-objective genetic algorithm (MOGA SSSC). The root mean square Error (RMSE) is used to evaluate the results. The results illustrate that the performance of CWF can be improved using SSSC adjusted by ANN. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T08:46:28Z No. of bitstreams: 1 ijimai20195_7_12_pdf_71211.pdf: 1459034 bytes, checksum: 53163579f1b2f2aa8d98ec065c66b161 (MD5); Made available in DSpace on 2022-03-17T08:46:28Z (GMT). No. of bitstreams: 1 ijimai20195_7_12_pdf_71211.pdf: 1459034 bytes, checksum: 53163579f1b2f2aa8d98ec065c66b161 (MD5)

MWAND: A New Early Termination Algorithm for Fast and Efficient Query Evaluation

MWAND: A New Early Termination Algorithm for Fast and Efficient Query Evaluation Mansouria, Zemani Imene; Lougmiri, Zekri; Mohamed, Senouci Nowadays, current information systems are so large and maintain huge amount of data. At every time, they process millions of documents and millions of queries. In order to choose the most important responses from this amount of data, it is well to apply what is so called early termination algorithms. These ones attempt to extract the Top-K documents according to a specified increasing monotone function. The principal idea behind is to reach and score the most significant less number of documents. So, they avoid fully processing the whole documents. WAND algorithm is at the state of the art in this area. Despite it is efficient, it is missing effectiveness and precision. In this paper, we propose two contributions, the principal proposal is a new early termination algorithm based on WAND approach, we call it MWAND (Modified WAND). This one is faster and more precise than the first. It has the ability to avoid unnecessary WAND steps. In this work, we integrate a tree structure as an index into WAND and we add new levels in query processing. In the second contribution, we define new fine metrics to ameliorate the evaluation of the retrieved information. The experimental results on real datasets show that MWAND is more efficient than the WAND approach. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T08:39:49Z No. of bitstreams: 1 ijimai20195_7_6_pdf_83440.pdf: 2146533 bytes, checksum: a76d69fb4a8d38700081a88613da8f40 (MD5); Made available in DSpace on 2022-03-17T08:39:49Z (GMT). No. of bitstreams: 1 ijimai20195_7_6_pdf_83440.pdf: 2146533 bytes, checksum: a76d69fb4a8d38700081a88613da8f40 (MD5)

Humanoid Localization on Robocup Field using Corner Intersection and Geometric Distance Estimation

Humanoid Localization on Robocup Field using Corner Intersection and Geometric Distance Estimation Sudin, M N; Abdullah, Sheikh; Nasudin, M F In the humanoid competition field, identifying landmarks for localizing robots in a dynamic environment is of crucial importance. By convention, state-of-the-art humanoid vision systems rely on poles located outside the middle of the field as an indicator for generating landmarks. However, in compliance with the recent rules of Robocup, the middle pole has been discarded to deliberately provide less prior information for the humanoid vision system to strategize its winning tactics on the field. Previous localization method used middle poles as a landmark. Therefore, robot localization tasks should apply accurate corner and distance detection simultaneously to locate the positions of goalposts. State-of-the-art corner detection algorithms such as the Harris corner and mean projection transformation are excessively sensitive to image noise and suffer from high processing times. Moreover, despite their prevalence in robot motor log and fish-eye lens calibration for humanoid localization, current distance estimation techniques nonetheless remain highly dependent on multiple poles as vision landmarks, apart from being prone to huge localization errors. Thus, we propose a novel localization method consisting of a proposed corner extraction algorithm, namely, the contour intersection algorithm (CIA), and a distance estimation algorithm, namely, analytic geometric estimation (AGE), for efficiently identifying salient goalposts. At first, the proposed CIA algorithm, which is based on linear contour intersection using a projection matrix, is utilized to extract corners of a goalpost after performing an adaptive binarization process. Then, these extracted corner features are fed into our proposed AGE algorithm to estimate the real-word distance using analytic geometry methods. As a result, the proposed localization vision system and the state-of-the-art method obtained approximately 3-4 and 7-23 centimeter estimation errors, respectively. This demonstrates the capability of the proposed localization algorithm to outperform other methods, which renders it more effective in indoor task localization for further actions such as attack or defense strategies. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T08:24:45Z No. of bitstreams: 1 ijimai20195_7_5_pdf_15069.pdf: 1680591 bytes, checksum: 7a57d4336eeb3eb7d67e1435c36a3b83 (MD5); Made available in DSpace on 2022-03-17T08:24:45Z (GMT). No. of bitstreams: 1 ijimai20195_7_5_pdf_15069.pdf: 1680591 bytes, checksum: 7a57d4336eeb3eb7d67e1435c36a3b83 (MD5)

A Temporal Distributed Group Decision Support System Based on Multi-Criteria Analysis

A Temporal Distributed Group Decision Support System Based on Multi-Criteria Analysis Djamila, Hamdadou; Khiat, Sofiane Decision support consists of proposing tasks and projects by taking into account temporal constraints and the use of resources with the aim of finding a compromise solution between several alternatives. Indeed, on the one hand, centralized resolution systems and methods are generally inappropriate to the real case because of the local unavailability of decision makers. On the other hand, the data of the decisional problem are generally poorly expressed in a negotiation environment. Other techniques and approaches treat the same decision-making problem and impose a distributed vision for coherent decisions. For this purpose, Multi-Agent Systems (MAS) allow modeling a distributed resolution of the group decision support problem. In this article, we propose a new model of a multi-criteria group decision support system based on a multi-agent system modeling a spatial problem. We consider that each decision maker is assimilated to an agent that has a decision-making autonomy, in which he interacts with other agents in the debate through a negotiation process in order to reach an acceptable compromise. In this study, we propose coordination mechanisms among agents to highlight the simulated negotiation. Therefore, the proposed system finds a solution before fixed deadlines’ time expire. We experiment the suggested negotiation model to solve the decisional problem of spatial localization in territory planning. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T08:14:26Z No. of bitstreams: 1 ijimai20195_7_1_pdf_15237.pdf: 2047630 bytes, checksum: 9c0e0b3f38ca199198c6be541319464f (MD5); Made available in DSpace on 2022-03-17T08:14:26Z (GMT). No. of bitstreams: 1 ijimai20195_7_1_pdf_15237.pdf: 2047630 bytes, checksum: 9c0e0b3f38ca199198c6be541319464f (MD5)

A Low Cost and Computationally Efficient Approach for Occlusion Handling in Video Surveillance Systems

A Low Cost and Computationally Efficient Approach for Occlusion Handling in Video Surveillance Systems Joshi, Rakesh Chandra; Singh, Adithya Gaurav; Joshi, Mayank; Mathur, Sanjay In the development of intelligent video surveillance systems for tracking a vehicle, occlusions are one of the major challenges. It becomes difficult to retain features during occlusion especially in case of complete occlusion. In this paper, a target vehicle tracking algorithm for Smart Video Surveillance (SVS) is proposed to track an unidentified target vehicle even in case of occlusions. This paper proposes a computationally efficient approach for handling occlusions named as Kalman Filter Assisted Occlusion Handling (KFAOH) technique. The algorithm works through two periods namely tracking period when no occlusion is seen and detection period when occlusion occurs, thus depicting its hybrid nature. Kanade-Lucas-Tomasi (KLT) feature tracker governs the operation of algorithm during the tracking period, whereas, a Cascaded Object Detector (COD) of weak classifiers, specially trained on a large database of cars governs the operation during detection period or occlusion with the assistance of Kalman Filter (KF). The algorithm’s tracking efficiency has been tested on six different tracking scenarios with increasing complexity in real-time. Performance evaluation under different noise variances and illumination levels shows that the tracking algorithm has good robustness against high noise and low illumination. All tests have been conducted on the MATLAB platform. The validity and practicality of the algorithm are also verified by success plots and precision plots for the test cases. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-17T08:00:52Z No. of bitstreams: 1 ijimai20195_7_3_pdf_11888.pdf: 5039071 bytes, checksum: d35310ded982d480ace8aa56fdae377c (MD5); Made available in DSpace on 2022-03-17T08:00:53Z (GMT). No. of bitstreams: 1 ijimai20195_7_3_pdf_11888.pdf: 5039071 bytes, checksum: d35310ded982d480ace8aa56fdae377c (MD5)

Editor’s Note. Towards an Intelligent Society: Advances in Marketing and Neuroscience

Editor’s Note. Towards an Intelligent Society: Advances in Marketing and Neuroscience Mochón, Francisco; Baldominos Gómez, Alejandro This Special Issue focuses in cases that explore the relationship between Artificial Intelligence and marketing, as well as neuroscience. AI can be combined with specific neuroscience techniques to achieve a more successful and profitable neuromarketing. For this Special Issue, we have found that descriptions of successful use cases are highly valuable to help researchers identify fields where novel applications of AI can enhance the outcome of digital marketing and neuroscience. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T10:28:31Z No. of bitstreams: 1 ijimai20195_6_0_pdf_19590.pdf: 101699 bytes, checksum: 61e7a6f67a50657da0e326306647f5b8 (MD5); Made available in DSpace on 2022-03-15T10:28:31Z (GMT). No. of bitstreams: 1 ijimai20195_6_0_pdf_19590.pdf: 101699 bytes, checksum: 61e7a6f67a50657da0e326306647f5b8 (MD5)

MOBEEZE. Natural Interaction Technologies, Virtual Reality and Artificial Intelligence for Gait Disorders Analysis and Rehabilitation in Patients with Parkinson's Disease

MOBEEZE. Natural Interaction Technologies, Virtual Reality and Artificial Intelligence for Gait Disorders Analysis and Rehabilitation in Patients with Parkinson's Disease Lombardo, Juan Manuel; Lopez, Miguel Angel; Miron, Felipe; López, Mabel; León, Mónica; Arambarri, Jon; Álvarez, David Parkinson's Disease (PD) is the most common degenerative disorder after Alzheimer's disease. Generally affecting elderly groups, it has a strong limiting effect on physical functioning and performance of roles, vitality and general perception of health. Since the disease is progressive, the patient knows he's going to get worse. The deterioration is significant not only in mobility but also in pain, social isolation, and emotional reactions. Freezing is a phenomenon associated with this disease and it is characterized by a motor disorder that leaves the patient literally stuck to the ground. Mobeeze is designed with the main objective of providing health personnel with a tool to analyse, evaluate and monitor the progress of patients’ disorders as well as the personalization and adaptation of rehabilitation sessions in patients with Parkinson's disease. Based on the characteristics measured in real time which will allow the strengthening effects of rehabilitation and help to assimilate them in the long term. The creation of Mobeeze allows the constitution of a system of analysis and evaluation of march disorders in real time, through natural interaction, virtual reality and artificial intelligence. In this project, we will analyse if these non-invasive technologies reduce the stress induced to the patient when he is feeling evaluated. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T10:17:16Z No. of bitstreams: 1 ijimai20195_6_7_pdf_12627.pdf: 371762 bytes, checksum: 29b65e04186ac4647b90cbaad23b59f5 (MD5); Made available in DSpace on 2022-03-15T10:17:16Z (GMT). No. of bitstreams: 1 ijimai20195_6_7_pdf_12627.pdf: 371762 bytes, checksum: 29b65e04186ac4647b90cbaad23b59f5 (MD5)

The Promotion of Graduate Programs through Clustering Prospective Students

The Promotion of Graduate Programs through Clustering Prospective Students Cantón Croda, Rosa María; Gibaja Romero, Damián Emilio; Castillo-Villar, Fernando-Rey The promotion of academic programs, particularly at graduate levels, emerges as a response to market changes. In general, graduate programs are not a first order necessity which makes necessary the right promotion of such programs guarantee the attraction of prospective students, which enroll in some of them, which is essential for the financial sustainability of universities. Notably, the last one is a crucial problem for private universities. In this paper, we analyze the prospective students that enroll in a private to design better promotion strategies by using on data gathered by online sources. Specifically, we use clustering techniques to define marketing strategies based on segments of students. We find that age and city are crucial to promoting graduate programs while marital status and sex does not impact the decision of students in the university that we analyze. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T09:49:53Z No. of bitstreams: 1 ijimai20195_6_3_pdf_12770.pdf: 517809 bytes, checksum: de199a529087534d70e3bdc39990ce1a (MD5); Made available in DSpace on 2022-03-15T09:49:53Z (GMT). No. of bitstreams: 1 ijimai20195_6_3_pdf_12770.pdf: 517809 bytes, checksum: de199a529087534d70e3bdc39990ce1a (MD5)

Model for Prediction of Progression in Multiple Sclerosis

Model for Prediction of Progression in Multiple Sclerosis Pruenza, Cristina; Díaz, Julia; Solano, María Teresa; Arroyo, Rafael; Izquierdo, Guillermo Multiple sclerosis is an idiopathic inflammatory disease of the central nervous system and the second most common cause of disability in young adults. Choosing an effective treatment is crucial to preventing disability. However, response to treatment varies greatly between patients. Because of this, accurate and timely detection of individual response to treatment is an essential requisite of efficient personalised multiple sclerosis therapy. Nowadays, there is a lack of comprehensive predictive models of response to individual treatment.This paper arises from the clinical need to improve this situation. To achieve it, all patient's information was used to evaluate the effectiveness of demographic, clinical and paraclinical variables of individual response to fourteen disease-modifying therapies in MSBase, an international cohort. A personalized prediction model to three stages of disease, as a support tool in clinical decision making for each MS patient, was developed applying machine learning and Big Data techniques. These techniques were also used to reduce the data set and define a minimum set of characteristics for each patient. Best predictors for the response to treatment were identified to refine the predictive model. Fourteen relevant variables were selected. A web application was implemented to be used to support the specialist neurologist in real time. This tool provides a prediction of progression in EDSS from the last relapse of an individual patient, and a report for the medical expert. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T09:36:47Z No. of bitstreams: 1 ijimai20195_6_6_pdf_58400.pdf: 506098 bytes, checksum: 54cb90b44ee937d966e2bd9c2cf8b3e2 (MD5); Made available in DSpace on 2022-03-15T09:36:47Z (GMT). No. of bitstreams: 1 ijimai20195_6_6_pdf_58400.pdf: 506098 bytes, checksum: 54cb90b44ee937d966e2bd9c2cf8b3e2 (MD5)

Data and Artificial Intelligence Strategy: A Conceptual Enterprise Big Data Cloud Architecture to Enable Market-Oriented Organisations

Data and Artificial Intelligence Strategy: A Conceptual Enterprise Big Data Cloud Architecture to Enable Market-Oriented Organisations Herrera-Viedma, Enrique; Carrasco, Ramón Alberto; Moreno, Caio Market-Oriented companies are committed to understanding both the needs of their customers, and the capabilities and plans of their competitors through the processes of acquiring and evaluating market information in a systematic and anticipatory manner. On the other hand, most companies in the last years have defined that one of their main strategic objectives for the next years is to become a truly data-driven organisation in the current Big Data context. They are willing to invest heavily in Data and Artificial Intelligence Strategy and build enterprise data platforms that will enable this Market-Oriented vision. In this paper, it is presented an Artificial Intelligence Cloud Architecture capable to help global companies to move from the use of data from descriptive to prescriptive and leveraging existing cloud services to deliver true Market-Oriented in a much shorter time (compared with traditional approaches). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T09:23:29Z No. of bitstreams: 1 ijimai20195_6_1_pdf_17622.pdf: 1061408 bytes, checksum: ba43ba351ab50685925b88f828b9a460 (MD5); Made available in DSpace on 2022-03-15T09:23:29Z (GMT). No. of bitstreams: 1 ijimai20195_6_1_pdf_17622.pdf: 1061408 bytes, checksum: ba43ba351ab50685925b88f828b9a460 (MD5)

A User-centered Smartphone Application for Wireless EEG and its Role in Epilepsy

A User-centered Smartphone Application for Wireless EEG and its Role in Epilepsy Ahufinger, Sofía; Balugo, Paloma; González, María Mercedes; Pequeño, Elías; González, Henar; Herrero, Pilar Electroencephalography is well-known for its importance in the diagnosis and treatment of mental and neurological disorders and abnormalities, being especially noted in critically ill patients who suffer a variety of cerebral injuries and altered states of consciousness. However, there is an important lack of adapted equipment and applications designed to suit the clinical and research needs. Hence, patients, physicians and researchers suffer, in most cases, from a restricted mobility due to non-portable devices and wires which keep them attached to the bed, leading to an uncomfortable patient experience or difficulties during the recording. In addition, nowadays, both physicians and researchers need to access the recordings and patient information from different places such as different units or hospitals. To solve this problem, this paper presents the design and evaluation of the high-fidelity prototype of a wireless EEG smartphone application based on a user-centred design, including expert panel guidance, paper and high-fidelity prototyping and usability testing, which confirm the accuracy of the defined context of use and the validity of the prototyped application to suit the clinical and research needs. In fact, since the EEG is the most efficient and specific way to define the epileptogenic cortex, we will focus on the possible use of the presented App in epilepsy diagnosis, which is one of the main targets in the field. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T09:18:04Z No. of bitstreams: 1 ijimai20195_6_5_pdf_21239.pdf: 293153 bytes, checksum: aae82f3c0f5d0731e4c32d521dc88c15 (MD5); Made available in DSpace on 2022-03-15T09:18:04Z (GMT). No. of bitstreams: 1 ijimai20195_6_5_pdf_21239.pdf: 293153 bytes, checksum: aae82f3c0f5d0731e4c32d521dc88c15 (MD5)

Do Women and Men Perceive User Experience Differently?

Do Women and Men Perceive User Experience Differently? Schrepp, Martin; chewski, Jörg Thomas; Aufderhaar, Kristina We study three web sites to see whether there are systematic differences between women and men in their rating of the user experience of the sites. One of the sites addresses especially the target group of women, another the target group of men, whereas the third site is neutral in this respect. The selection of the sites was safeguarded with gender screening. The participants in the study rated the three chosen websites with the questionnaires UEQ and VISAWI-S. The results indicate that there are no substantial differences in the perception of the UX between men and women. Personal attitudes and preferences seem to have a substantially greater influence than sex. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T09:12:13Z No. of bitstreams: 1 ijimai20195_6_8_pdf_16167.pdf: 728501 bytes, checksum: fbd608ea8834dd64e79bc337260d3170 (MD5); Made available in DSpace on 2022-03-15T09:12:13Z (GMT). No. of bitstreams: 1 ijimai20195_6_8_pdf_16167.pdf: 728501 bytes, checksum: fbd608ea8834dd64e79bc337260d3170 (MD5)

Voice Analysis Using PRAAT Software and Classification of User Emotional State

Voice Analysis Using PRAAT Software and Classification of User Emotional State Magdin, Martin; Sulka, T; Tomanová, J; Vozár, M During the last decades the field of IT has seen an incredible and very rapid development. This development has shown that it is important not only to shift performance and functional boundaries but also to adapt the way human-computer interaction to modern needs. One of the interaction possibilities is a voice control which nowadays can‘t be restricted only to direct commands. The goal of adaptive interaction between man and computer is the human needs understanding. The paper deals with the user's emotional state classification based on the voice track analysis, it describes its own solution - the measurement and the selection process of appropriate voice characteristics using ANOVA analysis and the use of PRAAT software for many voice aspects analysis and for the implementation of own application to classify the user's emotional state from his/her voice. In the paper are presented the results of the created application testing and the possibilities of further expansion and improvement of this solution. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T09:07:53Z No. of bitstreams: 1 ijimai20195_6_4_pdf_80526.pdf: 470562 bytes, checksum: fa5d3148fdcbe8f347d7ef3660cf6b20 (MD5); Made available in DSpace on 2022-03-15T09:07:53Z (GMT). No. of bitstreams: 1 ijimai20195_6_4_pdf_80526.pdf: 470562 bytes, checksum: fa5d3148fdcbe8f347d7ef3660cf6b20 (MD5)

An Improved Artificial Intelligence Based on Gray Wolf Optimization and Cultural Algorithm to Predict Demand for Dairy Products: A Case Study

An Improved Artificial Intelligence Based on Gray Wolf Optimization and Cultural Algorithm to Predict Demand for Dairy Products: A Case Study Goli, Alireza; Zare, Hassan Khademi; Tavakkoli-Moghaddam, Reza; Sadeghieh, Ahmad This paper provides an integrated framework based on statistical tests, time series neural network and improved multi-layer perceptron neural network (MLP) with novel meta-heuristic algorithms in order to obtain best prediction of dairy product demand (DPD) in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using Pearson correlation coefficient, and statistically significant variables are determined. Then, MLP is improved with the help of novel meta-heuristic algorithms such as gray wolf optimization and cultural algorithm. The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The results show that the MLP offers 71.9% of the coefficient of determination, which is better compared to the other two methods if no improvement is achieved. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-15T08:55:38Z No. of bitstreams: 1 ijimai20195_6_2_pdf_10531.pdf: 649398 bytes, checksum: f98b30f13053b5c86fc063c76e4ec77f (MD5); Made available in DSpace on 2022-03-15T08:55:38Z (GMT). No. of bitstreams: 1 ijimai20195_6_2_pdf_10531.pdf: 649398 bytes, checksum: f98b30f13053b5c86fc063c76e4ec77f (MD5)

Contour Enhancement Algorithm for Improving Visual Perception of Deutan and Protan Dichromats

Contour Enhancement Algorithm for Improving Visual Perception of Deutan and Protan Dichromats Ribeiro, Madalena; Gomes, Abel A variety of recoloring methods has been proposed in the literature to remedy the problem of confusing red-green colors faced by dichromat people (as well by other color-blinded people). The common strategy to mitigate this problem is to remap colors to other colors. But, it is clear this does not guarantee neither the necessary contrast to distinguish the elements of an image, nor the naturalness of colors learnt from past experience of each individual. In other words, the individual’s perceptual learning may not hold under color remapping. With this in mind, we introduce the first algorithm primarily focused on the enhancement of object contours in still images, instead of recoloring the pixels of the regions bounded by such contours. This is particularly adequate to increase contrast in images where we find adjacent regions that are color-indistinguishable from the dichromacy’s point of view. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T12:53:19Z No. of bitstreams: 1 ijimai_5_5_10_pdf_53220.pdf: 2599974 bytes, checksum: d3cfc67d7ff776095cae39795af2fa3e (MD5); Made available in DSpace on 2022-02-28T12:53:19Z (GMT). No. of bitstreams: 1 ijimai_5_5_10_pdf_53220.pdf: 2599974 bytes, checksum: d3cfc67d7ff776095cae39795af2fa3e (MD5)

Editor’s Note

Editor’s Note Verdú, Elena The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. This regular issue presents research works based on different AI methods such as deep networks, genetic algorithms or classification trees algorithms. These methods are applied into many and various fields as video surveillance, forgery detection, facial recognition, activity recognition, hand written character recognition, clinical decision, marketing, renewable energy or social networking. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T12:26:40Z No. of bitstreams: 1 ijimai_5_5_0_pdf_42920.pdf: 179636 bytes, checksum: bb3a08eac40ec4a0ee8f3668c0274858 (MD5); Made available in DSpace on 2022-02-28T12:26:40Z (GMT). No. of bitstreams: 1 ijimai_5_5_0_pdf_42920.pdf: 179636 bytes, checksum: bb3a08eac40ec4a0ee8f3668c0274858 (MD5)

Marketing Intelligence and Big Data: Digital Marketing Techniques on their Way to Becoming Social Engineering Techniques in Marketing

Marketing Intelligence and Big Data: Digital Marketing Techniques on their Way to Becoming Social Engineering Techniques in Marketing Lies, Jan This contribution reviews the vast scope of digital application areas, which shape the digital marketing landscape and coin the present term “marketing intelligence” from a marketing technique point of view. Additionally, marketing intelligence as social engineering techniques are described. The review ranges from digital IT- and big data marketing until marketing 5.0 as digitalized trust marketing. The multiplicity of applications and interdependencies of the digital and social techniques reviewed should show that big data and marketing intelligence have already become a marketing reality. It becomes clear that marketing is witnessing a methodological, technical and cultural paradigm shift that augments and amplifies traditional outbound marketing with inbound marketing. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T12:18:06Z No. of bitstreams: 1 ijimai_5_5_16_pdf_13983.pdf: 287444 bytes, checksum: c6acfcd1dc39d1736b3d6ce2e13ad815 (MD5); Made available in DSpace on 2022-02-28T12:18:06Z (GMT). No. of bitstreams: 1 ijimai_5_5_16_pdf_13983.pdf: 287444 bytes, checksum: c6acfcd1dc39d1736b3d6ce2e13ad815 (MD5)

Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models

Forecasting the Behavior of Gas Furnace Multivariate Time Series Using Ridge Polynomial Based Neural Network Models Waheeb, Waddah; Ghazali, Rozaida In this paper, a new application of ridge polynomial based neural network models in multivariate time series forecasting is presented. The existing ridge polynomial based neural network models can be grouped into two groups. Group A consists of models that use only autoregressive inputs, whereas Group B consists of models that use autoregressive and moving-average (i.e., error feedback) inputs. The well-known Box-Jenkins gas furnace multivariate time series was used in the forecasting comparison between the two groups. Simulation results show that the models in Group B achieve significant forecasting performance as compared to the models in Group A. Therefore, the Box-Jenkins gas furnace data can be modeled better using neural networks when error feedback is used. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T11:51:12Z No. of bitstreams: 1 ijimai_5_5_15_pdf_10586.pdf: 1192665 bytes, checksum: 342da9c69270f6bb01bd1e616bdf2c04 (MD5); Made available in DSpace on 2022-02-28T11:51:12Z (GMT). No. of bitstreams: 1 ijimai_5_5_15_pdf_10586.pdf: 1192665 bytes, checksum: 342da9c69270f6bb01bd1e616bdf2c04 (MD5)

A Recent Trend in Individual Counting Approach Using Deep Network

A Recent Trend in Individual Counting Approach Using Deep Network Ghazvini, Anahita; Abdullah, Siti Norul Huda Sheikh; Ayob, Masri In video surveillance scheme, counting individuals is regarded as a crucial task. Of all the individual counting techniques in existence, the regression technique can offer enhanced performance under overcrowded area. However, this technique is unable to specify the details of counting individual such that it fails in locating the individual. On contrary, the density map approach is very effective to overcome the counting problems in various situations such as heavy overlapping and low resolution. Nevertheless, this approach may break down in cases when only the heads of individuals appear in video scenes, and it is also restricted to the feature’s types. The popular technique to obtain the pertinent information automatically is Convolutional Neural Network (CNN). However, the CNN based counting scheme is unable to sufficiently tackle three difficulties, namely, distributions of non-uniform density, changes of scale and variation of drastic scale. In this study, we cater a review on current counting techniques which are in correlation with deep net in different applications of crowded scene. The goal of this work is to specify the effectiveness of CNN applied on popular individuals counting approaches for attaining higher precision results. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T11:45:42Z No. of bitstreams: 1 ijimai_5_5_1_pdf_11035.pdf: 274933 bytes, checksum: d70947019a084a20d5065c9d467967a2 (MD5); Made available in DSpace on 2022-02-28T11:45:42Z (GMT). No. of bitstreams: 1 ijimai_5_5_1_pdf_11035.pdf: 274933 bytes, checksum: d70947019a084a20d5065c9d467967a2 (MD5)

Optimal Performance of Doubly Fed Induction Generator Wind Farm Using Multi-Objective Genetic Algorithm

Optimal Performance of Doubly Fed Induction Generator Wind Farm Using Multi-Objective Genetic Algorithm Kamel, Salah; Jurado, Francisco; Elkasem, Ahmed; Rashad, Ahmed The main purpose of this paper is allowing doubly fed induction generator wind farms (DFIG), which are connected to power system, to effectively participate in feeding electrical loads. The oscillation in power system is one of the challenges of the interconnection of wind farms to the grid. The model of DFIG contains several gains which need to be achieved with optimal values. This aim can be accomplished using an optimization algorithm in order to obtain the best performance. The multi-objective optimization algorithm is used to determine the optimal control system gains under several objectives. In this paper, a multi-objective genetic algorithm is applied to the DFIG model to determine the optimal values of the gains of DFIG control system. In order to point out the contribution of this work; the performance of optimized DFIG model is compared with the non-optimized model of DFIG. The results show that the optimized model of DFIG has better performance over the non-optimized DFIG model. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T11:25:50Z No. of bitstreams: 1 ijimai_5_5_6_pdf_17227.pdf: 915651 bytes, checksum: da8f20b0a8735d65c463d86cbb119b5c (MD5); Made available in DSpace on 2022-02-28T11:25:50Z (GMT). No. of bitstreams: 1 ijimai_5_5_6_pdf_17227.pdf: 915651 bytes, checksum: da8f20b0a8735d65c463d86cbb119b5c (MD5)

GIFT: Gesture-Based Interaction by Fingers Tracking, an Interaction Technique for Virtual Environment

GIFT: Gesture-Based Interaction by Fingers Tracking, an Interaction Technique for Virtual Environment Ullah, S; Raees, M Three Dimensional (3D) interaction is the plausible human interaction inside a Virtual Environment (VE). The rise of the Virtual Reality (VR) applications in various domains demands for a feasible 3D interface. Ensuring immersivity in a virtual space, this paper presents an interaction technique where manipulation is performed by the perceptive gestures of the two dominant fingers; thumb and index. The two fingertip-thimbles made of paper are used to trace states and positions of the fingers by an ordinary camera. Based on the positions of the fingers, the basic interaction tasks; selection, scaling, rotation, translation and navigation are performed by intuitive gestures of the fingers. Without keeping a gestural database, the features-free detection of the fingers guarantees speedier interactions. Moreover, the system is user-independent and depends neither on the size nor on the color of the users’ hand. With a case-study project; Interactions by the Gestures of Fingers (IGF) the technique is implemented for evaluation. The IGF application traces gestures of the fingers using the libraries of OpenCV at the back-end. At the front-end, the objects of the VE are rendered accordingly using the Open Graphics Library; OpenGL. The system is assessed in a moderate lighting condition by a group of 15 users. Furthermore, usability of the technique is investigated in games. Outcomes of the evaluations revealed that the approach is suitable for VR applications both in terms of cost and accuracy. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T11:19:40Z No. of bitstreams: 1 ijimai_5_5_14_pdf_29363.pdf: 2694018 bytes, checksum: f9cf545f2eeb77a2cb238eb34e2b88f3 (MD5); Made available in DSpace on 2022-02-28T11:19:40Z (GMT). No. of bitstreams: 1 ijimai_5_5_14_pdf_29363.pdf: 2694018 bytes, checksum: f9cf545f2eeb77a2cb238eb34e2b88f3 (MD5)

User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features

User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features George, Loay; Hadi, Hend In this study, the approach of combined features from two simultaneous Electroencephalogram (EEG) channels when a user is performing a certain mental task is discussed to increase the discrimination degree among subject classes, hence the visibility of using sets of features extracted from a single channel was investigated in previously published articles. The feature sets considered in previous studies is utilized to establish a combined set of features extracted from two channels. The first feature set is the energy density of power spectra of Discrete Fourier Transform (DFT) or Discrete Cosine Transform; the second one is the set of statistical moments of Discrete Wavelet Transform (DWT). Euclidean distance metric is used to accomplish feature set matching task. The combinations of features from two EEG channels showed high accuracy for the identification system, and competitive results for the verification system. The best achieved identification accuracy is (100%) for all proposed feature sets. For verification mode the best achieved Half Total Error Rate (HTER) is (0.88) with accuracy (99.12%) on Colorado State University (CSU) dataset, and (0.26) with accuracy (99.97%) on Motor Movement/Imagery (MMI) dataset. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T11:05:36Z No. of bitstreams: 1 ijimai_5_5_7_pdf_30776.pdf: 1234323 bytes, checksum: ae5a77bae2101df0b707f9b23e170f63 (MD5); Made available in DSpace on 2022-02-28T11:05:36Z (GMT). No. of bitstreams: 1 ijimai_5_5_7_pdf_30776.pdf: 1234323 bytes, checksum: ae5a77bae2101df0b707f9b23e170f63 (MD5)

Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method

Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method Harish, B S; Kumar, Keerthi; Darshan, H K Social Networking sites have become popular and common places for sharing wide range of emotions through short texts. These emotions include happiness, sadness, anxiety, fear, etc. Analyzing short texts helps in identifying the sentiment expressed by the crowd. Sentiment Analysis on IMDb movie reviews identifies the overall sentiment or opinion expressed by a reviewer towards a movie. Many researchers are working on pruning the sentiment analysis model that clearly identifies and distinguishes between a positive review and a negative review. In the proposed work, we show that the use of Hybrid features obtained by concatenating Machine Learning features (TF, TF-IDF) with Lexicon features (Positive-Negative word count, Connotation) gives better results both in terms of accuracy and complexity when tested against classifiers like SVM, Naïve Bayes, KNN and Maximum Entropy. The proposed model clearly differentiates between a positive review and negative review. Since understanding the context of the reviews plays an important role in classification, using hybrid features helps in capturing the context of the movie reviews and hence increases the accuracy of classification. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T10:47:30Z No. of bitstreams: 1 ijimai_5_5_13_pdf_67503.pdf: 456286 bytes, checksum: f5a3f5578df8946e92c8c1e61e2d89fa (MD5); Made available in DSpace on 2022-02-28T10:47:30Z (GMT). No. of bitstreams: 1 ijimai_5_5_13_pdf_67503.pdf: 456286 bytes, checksum: f5a3f5578df8946e92c8c1e61e2d89fa (MD5)

IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition Djamila, Hamdadou; Belkhodja, Leila Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition System. The proposed approach is applied to real clinical images from the full field digital mammographic database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of 100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on recognizing masses from a very restricted set of training regions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T10:36:30Z No. of bitstreams: 1 ijimai_5_5_12_pdf_14881.pdf: 1446164 bytes, checksum: 160008d7c7caea9b08dc5dc1b63adb14 (MD5); Made available in DSpace on 2022-02-28T10:36:30Z (GMT). No. of bitstreams: 1 ijimai_5_5_12_pdf_14881.pdf: 1446164 bytes, checksum: 160008d7c7caea9b08dc5dc1b63adb14 (MD5)

Contribution to the Association Rules Visualization for Decision Support: A Combined Use Between Boolean Modeling and the Colored 2D Matrix

Contribution to the Association Rules Visualization for Decision Support: A Combined Use Between Boolean Modeling and the Colored 2D Matrix Atmani, Baghdad; Benhacine, Fatima Zohra; Abdelouhab, Fawzia Zohra In the present paper we aim to study the visual decision support based on Cellular machine CASI (Cellular Automata for Symbolic Induction). The purpose is to improve the visualization of large sets of association rules, in order to perform Clinical decision support system and decrease doctors’ cognitive charge. One of the major problems in processing association rules is the exponential growth of generated rules volume which impacts doctor’s adaptation. In order to clarify it, many approaches meant to represent this set of association rules under visual context have been suggested. In this article we suggest to use jointly the CASI cellular machine and the colored 2D matrices to improve the visualization of association rules. Our approach has been divided into four important phases: (1) Data preparation, (2) Extracting association rules, (3) Boolean modeling of the rules base (4) 2D visualization colored by Boolean inferences. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-28T10:14:59Z No. of bitstreams: 1 ijimai_5_5_5_pdf_16533.pdf: 827642 bytes, checksum: 6bd65e303d0548b2c2f9c5d58e17b265 (MD5); Made available in DSpace on 2022-02-28T10:14:59Z (GMT). No. of bitstreams: 1 ijimai_5_5_5_pdf_16533.pdf: 827642 bytes, checksum: 6bd65e303d0548b2c2f9c5d58e17b265 (MD5)

A Novel Approach on Visual Question Answering by Parameter Prediction using Faster Region Based Convolutional Neural Network

A Novel Approach on Visual Question Answering by Parameter Prediction using Faster Region Based Convolutional Neural Network Jha, Sudan; Dey, Anirban; Kumar, Raghvendra; Kumar-Solanki, Vijender Visual Question Answering (VQA) is a stimulating process in the ﬁeld of Natural Language Processing (NLP) and Computer Vision (CV). In this process machine can find an answer to a natural language question which is related to an image. Question can be open-ended or multiple choice. Datasets of VQA contain mainly three components; questions, images and answers. Researchers overcome the VQA problem with deep learning based architecture that jointly combines both of two networks i.e. Convolution Neural Network (CNN) for visual (image) representation and Recurrent Neural Network (RNN) with Long Short Time Memory (LSTM) for textual (question) representation and trained the combined network end to end to generate the answer. Those models are able to answer the common and simple questions that are directly related to the image’s content. But different types of questions need different level of understanding to produce correct answers. To solve this problem, we use faster Region based-CNN (R-CNN) for extracting image features with an extra fully connected layer whose weights are dynamically obtained by LSTMs cell according to the question. We claim in this paper that a single R-CNN architecture can solve the problems related to VQA by modifying weights in the parameter prediction layer. Authors trained the network end to end by Stochastic Gradient Descent (SGD) using pretrained faster R-CNN and LSTM and tested it on benchmark datasets of VQA. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-24T10:50:56Z No. of bitstreams: 1 ijimai_5_5_4_pdf_36854.pdf: 1645422 bytes, checksum: 2389b1ebfa24f250540f62850dc0ec52 (MD5); Made available in DSpace on 2022-02-24T10:50:56Z (GMT). No. of bitstreams: 1 ijimai_5_5_4_pdf_36854.pdf: 1645422 bytes, checksum: 2389b1ebfa24f250540f62850dc0ec52 (MD5)

Detecting Image Brush Editing Using the Discarded Coefficients and Intentions

Detecting Image Brush Editing Using the Discarded Coefficients and Intentions López Hernández, Fernando Carlos; de-la-Fuente-Valentín, Luis; Sarría, Íñigo This paper describes a quick and simple method to detect brush editing in JPEG images. The novelty of the proposed method is based on detecting the discarded coefficients during the quantization of the image. Another novelty of this paper is the development of a subjective metric named intentions. The method directly analyzes the allegedly tampered image and generates a forgery mask indicating forgery evidence for each image block. The experiments show that our method works especially well in detecting brush strokes, and it works reasonably well with added captions and image splicing. However, the method is less effective detecting copy-moved and blurred regions. This means that our method can effectively contribute to implementing a complete imagetampering detection tool. The editing operations for which our method is less effective can be complemented with methods more adequate to detect them. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-24T10:27:11Z No. of bitstreams: 1 ijimai_5_5_2_pdf_18456.pdf: 1323688 bytes, checksum: 9a6025ecf36c634d091e29269c999ff2 (MD5); Made available in DSpace on 2022-02-24T10:27:11Z (GMT). No. of bitstreams: 1 ijimai_5_5_2_pdf_18456.pdf: 1323688 bytes, checksum: 9a6025ecf36c634d091e29269c999ff2 (MD5)

Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images

Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images Jalal, Ahmad; Kamal, Shaharyar Behavior monitoring and classification is a mechanism used to automatically identify or verify individual based on their human detection, tracking and behavior recognition from video sequences captured by a depth camera. In this paper, we designed a system that precisely classifies the nature of 3D body postures obtained by Kinect using an advanced recognizer. We proposed novel features that are suitable for depth data. These features are robust to noise, invariant to translation and scaling, and capable of monitoring fast human bodyparts movements. Lastly, advanced hidden Markov model is used to recognize different activities. In the extensive experiments, we have seen that our system consistently outperforms over three depth-based behavior datasets, i.e., IM-DailyDepthActivity, MSRDailyActivity3D and MSRAction3D in both posture classification and behavior recognition. Moreover, our system handles subject's body parts rotation, self-occlusion and body parts missing which significantly track complex activities and improve recognition rate. Due to easy accessible, low-cost and friendly deployment process of depth camera, the proposed system can be applied over various consumer-applications including patient-monitoring system, automatic video surveillance, smart homes/offices and 3D games. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-24T10:01:36Z No. of bitstreams: 1 ijimai_5_5_9_pdf_48446.pdf: 578451 bytes, checksum: d0dd27c2ec3c4c07158deeb3ab61e9ba (MD5); Made available in DSpace on 2022-02-24T10:01:36Z (GMT). No. of bitstreams: 1 ijimai_5_5_9_pdf_48446.pdf: 578451 bytes, checksum: d0dd27c2ec3c4c07158deeb3ab61e9ba (MD5)

A Diversity-Accuracy Measure for Homogenous Ensemble Selection

A Diversity-Accuracy Measure for Homogenous Ensemble Selection Zouggar, Taleb; Adla, A Several selection methods in the literature are essentially based on an evaluation function that determines whether a model M contributes positively to boost the performances of the whole ensemble. In this paper, we propose a method called DIversity and ACcuracy for Ensemble Selection (DIACES) using an evaluation function based on both diversity and accuracy. The method is applied on homogenous ensembles composed of C4.5 decision trees and based on a hill climbing strategy. This allows selecting ensembles with the best compromise between maximum diversity and minimum error rate. Comparative studies show that in most cases the proposed method generates reduced size ensembles with better performances than usual ensemble simplification methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-24T09:54:33Z No. of bitstreams: 1 ijimai_5_5_8_pdf_37634.pdf: 644785 bytes, checksum: 99cc13fa30ac38ba5beec9ab85043c80 (MD5); Made available in DSpace on 2022-02-24T09:54:33Z (GMT). No. of bitstreams: 1 ijimai_5_5_8_pdf_37634.pdf: 644785 bytes, checksum: 99cc13fa30ac38ba5beec9ab85043c80 (MD5)

Deep Belief Network and Auto-Encoder for Face Classification

Deep Belief Network and Auto-Encoder for Face Classification Bouchra, Nassih; Mohammed, Ngadi; Nabil, Hmina; Aouatif, Amine The Deep Learning models have drawn ever-increasing research interest owing to their intrinsic capability of overcoming the drawback of traditional algorithm. Hence, we have adopted the representative Deep Learning methods which are Deep Belief Network (DBN) and Stacked Auto-Encoder (SAE), to initialize deep supervised Neural Networks (NN), besides of Back Propagation Neural Networks (BPNN) applied to face classification task. Moreover, our contribution is to extract hierarchical representations of face image based on the Deep Learning models which are: DBN, SAE and BPNN. Then, the extracted feature vectors of each model are used as input of NN classifier. Next, to test our approach and evaluate its performance, a simulation series of experiments were performed on two facial databases: BOSS and MIT. Our proposed approach which is (DBN,NN) has a significant improvement on the classification error rate compared to (SAE,NN) and BPNN which we get 1.14% and 1.96% in terms of error rate with BOSS and MIT respectively. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-24T09:30:17Z No. of bitstreams: 1 ijimai_5_5_3_pdf_64780.pdf: 1185160 bytes, checksum: fa2fcff4722df825ed0f384dc93fa8f9 (MD5); Made available in DSpace on 2022-02-24T09:30:17Z (GMT). No. of bitstreams: 1 ijimai_5_5_3_pdf_64780.pdf: 1185160 bytes, checksum: fa2fcff4722df825ed0f384dc93fa8f9 (MD5)

Handwritten Arabic Documents Segmentation into Text Lines using Seam Carving

Handwritten Arabic Documents Segmentation into Text Lines using Seam Carving Souhar, Abdelghani; Daldali, M Inspired from human perception and common text documents characteristics based on readability constraints, an Arabic text line segmentation approach is proposed using seam carving. Taking the gray scale of the image as input data, this technique offers better results at extracting handwritten text lines without the need for the binary representation of the document image. In addition to its fast processing time, its versatility permits to process a multitude of document types, especially documents presenting low text-to-background contrast such as degraded historical manuscripts or complex writing styles like cursive handwriting. Even if our focus in this paper was on Arabic text segmentation, this method is language independent. Tests on a public database of 123 handwritten Arabic documents showed a line detection rate of 97.5% for a matching score of 90%. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-24T08:50:00Z No. of bitstreams: 1 ijimai_5_5_11_pdf_12351.pdf: 1591538 bytes, checksum: 5825c7c92e4aa7cb244d81430781128b (MD5); Made available in DSpace on 2022-02-24T08:50:00Z (GMT). No. of bitstreams: 1 ijimai_5_5_11_pdf_12351.pdf: 1591538 bytes, checksum: 5825c7c92e4aa7cb244d81430781128b (MD5)

Editor’s Note

Editor’s Note Mochón, Francisco This special issue has been designed with the primary objective of demonstrating the diversity of fields where AI is used and, consequently, how it is gaining increasing importance as a tool for analysis and research. In this sense, there are works related to the following topics: the use of AI with the IoT, campaign management, topic models and fusion methods, sales forecasting, price forecasting for electricity market, NLP techniques in computational medicine, evaluation of patient triage in hospital emergency settings, algorithms for solving the assignment problem, scheduling strategy for scientific workflow, driver fatigue detection mechanisms, virtual reality and specialized training, image segmentation, web service selection, multimedia documents adaptation, 3D navigation in virtual environments, multi-criteria decision-making methods and emotional states classification. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T11:02:12Z No. of bitstreams: 1 ijimai_5_4_0_pdf_20163.pdf: 192131 bytes, checksum: af9e89cafe5375787740df1711528ae5 (MD5); Made available in DSpace on 2022-02-21T11:02:13Z (GMT). No. of bitstreams: 1 ijimai_5_4_0_pdf_20163.pdf: 192131 bytes, checksum: af9e89cafe5375787740df1711528ae5 (MD5)

Topic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling

Topic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling Pourvali, Mohsen; Orlando, Salvatore; Omidvarborna, Hosna Topic modeling algorithms are statistical methods that aim to discover the topics running through the text documents. Using topic models in machine learning and text mining is popular due to its applicability in inferring the latent topic structure of a corpus. In this paper, we represent an enriching document approach, using state-of-the-art topic models and data fusion methods, to enrich documents of a collection with the aim of improving the quality of text clustering and cluster labeling. We propose a bi-vector space model in which every document of the corpus is represented by two vectors: one is generated based on the fusion-based topic modeling approach, and one simply is the traditional vector model. Our experiments on various datasets show that using a combination of topic modeling and fusion methods to create documents’ vectors can significantly improve the quality of the results in clustering the documents. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T10:58:19Z No. of bitstreams: 1 ijimai_5_4_3_pdf_49140.pdf: 2145322 bytes, checksum: 573ec5c868f5dcbe3137036e445222f6 (MD5); Made available in DSpace on 2022-02-21T10:58:19Z (GMT). No. of bitstreams: 1 ijimai_5_4_3_pdf_49140.pdf: 2145322 bytes, checksum: 573ec5c868f5dcbe3137036e445222f6 (MD5)

Multilevel Thresholding for Image Segmentation Using an Improved Electromagnetism Optimization Algorithm

Multilevel Thresholding for Image Segmentation Using an Improved Electromagnetism Optimization Algorithm Hemeida, Ashraf; Mansour, Radwa; Hussein, M E Image segmentation is considered one of the most important tasks in image processing, which has several applications in different areas such as; industry agriculture, medicine, etc. In this paper, we develop the electromagnetic optimization (EMO) algorithm based on levy function, EMO-levy, to enhance the EMO performance for determining the optimal multi-level thresholding of image segmentation. In general, EMO simulates the mechanism of attraction and repulsion between charges to develop the individuals of a population. EMO takes random samples from search space within the histogram of image, where, each sample represents each particle in EMO. The quality of each particle is assessed based on Otsu’s or Kapur objective function value. The solutions are updated using EMO operators until determine the optimal objective functions. Finally, this approach produces segmented images with optimal values for the threshold and a few number of iterations. The proposed technique is validated using different standard test images. Experimental results prove the effectiveness and superiority of the proposed algorithm for image segmentation compared with well-known optimization methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T10:14:40Z No. of bitstreams: 1 ijimai_5_4_12_pdf_62616.pdf: 1296186 bytes, checksum: eeedeaecf12dec987042458aa965850d (MD5); Made available in DSpace on 2022-02-21T10:14:40Z (GMT). No. of bitstreams: 1 ijimai_5_4_12_pdf_62616.pdf: 1296186 bytes, checksum: eeedeaecf12dec987042458aa965850d (MD5)

Data-Aware Scheduling Strategy for Scientific Workflow Applications in IaaS Cloud Computing

Data-Aware Scheduling Strategy for Scientific Workflow Applications in IaaS Cloud Computing Makhlouf, Sid Ahmed; Yagoubi, Belabbas Scientific workflows benefit from the cloud computing paradigm, which offers access to virtual resources provisioned on pay-as-you-go and on-demand basis. Minimizing resources costs to meet user’s budget is very important in a cloud environment. Several optimization approaches have been proposed to improve the performance and the cost of data-intensive scientific Workflow Scheduling (DiSWS) in cloud computing. However, in the literature, the majority of the DiSWS approaches focused on the use of heuristic and metaheuristic as an optimization method. Furthermore, the tasks hierarchy in data-intensive scientific workflows has not been extensively explored in the current literature. Specifically, in this paper, a data-intensive scientific workflow is represented as a hierarchy, which specifies hierarchical relations between workflow tasks, and an approach for data-intensive workflow scheduling applications is proposed. In this approach, first, the datasets and workflow tasks are modeled as a conditional probability matrix (CPM). Second, several data transformation and hierarchical clustering are applied to the CPM structure to determine the minimum number of virtual machines needed for the workflow execution. In this approach, the hierarchical clustering is done with respect to the budget imposed by the user. After data transformation and hierarchical clustering, the amount of data transmitted between clusters can be reduced, which can improve cost and makespan of the workflow by optimizing the use of virtual resources and network bandwidth. The performance and cost are analyzed using an extension of Cloudsim simulation tool and compared with existing multi-objective approaches. The results demonstrate that our approach reduces resources cost with respect to the user budgets. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T10:00:59Z No. of bitstreams: 1 ijimai_5_4_9_pdf_20202.pdf: 807325 bytes, checksum: 722b5222347095131cae7b5cc55b94ff (MD5); Made available in DSpace on 2022-02-21T10:00:59Z (GMT). No. of bitstreams: 1 ijimai_5_4_9_pdf_20202.pdf: 807325 bytes, checksum: 722b5222347095131cae7b5cc55b94ff (MD5)

Two Hand Gesture Based 3D Navigation in Virtual Environments

Two Hand Gesture Based 3D Navigation in Virtual Environments Rehman, I; Ullah, S; Raees, M Natural interaction is gaining popularity due to its simple, attractive, and realistic nature, which realizes direct Human Computer Interaction (HCI). In this paper, we presented a novel two hand gesture based interaction technique for 3 dimensional (3D) navigation in Virtual Environments (VEs). The system used computer vision techniques for the detection of hand gestures (colored thumbs) from real scene and performed different navigation (forward, backward, up, down, left, and right) tasks in the VE. The proposed technique also allow users to efficiently control speed during navigation. The proposed technique is implemented via a VE for experimental purposes. Forty (40) participants performed the experimental study. Experiments revealed that the proposed technique is feasible, easy to learn and use, having less cognitive load on users. Finally gesture recognition engines were used to assess the accuracy and performance of the proposed gestures. kNN achieved high accuracy rates (95.7%) as compared to SVM (95.3%). kNN also has high performance rates in terms of training time (3.16 secs) and prediction speed (6600 obs/sec) as compared to SVM with 6.40 secs and 2900 obs/sec. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T09:54:32Z No. of bitstreams: 1 ijimai_5_4_15_pdf_17617.pdf: 1445539 bytes, checksum: 01851524e9e0dd1e592a28dcadf4fdd5 (MD5); Made available in DSpace on 2022-02-21T09:54:32Z (GMT). No. of bitstreams: 1 ijimai_5_4_15_pdf_17617.pdf: 1445539 bytes, checksum: 01851524e9e0dd1e592a28dcadf4fdd5 (MD5)

Day-Ahead Price Forecasting for the Spanish Electricity Market

Day-Ahead Price Forecasting for the Spanish Electricity Market Díaz, Julia; Romero, Álvaro; Dorronsoro, José Ramón During the last years, electrical systems around the world and in particular the Spanish electric sector have undergone great changes with the focus of turning them into more liberalized and competitive markets. For this reason, in many countries like Spain have appeared electric markets where producers sell and electricity retailers buy the power we consume. All agents involved in this market need predictions of generation, demand and especially prices to be able to participate in them in a more efficient way, obtaining a greater profit. The present work is focused on the context of development of a tool that allows to predict the price of electricity for the next day in the most precise way possible. For such target, this document analyzes the electric market to understand how prices are calculated and who are the agents that can make prices vary. Traditional proposals in the literature range from the use of Game Theory to the use of Machine Learning, Time Series Analysis or Simulation Models. In this work we analyze a normalization of the target variable due to a strong seasonal component in an hourly and daily way to later benchmark several models of Machine Learning: Ridge Regression, K-Nearest Neighbors, Support Vector Machines, Neural Networks and Random Forest. After observing that the best model is Random Forest, a discussion has been carried out on the appropriateness of the normalization for this algorithm. From this analysis it is obtained that the model that gives the best results has been Random Forest without applying the normalization function. This is due to the loss of the close relationship between the objective variable and the electric demand, obtaining an Average Absolute Error of 3.92€ for the whole period of 2016. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T09:43:11Z No. of bitstreams: 1 ijimai_5_4_5_pdf_14997.pdf: 737444 bytes, checksum: 29b9319e28eee412d8a16e3c73027b44 (MD5); Made available in DSpace on 2022-02-21T09:43:11Z (GMT). No. of bitstreams: 1 ijimai_5_4_5_pdf_14997.pdf: 737444 bytes, checksum: 29b9319e28eee412d8a16e3c73027b44 (MD5)

PRACTICA. A Virtual Reality Platform for Specialized Training Oriented to Improve the Productivity

PRACTICA. A Virtual Reality Platform for Specialized Training Oriented to Improve the Productivity Lombardo, Juan Manuel; López, Miguel Ángel; Velasco, Susana; García, Vicente; López, Mabel; Cañadas, Rubén; León, Mónica With the proliferation of Virtual reality headset that are emerging into a consumer-oriented market for video games, it will open new possibilities for exploiting the virtual reality (VR). Therefore, the PRACTICA project is defined as a new service aimed to offering a system for creating courses based on a VR simulator for specialized training companies that allows offering to the students an experience close to reality. The general problem of creating these virtual courses derives from the need to have programmers that can generate them. Therefore, the PRACTICA project allows the creation of courses without the need to program source code. In addition, elements of virtual interaction have been incorporated that cannot be used in a real environment due to risks for the staff, such as the introduction of fictional characters or obstacles that interact with the environment. So to do this, artificial intelligence techniques have been incorporated so these elements can interact with the user, as it may be, the movement of these fictional characters on stage with a certain behavior. This feature offers the opportunity to create situations and scenarios that are even more complex and realistic.This project aims to create a service to bring virtual reality technologies closer and artificial intelligence for non-technological companies, so that they can generate (or acquire) their own content and give it the desired shape for their purposes. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T09:16:45Z No. of bitstreams: 1 ijimai_5_4_11_pdf_83808.pdf: 632376 bytes, checksum: efb27c6187a8d60ef1ab8f73f00fdaf5 (MD5); Made available in DSpace on 2022-02-21T09:16:45Z (GMT). No. of bitstreams: 1 ijimai_5_4_11_pdf_83808.pdf: 632376 bytes, checksum: efb27c6187a8d60ef1ab8f73f00fdaf5 (MD5)

Evaluation of a Diagnostic Decision Support System for the Triage of Patients in a Hospital Emergency Department

Evaluation of a Diagnostic Decision Support System for the Triage of Patients in a Hospital Emergency Department Seara, Germán; Mayol, Julio; Nazario Arancibia, JC; Martín Sanchez, FJ; Del rey Mejías, AL; del Gonzalez Castillo, J; Chafer Vilaplana, J; García Briñon, MA; Suárez-Cadenas, MM One of the biggest challenges for the management of the emergency department (ED) is to expedite the management of patients since their arrival for those with low priority pathologies selected by the classification systems, generating unnecessary saturation of the ED. Diagnostic decision support systems (DDSS) can be a powerful tool to guide diagnosis, facilitate correct classification and improve patient safety. Patients who attended the ED of a tertiary hospital with the preconditions of Manchester Triage system level of low priority (levels 3, 4 and 5), and with one of the five most frequent causes for consultation: dyspnea, chest pain, gastrointestinal bleeding, general discomfort and abdominal pain, were interviewed by an independent researcher with a DDSS, the Mediktor system. After the interview, we compare the Manchester triage and the final diagnoses made by the ED with the triage and diagnostic possibilities ordered by probability obtained by the Mediktor system, respectively. In a final sample of 214 patients, the urgency assignment made by both systems does not match exactly, which could indicate a different classification model, but there were no statistically significant differences between the assigned levels (S = 0.059, p = 0.442). The diagnostic accuracy between the final diagnosis and any of the first 10 Mediktor diagnoses was of 76.5%, for the first five diagnoses was 65.4%, for the first three diagnoses was 58%, and the exact match with the first diagnosis was 37.9%. The classification of Mediktor in this segment of patients shows that a higher level of severity corresponds to a greater number of hospital admissions, hospital readmissions and emergency screenings at 30 days, although without statistical significance. It is expected that this type of applications may be useful as a complement to the triage, to accelerate the diagnostic approach, to improve the request for appropriate complementary tests in a protocolized action model and to reduce waiting times in the ED. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-21T08:43:47Z No. of bitstreams: 1 ijimai_5_4_7_pdf_43667.pdf: 526584 bytes, checksum: 4040beea73b719a1ee00a981e5a8a6de (MD5); Made available in DSpace on 2022-02-21T08:43:47Z (GMT). No. of bitstreams: 1 ijimai_5_4_7_pdf_43667.pdf: 526584 bytes, checksum: 4040beea73b719a1ee00a981e5a8a6de (MD5)

Sales Prediction through Neural Networks for a Small Dataset

Sales Prediction through Neural Networks for a Small Dataset Cantón Croda, Rosa María; Gibaja Romero, Damián Emilio; Caballero Morales, Santiago Omar Sales forecasting allows firms to plan their production outputs, which contributes to optimizing firms' inventory management via a cost reduction. However, not all firms have the same capacity to store all the necessary information through time. So, time-series with a short length are common within industries, and problems arise due to small time series does not fully capture sales' behavior. In this paper, we show the applicability of neural networks in a case where a company reports a short time-series given the changes in its warehouse structure. Given the neural networks independence form statistical assumptions, we use a multilayer-perceptron to get the sales forecasting of this enterprise. We find that learning rates variations do not significantly increase the computing time, and the validation fails with an error minor to five percent. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T12:04:11Z No. of bitstreams: 1 ijimai_5_4_4_pdf_17317.pdf: 559885 bytes, checksum: 2179d8c307eff4a29cfaa1b1f4482f18 (MD5); Made available in DSpace on 2022-02-14T12:04:11Z (GMT). No. of bitstreams: 1 ijimai_5_4_4_pdf_17317.pdf: 559885 bytes, checksum: 2179d8c307eff4a29cfaa1b1f4482f18 (MD5)

Biomedical Term Extraction: NLP Techniques in Computational Medicine

Biomedical Term Extraction: NLP Techniques in Computational Medicine Redondo, Teófilo; Díaz, Julia; Moreno Sandoval, Antonio; Campillos Llanos, Leonardo Artificial Intelligence (AI) and its branch Natural Language Processing (NLP) in particular are main contributors to recent advances in classifying documentation and extracting information from assorted fields, Medicine being one that has gathered a lot of attention due to the amount of information generated in public professional journals and other means of communication within the medical profession. The typical information extraction task from technical texts is performed via an automatic term recognition extractor. Automatic Term Recognition (ATR) from technical texts is applied for the identification of key concepts for information retrieval and, secondarily, for machine translation. Term recognition depends on the subject domain and the lexical patterns of a given language, in our case, Spanish, Arabic and Japanese. In this article, we present the methods and techniques for creating a biomedical corpus of validated terms, with several tools for optimal exploitation of the information therewith contained in said corpus. This paper also shows how these techniques and tools have been used in a prototype. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T11:15:51Z No. of bitstreams: 1 ijimai_5_4_6_pdf_46381.pdf: 515240 bytes, checksum: be9566029f2b44cefc55c806875b64c7 (MD5); Made available in DSpace on 2022-02-14T11:15:51Z (GMT). No. of bitstreams: 1 ijimai_5_4_6_pdf_46381.pdf: 515240 bytes, checksum: be9566029f2b44cefc55c806875b64c7 (MD5)

A Review of Artificial Intelligence in the Internet of Things

A Review of Artificial Intelligence in the Internet of Things González García, Cristian; Núñez-Valdez, Edward; García-Díaz, Vicente; Pelayo García-Bustelo, B. Cristina; Cueva-Lovelle, Juan Manuel Humankind has the ability of learning new things automatically due to the capacities with which we were born. We simply need to have experiences, read, study… live. For these processes, we are capable of acquiring new abilities or modifying those we already have. Another ability we possess is the faculty of thinking, imagine, create our own ideas, and dream. Nevertheless, what occurs when we extrapolate this to machines? Machines can learn. We can teach them. In the last years, considerable advances have been done and we have seen cars that can recognise pedestrians or other cars, systems that distinguish animals, and even, how some artificial intelligences have been able to dream, paint, and compose music by themselves. Despite this, the doubt is the following: Can machines think? Or, in other words, could a machine which is talking to a person and is situated in another room make them believe they are talking with another human? This is a doubt that has been present since Alan Mathison Turing contemplated it and it has not been resolved yet. In this article, we will show the beginnings of what is known as Artificial Intelligence and some branches of it such as Machine Learning, Computer Vision, Fuzzy Logic, and Natural Language Processing. We will talk about each of them, their concepts, how they work, and the related work on the Internet of Things fields. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T11:04:32Z No. of bitstreams: 1 ijimai_5_4_1_pdf_27055.pdf: 579397 bytes, checksum: 8e97461f7808e8189d461fdfbf3b6ca0 (MD5); Made available in DSpace on 2022-02-14T11:04:32Z (GMT). No. of bitstreams: 1 ijimai_5_4_1_pdf_27055.pdf: 579397 bytes, checksum: 8e97461f7808e8189d461fdfbf3b6ca0 (MD5)

A Fuzzy Linguistic RFM Model Applied to Campaign Management

A Fuzzy Linguistic RFM Model Applied to Campaign Management Herrera-Viedma, Enrique; Carrasco, Ramón Alberto; Blasco, María Francisca; García-Madariaga, Jesús In the literature there are some proposals for integrated schemes for campaign management based on segmentation from the results of the RFM model. RFM is a technique used to analyze customer behavior by means of three variables: Recency, Frequency and Monetary value. It is s very much in use in the business world due to its simplicity of use, implementation and interpretability of its results. However, RFM applications to campaign management present known limitations like the lack of precision because the scores of these variables are expressed by an ordinal scale. In this paper, we propose to link customer segmentation methods with campaign activities in a more effective way incorporating the 2–tuple model both to the RFM calculation process and to its subsequent exploitation by means of segmentation algorithms, specifically, k-means. This yields a greater interpretability of these results and also allows computing these values without loss of information. Therefore, marketers can effectively develop more effective marketing strategy. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T10:37:54Z No. of bitstreams: 1 ijimai_5_4_2_pdf_37693.pdf: 785885 bytes, checksum: 28aa5dd80e319abd1fbe056954d1bc2d (MD5); Made available in DSpace on 2022-02-14T10:37:54Z (GMT). No. of bitstreams: 1 ijimai_5_4_2_pdf_37693.pdf: 785885 bytes, checksum: 28aa5dd80e319abd1fbe056954d1bc2d (MD5)

Are Instructed Emotional States Suitable for Classification? Demonstration of How They Can Significantly Influence the Classification Result in An Automated Recognition System

Are Instructed Emotional States Suitable for Classification? Demonstration of How They Can Significantly Influence the Classification Result in An Automated Recognition System Magdin, Martin; Prikler, F At the present time, various freely available or commercial solutions are used to classify the subject's emotional state. Classification of the emotional state helps us to understand how the subject feels and what he is experiencing in a particular situation. Classification of the emotional state can thus be used in various areas of our life from neuromarketing, through the automotive industry (determining how emotions affect driving), to implementing such a system into the learning process. The learning process, which is the (mutual) interaction between the teacher and the learner, is an interesting area in which individual emotional states can be explored. In this pedagogical-psychological area several research studies were realized. These studies in some cases demonstrated the important impact of the emotional state on the results of the students. However, for comparison and unambiguous classification of the emotional state most of these studies used the instructed (even constructed) stereotypical facial expressions of the most well-known test databases (Jaffe is a typical example). Such facial expressions are highly standardized, and the software can recognize them with a fairly big percentage, but this does not necessarily point to the actual success rate of the subject's emotional classification in such a test because the similarity to real emotional expression remains unknown. Therefore, we examined facial expressions in real situations. We have subsequently compared these examined facial expressions with the instructed expressions of the same emotions (the Jaffe database). The overall average classification score in real facial expressions was 94.58%. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T10:32:31Z No. of bitstreams: 1 ijimai_5_4_16_pdf_15374.pdf: 655849 bytes, checksum: 6022b34cc54c84517ded043763d9162e (MD5); Made available in DSpace on 2022-02-14T10:32:31Z (GMT). No. of bitstreams: 1 ijimai_5_4_16_pdf_15374.pdf: 655849 bytes, checksum: 6022b34cc54c84517ded043763d9162e (MD5)

Multimodal Generic Framework for Multimedia Documents Adaptation

Multimodal Generic Framework for Multimedia Documents Adaptation Bahaj, Mohamed; Khallouki, Hajar Today, people are increasingly capable of creating and sharing documents (which generally are multimedia oriented) via the internet. These multimedia documents can be accessed at anytime and anywhere (city, home, etc.) on a wide variety of devices, such as laptops, tablets and smartphones. The heterogeneity of devices and user preferences has raised a serious issue for multimedia contents adaptation. Our research focuses on multimedia documents adaptation with a strong focus on interaction with users and exploration of multimodality. We propose a multimodal framework for adapting multimedia documents based on a distributed implementation of W3C’s Multimodal Architecture and Interfaces applied to ubiquitous computing. The core of our proposed architecture is the presence of a smart interaction manager that accepts context related information from sensors in the environment as well as from other sources, including information available on the web and multimodal user inputs. The interaction manager integrates and reasons over this information to predict the user’s situation and service use. A key to realizing this framework is the use of an ontology that undergirds the communication and representation, and the use of the cloud to insure the service continuity on heterogeneous mobile devices. Smart city is assumed as the reference scenario. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T10:03:53Z No. of bitstreams: 1 ijimai_5_4_14_pdf_17167.pdf: 606586 bytes, checksum: 01d64e19657f9c8b4ec47e294f475cb6 (MD5); Made available in DSpace on 2022-02-14T10:03:53Z (GMT). No. of bitstreams: 1 ijimai_5_4_14_pdf_17167.pdf: 606586 bytes, checksum: 01d64e19657f9c8b4ec47e294f475cb6 (MD5)

QoS based Web Service Selection and Multi-Criteria Decision Making Methods

QoS based Web Service Selection and Multi-Criteria Decision Making Methods Bagga, Pallavi; Hans, Rahul; Joshi, Aarchit With the continuing proliferation of web services offering similar efficacies, around the globe, it has become a challenge for a user to select the best web service. In literature, this challenge is exhibited as a 0-1 knapsack problem of multiple dimensions and multiple choices, known as an NP-hard problem. Multi-Criteria Decision Making (MCDM) method is one of the ways which suits this problem and helps the users to select the best service based on his/her preferences. In this regard, this paper assists the researchers in two conducts: Firstly, to witness the performance of different MCDM methods for large number of alternatives and attributes. Secondly, to perceive the possible deviation in the ranking obtained from these methods. For carrying out the experimental evaluation, in this paper, five different well-known MCDM methods have been implemented and compared over two different scenarios of 50 as well as 100 web services, where their ranking is defined on an account of several Quality of Service (QoS) parameters. Additionally, a Spearman’s Rank Correlation Coefficient has been calculated for different pairs of MCDM methods in order to provide a clear depiction of MCDM methods showing the least deviation in their ranking. The experimental results comfort web service users in conquering an appropriate decision on the selection of suitable service. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T09:58:20Z No. of bitstreams: 1 ijimai_5_4_13_pdf_11750.pdf: 552495 bytes, checksum: c23853ef8ddd26a53c1c71ea8df858fb (MD5); Made available in DSpace on 2022-02-14T09:58:20Z (GMT). No. of bitstreams: 1 ijimai_5_4_13_pdf_11750.pdf: 552495 bytes, checksum: c23853ef8ddd26a53c1c71ea8df858fb (MD5)

Hybrid Algorithm for Solving the Quadratic Assignment Problem

Hybrid Algorithm for Solving the Quadratic Assignment Problem Sayoti, Fatima; Riffi, Mohammed Essaid The Quadratic Assignment Problem (QAP) is a combinatorial optimization problem; it belongs to the class of NP-hard problems. This problem is applied in various fields such as hospital layout, scheduling parallel production lines and analyzing chemical reactions for organic compounds. In this paper we propose an application of Golden Ball algorithm mixed with Simulated Annealing (GBSA) to solve QAP. This algorithm is based on different concepts of football. The simulated annealing search can be blocked in a local optimum due to the unacceptable movements; our proposed strategy guides the simulated annealing search to escape from the local optima and to explore in an efficient way the search space. To validate the proposed approach, numerous simulations were conducted on 64 instances of QAPLIB to compare GBSA with existing algorithms in the literature of QAP. The obtained numerical results show that the GBSA produces optimal solutions in reasonable time; it has the better computational time. This work demonstrates that our proposed adaptation is effective in solving the quadratic assignment problem. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T08:43:12Z No. of bitstreams: 1 ijimai_5_4_8_pdf_86744.pdf: 495397 bytes, checksum: 774b6eead3deafdd23d9dd2d5b46a3bd (MD5); Made available in DSpace on 2022-02-14T08:43:12Z (GMT). No. of bitstreams: 1 ijimai_5_4_8_pdf_86744.pdf: 495397 bytes, checksum: 774b6eead3deafdd23d9dd2d5b46a3bd (MD5)

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT Naz, Saima; Ziauddin, Sheikh; Shahid, Ahmad Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-14T08:24:18Z No. of bitstreams: 1 ijimai_5_4_10_pdf_16089.pdf: 620274 bytes, checksum: 3f68d042a3f7f7adadf0833cc8c7f09a (MD5); Made available in DSpace on 2022-02-14T08:24:18Z (GMT). No. of bitstreams: 1 ijimai_5_4_10_pdf_16089.pdf: 620274 bytes, checksum: 3f68d042a3f7f7adadf0833cc8c7f09a (MD5)

IJIMAI Editor's Note - Vol. 5 Issue 3 - 10th Anniversary

IJIMAI Editor's Note - Vol. 5 Issue 3 - 10th Anniversary González-Crespo, Rubén The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on AI tools or tools that use AI with interactive multimedia techniques. This was the first phrase that appeared into the website of the journal, whose launching had several motivations. First, IJIMAI was established on December 2008 in response to several agents, such as students, teachers, researchers, primarily in Spain and Colombia, who wanted to increase the impact of science in their environment. Second, IJIMAI was established to increase the number of scientific journals developed in Spain into the scope of Artificial Intelligence and Interactive Multimedia; there are very few journals about these topics in our country. Third, since the beginning we believed into an open access project, open for the whole stakeholders. Currently no money is needed to public a contribution in IJIMAI, and no money is needed to read all papers in IJIMAI as well; science should be open to achieve the maximum dissemination of knowledge. Finally, IJIMAI was established with the hope of being a long-term project; this 10th anniversary allows us to affirm that this goal is getting closer. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-09T10:46:34Z No. of bitstreams: 1 ijimai_5_3_0_pdf_26965.pdf: 596339 bytes, checksum: 25d97b9795c2fd2fe53032acc8a94a73 (MD5); Made available in DSpace on 2022-02-09T10:46:34Z (GMT). No. of bitstreams: 1 ijimai_5_3_0_pdf_26965.pdf: 596339 bytes, checksum: 25d97b9795c2fd2fe53032acc8a94a73 (MD5)

Happiness and Technology: Special Consideration of Digital Technology and Internet

Happiness and Technology: Special Consideration of Digital Technology and Internet Mochón, Francisco This research paper can be considered a survey about the impact of technology in happiness. The article points out that the scientific approach of happiness states that happiness can be measured and explanatory factors of well-being must be searched empirically. The analysis of technology impact on happiness starts with the opinion of philosophers and social thinkers, and then focus on the revision of empirical research works. The paper concludes highlighting that technology, being the motor of economic well-being, has positive and negative effects on the subjective well-being of individuals. Therefore it is essential to undertake an adequate regulation that promotes positive effects and mitigates the possible harm. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-09T10:43:03Z No. of bitstreams: 1 ijimai_5_3_17_pdf_20180.pdf: 338634 bytes, checksum: 88e818f346a2150a5f65465f1458f6c6 (MD5); Made available in DSpace on 2022-02-09T10:43:03Z (GMT). No. of bitstreams: 1 ijimai_5_3_17_pdf_20180.pdf: 338634 bytes, checksum: 88e818f346a2150a5f65465f1458f6c6 (MD5)

Revisiting “Recognizing Human Activities User- Independently on Smartphones Based on Accelerometer Data” – What Has Happened Since 2012?

Revisiting “Recognizing Human Activities User- Independently on Smartphones Based on Accelerometer Data” – What Has Happened Since 2012? Siirtola, Pekka; Röning, Juha Our article “Recognizing human activities user-independently on smartphones based on accelerometer data” was published in the International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) in 2012. In 2018, it was selected as the most outstanding article published in the 10 years of IJIMAI life. To celebrate the 10th anniversary of IJIMAI, in this article we will introduce what has happened in the field of human activity recognition and wearable sensor-based recognition since 2012, and especially, this article concentrates on introducing our work since 2012. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-09T09:41:13Z No. of bitstreams: 1 ijimai_5_3_2_pdf_84569.pdf: 328418 bytes, checksum: a2a55465ccb2a7260218d5aa73016f97 (MD5); Made available in DSpace on 2022-02-09T09:41:13Z (GMT). No. of bitstreams: 1 ijimai_5_3_2_pdf_84569.pdf: 328418 bytes, checksum: a2a55465ccb2a7260218d5aa73016f97 (MD5)

A Bibliometric Overview of the International Journal of Interactive Multimedia and Artificial Intelligence

A Bibliometric Overview of the International Journal of Interactive Multimedia and Artificial Intelligence Herrera-Viedma, Enrique; Baier-Fuentes, Hugo; Cascón-Katchadourian, Jesús; Merigó, José; Martínez, M A The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) published its first issue ten years ago. Currently, IJIMAI is indexed in the important database Emerging Sources Citation Index. This paper aims to identify, through a mapping of science, those most relevant aspects of the structure of publications made during the first 10 years of IJIMAI. Using VOSviewer software, the structural maps of the IJIMAI publications are analysed according to techniques such as bibliographic coupling, co-citations and cooccurrence of keywords. In addition, the evolution of the publications, citations and an analysis of the most cited papers of the journal are presented. The results show that IJIMAI has experienced a remarkable growth of both publications and citations in the last five years. We also observe that IJIMAI does not only capture the attention of the Spanish scientific community, but also of emerging countries such as India and Iran and emerging Latin American countries such as Colombia. With a such increasing behaviour, it is expected in the coming years that IJIMAI will position itself among the best journals with similar scientific scope. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-09T09:36:10Z No. of bitstreams: 1 ijimai_5_3_1_pdf_85989.pdf: 1061000 bytes, checksum: 2c17bdcc203f05d2e3abe995878dd013 (MD5); Made available in DSpace on 2022-02-09T09:36:10Z (GMT). No. of bitstreams: 1 ijimai_5_3_1_pdf_85989.pdf: 1061000 bytes, checksum: 2c17bdcc203f05d2e3abe995878dd013 (MD5)

TV Series and Social Media: Powerful Engagement Factors in Mobile Video Games

TV Series and Social Media: Powerful Engagement Factors in Mobile Video Games Saez, Yago; Mochón, Asunción; Rada, Fernando The free-to-play business model has become hegemonic in the mobile video game industry, displacing the traditional paid content model that was the norm until the appearance of manufacturers’ app stores. Companies attempt to monetize these games by means of in-game micro-transactions and in-game advertising; thus, it is essential to acquire an enormous number of users because only a small percentage will ultimately make any purchases. To keep players engaged, companies typically put in place marketing and design strategies derived from behavioral telemetry, to maintain a grip on players. We propose an innovative approach, focusing our attention on the impact of having a video game based on a famous TV series. Furthermore, we analyze the effect of social networks on game metrics. The outcome indicates that developing a game based on a TV series and integrating social media with the gameplay improve and reinforce the user’s activation, retention and monetization. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-09T09:18:20Z No. of bitstreams: 1 ijimai_5_3_5_pdf_14724.pdf: 1297779 bytes, checksum: cad83f8812d4beeeea0d7e9933cbc79f (MD5); Made available in DSpace on 2022-02-09T09:18:20Z (GMT). No. of bitstreams: 1 ijimai_5_3_5_pdf_14724.pdf: 1297779 bytes, checksum: cad83f8812d4beeeea0d7e9933cbc79f (MD5)

Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System

Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System Ahmed, Walaa; Selim, Ali; Kamel, Salah; Yu, Juan; Jurado, Francisco This paper proposes a solution procedure for probabilistic load flow problem considering the optimal allocation of Static Var Compensator (SVC) in radial distribution systems. Pareto Envelope-based Selection Algorithm II (PESA-II) with fuzzy logic decision maker is developed to determine the optimal location and size of SVC based on the minimum total power losses and Voltage Deviation (VD). Combined cumulants and gram-chalier expansion are used for solving the probabilistic load flow problem. The proposed algorithm is tested on 33-bus and 69-bus distribution systems. The developed algorithm gives an acceptable solution with low number of iterations and less computation cost compared with the Monte Carlo method. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-08T12:40:16Z No. of bitstreams: 1 ijimai_5_3_16_pdf_17977.pdf: 3057266 bytes, checksum: 8c56d7eb3ab2aaed0fe88cfab17ff4d6 (MD5); Made available in DSpace on 2022-02-08T12:40:16Z (GMT). No. of bitstreams: 1 ijimai_5_3_16_pdf_17977.pdf: 3057266 bytes, checksum: 8c56d7eb3ab2aaed0fe88cfab17ff4d6 (MD5)

Issues of Visual Search Methods in Digital Repositories

Issues of Visual Search Methods in Digital Repositories Montenegro-Marin, Carlos Enrique; Gaona-García, Paulo Alonso; Gaona-García, Elvis; Gómez-Acosta, Adriana; Hassan-Montero, Yusef Repositories are important infrastructures which allow the dissemination of large collections of digital resources hosted in museums, libraries, academic institutions or specialized documentation centers. However, there are nowadays several limitations associated with irrelevant search results based on a knowledge area. Some studies have highlighted the major role of information visualization strategies based on Simple Knowledge Organization Systems (SKOS) so as to mitigate such difficulties. The main goal of this article is to present recommendations using information visualization based on SKOS for the development of navigational search interfaces in digital repositories focused on learning process. We use card sorting as methodology in order to obtain qualitative results in our study. As preliminary results we found that taxonomies in visual search engines improve the access to large collections of digital resources based on SKOS, but it depends on the design of taxonomy concepts defined in digital repositories. Finally, it is recommended that the creators of repositories focus their efforts on define levels of relationship and partnership between digital resources using knowledge representation structures like thesauri or ontologies; work with usable visualization interfaces like tree, radial or icicle; and link relevant metadata fields with the navigation structure. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-08T12:06:20Z No. of bitstreams: 1 ijimai_5_3_10_pdf_37318.pdf: 674709 bytes, checksum: 1b9ac59164b0d5f68754361767d2d031 (MD5); Made available in DSpace on 2022-02-08T12:06:20Z (GMT). No. of bitstreams: 1 ijimai_5_3_10_pdf_37318.pdf: 674709 bytes, checksum: 1b9ac59164b0d5f68754361767d2d031 (MD5)

Building Phrase Polarity Lexicons for Sentiment Analysis

Building Phrase Polarity Lexicons for Sentiment Analysis Dehkharghani, Rahim Many approaches to sentiment analysis benefit from polarity lexicons. Most polarity lexicons include a list of polar (positive/negative) words, and sentiment analysis systems attempt to capture the occurrence of those words in text using polarity lexicons. Although there exist some polarity lexicons in many natural languages, most languages suffer from the lack of phrase polarity lexicons. Phrases play an important role in sentiment analysis because the polarity of a phrase cannot always be estimated based on the polarity of its parts. In this work, a hybrid approach is proposed for building phrase polarity lexicons which is experimented on Turkish as a low-resource language. The obtained classification accuracies in extracting and classifying phrases as positive, negative, or neutral, approve the effectiveness of the proposed methodology. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-08T11:54:17Z No. of bitstreams: 1 ijimai_5_3_11_pdf_18912.pdf: 485408 bytes, checksum: b1fbd19c77ee48985d4581e5deb4f0a5 (MD5); Made available in DSpace on 2022-02-08T11:54:17Z (GMT). No. of bitstreams: 1 ijimai_5_3_11_pdf_18912.pdf: 485408 bytes, checksum: b1fbd19c77ee48985d4581e5deb4f0a5 (MD5)

Removing Unclassified Hand Tremor Motion from Computer Mouse Input with Neural Networks

Removing Unclassified Hand Tremor Motion from Computer Mouse Input with Neural Networks Mack, Stephen An artificial neural network based filter to remove unwanted tremor-induced motion in computer mouse input is presented and tested. A method to efficiently capture appropriate training data is shown to be important in the operation and training of the neural network filter. The architecture of the neural network as well as the numerous design choices are presented and explained. A simulation study proves the artificial neural network is successful at removing a simulated Parkinson’s tremor from computer mouse movements even with minimal training data. Resulting tremor-free motion estimated by the artificial neural network is shown to be similar to normal tremor free computer mouse movements. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-08T11:47:34Z No. of bitstreams: 1 ijimai_5_3_6_pdf_59445.pdf: 684059 bytes, checksum: c2a118308d8a9c0ba653511a2c51e4a4 (MD5); Made available in DSpace on 2022-02-08T11:47:34Z (GMT). No. of bitstreams: 1 ijimai_5_3_6_pdf_59445.pdf: 684059 bytes, checksum: c2a118308d8a9c0ba653511a2c51e4a4 (MD5)

Generation of Two-Voice Imitative Counterpoint from Statistical Models

Generation of Two-Voice Imitative Counterpoint from Statistical Models Padilla, Victor; Conklin, Darrell Generating new music based on rules of counterpoint has been deeply studied in music informatics. In this article, we try to go further, exploring a method for generating new music based on the style of Palestrina, based on combining statistical generation and pattern discovery. A template piece is used for pattern discovery, and the patterns are selected and organized according to a probabilistic distribution, using horizontal viewpoints to describe melodic properties of events. Once the template is covered with patterns, two-voice counterpoint in a florid style is generated into those patterns using a first-order Markov model. The template method solves the problem of coherence and imitation never addressed before in previous research in counterpoint music generation. For constructing the Markov model, vertical slices of pitch and rhythm are compiled over a large corpus of dyads from Palestrina masses. The template enforces different restrictions that filter the possible paths through the generation process. A double backtracking algorithm is implemented to handle cases where no solutions are found at some point within a generation path. Results are evaluated by both information content and listener evaluation, and the paper concludes with a proposed relationship between musical quality and information content. Part of this research has been presented at SMC 2016 in Hamburg, Germany. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-08T09:57:27Z No. of bitstreams: 1 ijimai_5_3_3_pdf_45204.pdf: 1410400 bytes, checksum: a0040082304e458d158f5b9e8ca4d839 (MD5); Made available in DSpace on 2022-02-08T09:57:27Z (GMT). No. of bitstreams: 1 ijimai_5_3_3_pdf_45204.pdf: 1410400 bytes, checksum: a0040082304e458d158f5b9e8ca4d839 (MD5)

Exploratory Boosted Feature Selection and Neural Network Framework for Depression Classification

Exploratory Boosted Feature Selection and Neural Network Framework for Depression Classification Arun, Vanishri; Krishna, Murali; Arunkumar, B V; Padma, S K; Shyam Depression is a burdensome psychiatric disease common in low and middle income countries causing disability, morbidity and mortality in late life. In this study, we demonstrate a novel approach for detection of depression using clinical data obtained from the on-going Mysore Studies of Natal effects on Ageing and Health (MYNAH), in South India where the members have undergone a comprehensive assessment for cognitive function, mental health and cardiometabolic disorders. The proposed model is developed using machine learning approach for classification of depression using Meta-Cognitive Neural Network (McNN) classifier with Projection-based learning (PBL) to address the self-regulating principles like how, what and when to learn. XGBoost is used for feature selection on the available data of assessments with improved confidence. To improve the efficiency of McNN-PBL classifier the best parameters are found using Particle Swarm Optimization (PSO) algorithm. The results indicate that the McNNPBL classifier selects appropriate records to learn and remove repetitive records which improve the generalization performance. The study helps the clinician to identify the best parameters to analyze the patient. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-08T09:49:25Z No. of bitstreams: 1 ijimai_5_3_7_pdf_21185.pdf: 1282461 bytes, checksum: ffe57e53c76199c50e41d46785f307dd (MD5); Made available in DSpace on 2022-02-08T09:49:25Z (GMT). No. of bitstreams: 1 ijimai_5_3_7_pdf_21185.pdf: 1282461 bytes, checksum: ffe57e53c76199c50e41d46785f307dd (MD5)

EVEN-VE: Eyes Visibility Based Egocentric Navigation for Virtual Environments

EVEN-VE: Eyes Visibility Based Egocentric Navigation for Virtual Environments Ullah, S; Raees, M Navigation is one of the 3D interactions often needed to interact with a synthetic world. The latest advancements in image processing have made possible gesture based interaction with a virtual world. However, the speed with which a 3D virtual world responds to a user’s gesture is far greater than posing of the gesture itself. To incorporate faster and natural postures in the realm of Virtual Environment (VE), this paper presents a novel eyes-based interaction technique for navigation and panning. Dynamic wavering and positioning of eyes are deemed as interaction instructions by the system. The opening of eyes preceded by closing for a distinct time-threshold, activates forward or backward navigation. Supporting 2-Degree of Freedom head’s gestures (Rolling and Pitching) panning is performed over the xy-plane. The proposed technique was implemented in a case-study project; EWI (Eyes Wavering based Interaction). With EWI, real time detection and tracking of eyes are performed by the libraries of OpenCV at the backend. To interactively follow trajectory of both the eyes, dynamic mapping is performed in OpenGL. The technique was evaluated in two separate sessions by a total of 28 users to assess accuracy, speed and suitability of the system in Virtual Reality (VR). Using an ordinary camera, an average accuracy of 91% was achieved. However, assessment made by using a high quality camera testified that accuracy of the system could be raised to a higher level besides increase in navigation speed. Results of the unbiased statistical evaluations suggest/demonstrate applicability of the system in the emerging domains of virtual and augmented realities. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-07T12:47:53Z No. of bitstreams: 1 ijimai_5_3_15_pdf_15422.pdf: 2933417 bytes, checksum: f84208a4e51cfc13b5d6ad91bff34889 (MD5); Made available in DSpace on 2022-02-07T12:47:53Z (GMT). No. of bitstreams: 1 ijimai_5_3_15_pdf_15422.pdf: 2933417 bytes, checksum: f84208a4e51cfc13b5d6ad91bff34889 (MD5)

An Evolutionary Approach for Learning Opponent's Deadline and Reserve Points in Multi-Issue Negotiation

An Evolutionary Approach for Learning Opponent's Deadline and Reserve Points in Multi-Issue Negotiation Ayachi, R; Bouhani, H; Amor, Ben The efficiency of automated multi-issue negotiation depends on the available information about the opponent. In a competitive negotiation environment, agents do not reveal their parameters to their opponents in order to avoid exploitation. Several researchers have argued that an agent's optimal strategy can be determined using the opponent's deadline and reserve points. In this paper, we propose a new learning agent, so-called Evolutionary Learning Agent (ELA), able to estimate its opponent's deadline and reserve points in bilateral multi-issue negotiation based on opponent's counter-offers (without any additional extra information). ELA reduces the learning problem to a system of non-linear equations and uses an evolutionary algorithm based on the elitism aspect to solve it. Experimental study shows that our learning agent outperforms others agents by improving its outcome in term of average and joint utility. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-07T12:32:12Z No. of bitstreams: 1 ijimai_5_3_14_pdf_17519.pdf: 3829818 bytes, checksum: 715108699f04338d7f5a1c69bd265bb0 (MD5); Made available in DSpace on 2022-02-07T12:32:12Z (GMT). No. of bitstreams: 1 ijimai_5_3_14_pdf_17519.pdf: 3829818 bytes, checksum: 715108699f04338d7f5a1c69bd265bb0 (MD5)

An Adapted Approach for User Profiling in a Recommendation System: Application to Industrial Diagnosis

An Adapted Approach for User Profiling in a Recommendation System: Application to Industrial Diagnosis Taghezout, Noria; Benkaddour, Fatima Zohra; Kaddour-Ahmed, Fatima Zahra; Hammadi, Ilyes-Ahmed In this paper, we propose a global architecture of a recommender tool, which represents a part of an existing collaborative platform. This tool provides diagnostic documents for industrial operators. The recommendation process considered here is composed of three steps: Collecting and filtering information; Prediction or recommendation step; evaluating and improvement. In this work, we focus on collecting and filtering step. We mainly use information result from collaborative sessions and documents describing solutions that are attributed to the complex diagnostic problems. The developed tool is based on collaborative filtering that operates on users' preferences and similar responses. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-07T12:14:44Z No. of bitstreams: 1 ijimai_5_3_13_pdf_15716.pdf: 1421174 bytes, checksum: 0d4769f6e02af7d97de27c05050ed29c (MD5); Made available in DSpace on 2022-02-07T12:14:44Z (GMT). No. of bitstreams: 1 ijimai_5_3_13_pdf_15716.pdf: 1421174 bytes, checksum: 0d4769f6e02af7d97de27c05050ed29c (MD5)

Spatial Sound Rendering – A Survey

Spatial Sound Rendering – A Survey Lakka, Eftychia; Malamos, Athanasios; Pavlakis, K G; Ware, J A Simulating propagation of sound and audio rendering can improve the sense of realism and the immersion both in complex acoustic environments and dynamic virtual scenes. In studies of sound auralization, the focus has always been on room acoustics modeling, but most of the same methods are also applicable in the construction of virtual environments such as those developed to facilitate computer gaming, cognitive research, and simulated training scenarios. This paper is a review of state-of-the-art techniques that are based on acoustic principles that apply not only to real rooms but also in 3D virtual environments. The paper also highlights the need to expand the field of immersive sound in a web based browsing environment, because, despite the interest and many benefits, few developments seem to have taken place within this context. Moreover, the paper includes a list of the most effective algorithms used for modelling spatial sound propagation and reports their advantages and disadvantages. Finally, the paper emphasizes in the evaluation of these proposed works. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-07T11:47:35Z No. of bitstreams: 1 ijimai_5_3_4_pdf_11151.pdf: 786507 bytes, checksum: a753ff8f45ce544461f19ee7253b0d24 (MD5); Made available in DSpace on 2022-02-07T11:47:35Z (GMT). No. of bitstreams: 1 ijimai_5_3_4_pdf_11151.pdf: 786507 bytes, checksum: a753ff8f45ce544461f19ee7253b0d24 (MD5)

A New Feature Selection Method based on Intuitionistic Fuzzy Entropy to Categorize Text Documents

A New Feature Selection Method based on Intuitionistic Fuzzy Entropy to Categorize Text Documents Harish, B S; Revanasiddappa, M B Selection of highly discriminative feature in text document plays a major challenging role in categorization. Feature selection is an important task that involves dimensionality reduction of feature matrix, which in turn enhances the performance of categorization. This article presents a new feature selection method based on Intuitionistic Fuzzy Entropy (IFE) for Text Categorization. Firstly, Intuitionistic Fuzzy C-Means (IFCM) clustering method is employed to compute the intuitionistic membership values. The computed intuitionistic membership values are used to estimate intuitionistic fuzzy entropy via Match degree. Further, features with lower entropy values are selected to categorize the text documents. To find the efficacy of the proposed method, experiments are conducted on three standard benchmark datasets using three classifiers. F-measure is used to assess the performance of the classifiers. The proposed method shows impressive results as compared to other well known feature selection methods. Moreover, Intuitionistic Fuzzy Set (IFS) property addresses the uncertainty limitations of traditional fuzzy set. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-04T11:40:18Z No. of bitstreams: 1 ijimai_5_3_12_pdf_16348.pdf: 800936 bytes, checksum: d27513b5348efe9f252502b1eec83fc8 (MD5); Made available in DSpace on 2022-02-04T11:40:18Z (GMT). No. of bitstreams: 1 ijimai_5_3_12_pdf_16348.pdf: 800936 bytes, checksum: d27513b5348efe9f252502b1eec83fc8 (MD5)

Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic

Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic Atmani, Baghdad; Benamina, Mohammed; Benbelkacem, Sofia In the medical field, experts’ knowledge is based on experience, theoretical knowledge and rules. Case-based reasoning is a problem-solving paradigm which is based on past experiences. For this purpose, a large number of decision support applications based on CBR have been developed. Cases retrieval is often considered as the most important step of case-based reasoning. In this article, we integrate fuzzy logic and data mining to improve the response time and the accuracy of the retrieval of similar cases. The proposed Fuzzy CBR is composed of two complementary parts; the part of classification by fuzzy decision tree realized by Fispro and the part of case-based reasoning realized by the platform JColibri. The use of fuzzy logic aims to reduce the complexity of calculating the degree of similarity that can exist between diabetic patients who require different monitoring plans. The results of the proposed approach are compared with earlier methods using accuracy as metrics. The experimental results indicate that the fuzzy decision tree is very effective in improving the accuracy for diabetes classification and hence improving the retrieval step of CBR reasoning. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-02-04T11:22:40Z No. of bitstreams: 1 ijimai_5_3_8_pdf_20127.pdf: 1373431 bytes, checksum: da8ff945f19d64fe5b50f5816dc868f5 (MD5); Made available in DSpace on 2022-02-04T11:22:41Z (GMT). No. of bitstreams: 1 ijimai_5_3_8_pdf_20127.pdf: 1373431 bytes, checksum: da8ff945f19d64fe5b50f5816dc868f5 (MD5)

QAM-DWT-SVD Based Watermarking Scheme for Medical Images

QAM-DWT-SVD Based Watermarking Scheme for Medical Images Ayad, Habib; Khalil, Mohammed This paper presents a new semi-blind image watermarking system for medical applications. The new scheme utilizes Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) to embed a textual data into original medical images. In particular, text characters are encoded by a Quadrature Amplitude Modulation (QAM-16). In order to increase the security of the system and protect then the watermark from several attacks, the embedded data is submitted to Arnold Transform before inserting it into the host medical image. To evaluate the performances of the scheme, several medical images have been used in the experiments. Simulation results show that the proposed watermarking system ensures good imperceptibility and high robustness against several attacks. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T11:04:36Z No. of bitstreams: 1 ijimai_5_3_9_pdf_17617.pdf: 2102769 bytes, checksum: 065afc9e109abe3935779078ed90b8cd (MD5); Made available in DSpace on 2022-01-31T11:04:36Z (GMT). No. of bitstreams: 1 ijimai_5_3_9_pdf_17617.pdf: 2102769 bytes, checksum: 065afc9e109abe3935779078ed90b8cd (MD5)

IJIMAI Editor's Note - Vol. 5 Issue 2

IJIMAI Editor's Note - Vol. 5 Issue 2 Burgos, Daniel; Nikolov, Roumen; Stracke, Christian M. The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) provides an interdisciplinary forum in which scientists and professionals share their research results and report new advances on tools that use AI with interactive multimedia techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T10:31:16Z No. of bitstreams: 1 ijimai_5_2_0_pdf_14729.pdf: 117631 bytes, checksum: 906383d4577baa3b12802d1a7857a2dd (MD5); Made available in DSpace on 2022-01-31T10:31:16Z (GMT). No. of bitstreams: 1 ijimai_5_2_0_pdf_14729.pdf: 117631 bytes, checksum: 906383d4577baa3b12802d1a7857a2dd (MD5)

Exploring the Benefits of Using Gamification and Videogames for Physical Exercise: a Review of State of Art

Exploring the Benefits of Using Gamification and Videogames for Physical Exercise: a Review of State of Art González-González, Carina; Gómez del Río, Nazaret; Navarro-Adelantado, Vicente There is a lack of motivation in children and adolescents to do physical exercise and at the same time a worldwide obesity epidemic. Gamification and active videogames can be used to increase the motivation of young people, promoting healthy habits. In this work we explore different studies on active videogames, eSports and gamification applied to physical exercise and health promotion. Main findings include positive effects in a reduction in body weight and in the promotion to continue performing of physical exercise. It also contributes to increase the motivation in children and adolescents to practice exercise. The personalization of user experience and emerging technologies (big data, wearables, smart technologies, etc.) are presented as promising opportunities to keep the engagement in game-based program and gamification of physical exercise. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T10:26:29Z No. of bitstreams: 1 ijimai_5_2_6_pdf_43532.pdf: 701313 bytes, checksum: d9387fa0d6151500d16c462f5347898a (MD5); Made available in DSpace on 2022-01-31T10:26:29Z (GMT). No. of bitstreams: 1 ijimai_5_2_6_pdf_43532.pdf: 701313 bytes, checksum: d9387fa0d6151500d16c462f5347898a (MD5)

StuA: An Intelligent Student Assistant

StuA: An Intelligent Student Assistant Jain, Shikha; Lodhi, Pooja; Mishra, Omji; Bajaj, Vasvi With advanced innovation in digital technology, demand for virtual assistants is arising which can assist a person and at the same time, minimize the need for interaction with the human. Acknowledging the requirement, we propose an interactive and intelligent student assistant, StuA, which can help new-comer in a college who are hesitant in interacting with the seniors as they fear of being ragged. StuA is capable of answering all types of queries of a new-comer related to academics, examinations, library, hostel and extra curriculum activities. The model is designed using CLIPS which allows inferring using forward chaining. Nevertheless, a generalized algorithm for backward chaining for CLIPS is also implemented. Validation of the proposed model is presented in five steps which show that the model is complete and consistent with 99.16% accuracy of the knowledge model. Moreover, the backward chaining algorithm is found to be 100% accurate. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T09:58:22Z No. of bitstreams: 1 ijimai_5_2_2_pdf_57769.pdf: 864490 bytes, checksum: e4a1813aac9bb2873fb6c4b8266d9c92 (MD5); Made available in DSpace on 2022-01-31T09:58:22Z (GMT). No. of bitstreams: 1 ijimai_5_2_2_pdf_57769.pdf: 864490 bytes, checksum: e4a1813aac9bb2873fb6c4b8266d9c92 (MD5)

UC@MOOC's Effectiveness by Producing Open Educational Resources

UC@MOOC's Effectiveness by Producing Open Educational Resources Margoum, Sofia; Bendaoud, Rachid; Berrada, Khalid; Idrissi Jouicha, Abdellah Open education is one of the most important settings in every society. It grants everyone the right to learn freely. Today, technology is helping to make learning even more open by providing an environment of online education, which plays a remarkable role in shortening distances and encouraging students to learn. At Cadi Ayyad University (UCA) the new-enrolled students are facing linguistics barriers as well as overcrowding in classrooms, in particular for those in open access institutions. Subsequently, they cannot have an easy access to their face-to-face courses. To help students to overcome these problems, the university has decided to design an online environment for all courses and programmes. The most innovative project adopted at UCA to face massification was inspired from the massive open online courses and was designed as an open Educational platform entitled UC@MOOC. More than 120 scripted courses have been posted online so far. In this paper we will describe and discuss an analytics research on geometrical optics course designed for around 2000 students at UCA. Through out this research we will explain how this initiative has been considered as a source of producing open educational resources. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T09:51:19Z No. of bitstreams: 1 ijimai_5_2_8_pdf_53982.pdf: 613254 bytes, checksum: 760bb9fd0ca870c755b574203a8a8995 (MD5); Made available in DSpace on 2022-01-31T09:51:19Z (GMT). No. of bitstreams: 1 ijimai_5_2_8_pdf_53982.pdf: 613254 bytes, checksum: 760bb9fd0ca870c755b574203a8a8995 (MD5)

An Integrated Learning Analytics Approach for Virtual Vocational Training Centers

An Integrated Learning Analytics Approach for Virtual Vocational Training Centers Klamma, Ralf; Lange, de Peter; Neumann, Alexander Tobias; Nicolaescu, Petru Virtual training centers are hosted solutions for the implementation of training courses in the form of e.g. Webinars. Many existing centers neglect the informal and social dimension of vocational training as well as the legitimate business interests of training providers and companies sending their employees. In this paper, we present the virtual training center platform V3C that blends formal, certified virtual training courses with self-regulated and social learning in synchronous and asynchronous learning phases. We have developed an integrated learning analytics approach to collect, store, analyze and visualize data for different purposes like certification, interventions and gradual improvement of the platform. The results given here demonstrate the ability of the platform to deliver data for key performance indicators like learning outcomes and drop-out rates as well as the interplay between synchronous and asynchronous learning phases on a very large scale. Since the platform implementation is open source, results can be easily transferred and exploited in many contexts. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T09:28:21Z No. of bitstreams: 1 ijimai_5_2_4_pdf_97157.pdf: 1099837 bytes, checksum: 8104a29d3244961644a8cf16486c8dba (MD5); Made available in DSpace on 2022-01-31T09:28:22Z (GMT). No. of bitstreams: 1 ijimai_5_2_4_pdf_97157.pdf: 1099837 bytes, checksum: 8104a29d3244961644a8cf16486c8dba (MD5)

Planning and Allocation of Digital Learning Objects with Augmented Reality to Higher Education Students According to the VARK Model

Planning and Allocation of Digital Learning Objects with Augmented Reality to Higher Education Students According to the VARK Model Medina, Mireles; García, Carrillo; Olguín, Montes In the present research, the authors propose the planning, assignment and use of digital learning objects with augmented reality according to the learning style of students in higher education, according to the VARK Model. It is found that students with treatment have had better results in their final grades than students who have not undergone the treatment of having used digital learning objects with augmented reality. The digital objects of learning (DLO’s) with augmented reality designed according to the learning style of the students are an attractive and adequate option so that the teachers who are the main responsible for the didactic planning can spread the knowledge in the students. So that traditional forms of education are put aside and as a result of taking advantage of Information and Communication Technologies that have come to break with the paradigms that have prevailed for years in the teaching - learning process. On the other hand, education based on e-learning platforms facilitates the training of students at a distance allowing them to build and self-manage learning, as well as facilitate the dissemination of digital learning objects with augmented reality according to the learning style according to the VARK Model. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T08:45:17Z No. of bitstreams: 1 ijimai_5_2_7_pdf_10900.pdf: 533229 bytes, checksum: 6c883c7cda3cd3549a2b8a1923402006 (MD5); Made available in DSpace on 2022-01-31T08:45:17Z (GMT). No. of bitstreams: 1 ijimai_5_2_7_pdf_10900.pdf: 533229 bytes, checksum: 6c883c7cda3cd3549a2b8a1923402006 (MD5)

Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis

Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis Hamoud, Alaa Khalaf; Hashim, Ali Salah; Awadh, Wid Aqeel The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-31T08:29:11Z No. of bitstreams: 1 ijimai_5_2_3_pdf_30196.pdf: 397209 bytes, checksum: 5691b1fcdb7dbf433b3fa10c20227739 (MD5); Made available in DSpace on 2022-01-31T08:29:11Z (GMT). No. of bitstreams: 1 ijimai_5_2_3_pdf_30196.pdf: 397209 bytes, checksum: 5691b1fcdb7dbf433b3fa10c20227739 (MD5)

Comparison of Clustering Algorithms for Learning Analytics with Educational Datasets

Comparison of Clustering Algorithms for Learning Analytics with Educational Datasets Martínez Navarro, Álvaro; Moreno-Ger, Pablo Learning Analytics is becoming a key tool for the analysis and improvement of digital education processes, and its potential benefit grows with the size of the student cohorts generating data. In the context of Open Education, the potentially massive student cohorts and the global audience represent a great opportunity for significant analyses and breakthroughs in the field of learning analytics. However, these potentially huge datasets require proper analysis techniques, and different algorithms, tools and approaches may perform better in this specific context. In this work, we compare different clustering algorithms using an educational dataset. We start by identifying the most relevant algorithms in Learning Analytics and benchmark them to determine, according to internal validation and stability measurements, which algorithms perform better. We analyzed seven algorithms, and determined that K-means and PAM were the best performers among partition algorithms, and DIANA was the best performer among hierarchical algorithms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T09:45:52Z No. of bitstreams: 1 ijimai_5_2_1_pdf_84728.pdf: 1574330 bytes, checksum: 54be7ed138da258c12b58a9453092bec (MD5); Made available in DSpace on 2022-01-27T09:45:52Z (GMT). No. of bitstreams: 1 ijimai_5_2_1_pdf_84728.pdf: 1574330 bytes, checksum: 54be7ed138da258c12b58a9453092bec (MD5)

Proposing a Machine Learning Approach to Analyze and Predict Employment and its Factors

Proposing a Machine Learning Approach to Analyze and Predict Employment and its Factors García-Peñalvo, Francisco; Cruz-Benito, Juan; Martín-González, Martín; Vázquez-Ingelmo, Andrea; Sánchez-Prieto, José Carlos; Therón, Roberto This paper presents an original study with the aim of propose and test a machine learning approach to research about employability and employment. To understand how the graduates get employed, researchers propose to build predictive models using machine learning algorithms, extracting after that the most relevant factors that describe the model and employing further analysis techniques like clustering to get deeper insights. To test the proposal, is presented a case study that involves data from the Spanish Observatory for Employability and Employment (OEEU). Using data from this project (information about 3000 students), has been built predictive models that define how these students get a job after finalizing their degrees. The results obtained in this case study are very promising, and encourage authors to refine the process and validate it in further research. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T09:35:30Z No. of bitstreams: 1 ijimai_5_2_5_pdf_12552.pdf: 445862 bytes, checksum: 0db264e24b64c55e8111aeb77e3a7cf6 (MD5); Made available in DSpace on 2022-01-27T09:35:30Z (GMT). No. of bitstreams: 1 ijimai_5_2_5_pdf_12552.pdf: 445862 bytes, checksum: 0db264e24b64c55e8111aeb77e3a7cf6 (MD5)

IJIMAI Editor's Note - Vol. 5 Issue 1

IJIMAI Editor's Note - Vol. 5 Issue 1 Khari, Manju The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) provides an interdisciplinary forum in which scientists and professionals share their research results and report new advances on tools that use AI with interactive multimedia techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T08:46:13Z No. of bitstreams: 1 ijimai_5_1_0_pdf_13503.pdf: 170828 bytes, checksum: 7a816025c5da6bc51e3ba54cc2735c0e (MD5); Made available in DSpace on 2022-01-27T08:46:13Z (GMT). No. of bitstreams: 1 ijimai_5_1_0_pdf_13503.pdf: 170828 bytes, checksum: 7a816025c5da6bc51e3ba54cc2735c0e (MD5)

MSA for Optimal Reconfiguration and Capacitor Allocation in Radial/Ring Distribution Networks

MSA for Optimal Reconfiguration and Capacitor Allocation in Radial/Ring Distribution Networks Mohamed, Emad; Mohamed, Al-Attar Ali; Mitani, Yasunori This work presents a hybrid heuristic search algorithm called Moth Swarm Algorithm (MSA) in the context of power loss minimization of radial distribution networks (RDN) through optimal allocation and rating of shunt capacitors for enhancing the performance of distribution networks. With MSA, different optimization operators are used to mimic a set of behavioral patterns of moths in nature, which allows for flexible and powerful optimizer. Hence, a new dynamic selection strategy of crossover points is proposed based on population diversity to handle the difference vectors Lévy-mutation to force MSA jump out of stagnation and enhance its exploration ability. In addition, a spiral motion, adaptive Gaussian walks, and a novel associative learning mechanism with immediate memory are implemented to exploit the promising areas in the search space. In this article, the MSA is tested to adapt the objective function to reduce the system power losses, reduce total system cost and consequently increase the annual net saving with inequity constrains on capacitor size and voltage limits. The validation of the proposed algorithm has been tested and verified through small, medium and large scales of standard RDN of IEEE (33, 69, 85-bus) systems and also on ring main systems of 33 and 69-bus. In addition, the obtained results are compared with other algorithms to highlight the advantages of the proposed approach. Numerical results stated that the MSA can achieve optimal solutions for losses reduction and capacitor locations with finest performance compared with many existing algorithms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T08:42:39Z No. of bitstreams: 1 ijimai_5_1_14_pdf_75537.pdf: 2952774 bytes, checksum: 15abbd0a4a638e1080a3a712c78fedbd (MD5); Made available in DSpace on 2022-01-27T08:42:39Z (GMT). No. of bitstreams: 1 ijimai_5_1_14_pdf_75537.pdf: 2952774 bytes, checksum: 15abbd0a4a638e1080a3a712c78fedbd (MD5)

EEG Signal Analysis of Writing and Typing between Adults with Dyslexia and Normal Controls

EEG Signal Analysis of Writing and Typing between Adults with Dyslexia and Normal Controls Perera, Perera, Harshani Harshani; Shiratuddin, Mohd Fairuz; Wong, Kok Wai; Fullarton, Kelly EEG is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, plays a vital role in detecting neurological conditions. In this paper, we identify some unique EEG patterns pertaining to dyslexia, which is a learning disability with a neurological origin. Although EEG signals hold important insights of brain behaviours, uncovering these insights are not always straightforward due to its complexity. We tackle this using machine learning and uncover unique EEG signals generated in adults with dyslexia during writing and typing as well as optimal EEG electrodes and brain regions for classification. This study revealed that the greater level of difficulties seen in individuals with dyslexia during writing and typing compared to normal controls are reflected in the brainwave signal patterns. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T08:36:05Z No. of bitstreams: 1 ijimai_5_1_8_pdf_17747.pdf: 1445633 bytes, checksum: 3ac9512ea438440d1f3c88d45adc48ad (MD5); Made available in DSpace on 2022-01-27T08:36:05Z (GMT). No. of bitstreams: 1 ijimai_5_1_8_pdf_17747.pdf: 1445633 bytes, checksum: 3ac9512ea438440d1f3c88d45adc48ad (MD5)

Applying Bayesian Regularization for Acceleration of Levenberg-Marquardt based Neural Network Training

Applying Bayesian Regularization for Acceleration of Levenberg-Marquardt based Neural Network Training Suliman, Azizah; Omarov, Batyrkhan Neural network is widely used for image classification problems, and is proven to be effective with high successful rate. However one of its main challenges is the significant amount of time it takes to train the network. The goal of this research is to improve the neural network training algorithms and apply and test them in classification and recognition problems. In this paper, we describe a method of applying Bayesian regularization to improve Levenberg-Marquardt (LM) algorithm and make it better usable in training neural networks. In the experimental part, we qualify the modified LM algorithm using Bayesian regularization and use it to determine an appropriate number of hidden layers in the network to avoid overtraining. The result of the experiment was very encouraging with a 98.8% correct classification when run on test samples. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T08:28:30Z No. of bitstreams: 1 ijimai_5_1_9_pdf_32121.pdf: 687098 bytes, checksum: 0ece36f7c9507e56324e1207a046025d (MD5); Made available in DSpace on 2022-01-27T08:28:30Z (GMT). No. of bitstreams: 1 ijimai_5_1_9_pdf_32121.pdf: 687098 bytes, checksum: 0ece36f7c9507e56324e1207a046025d (MD5)

Conceptual Model Development of Big Data Analytics Implementation Assessment Effect on Decision-Making

Conceptual Model Development of Big Data Analytics Implementation Assessment Effect on Decision-Making Adrian, Cecilia; Abdullah, Rusli; Atan, Rodziah; Jusoh, Yusmadi Yah The significance of big data advancement has benefited various organizations to leverage the potential insights and capabilities of big data in organizational performance and decision-making. However, the analytics outcome and quality of big data analytics (BDA) implementation has yet to be addressed. Therefore the aims of this paper are to identify and analyze the affecting factors and elements of BDA implementation and to propose a conceptual model for effective decision-making through BDA implementation assessment. The model is developed based on three dimensions such as performing data strategy (organization), collaborative knowledge worker (people) and executing data analytics (technology). The findings of this ongoing study proceeds with designing a proposed conceptual model with the research hypothesis and may provide a better assessment model for effective decision-making in the long run. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T08:13:13Z No. of bitstreams: 1 ijimai_5_1_13_pdf_81158.pdf: 487442 bytes, checksum: 52e42f27aa17f01834a104976bc364a2 (MD5); Made available in DSpace on 2022-01-27T08:13:13Z (GMT). No. of bitstreams: 1 ijimai_5_1_13_pdf_81158.pdf: 487442 bytes, checksum: 52e42f27aa17f01834a104976bc364a2 (MD5)

A Study on Persuasive Technologies: The Relationship between User Emotions, Trust and Persuasion

A Study on Persuasive Technologies: The Relationship between User Emotions, Trust and Persuasion Ahmad, Wan Nooraishya Wan; Ali, Nazlena Mohamad A successful persuasive technology is able to persuade people to change from one state to a more well known state. Therefore, to allow for a change, persuasive technology must be able to affect users’ emotion and make the user trust the technology so that they will adopt the persuasive technology into their daily life routine, as well as continue to use the technology for long period. This paper is aimed to study the relation between users’ emotion with trust and persuasion and how they may contribute to the success of changing a person attitude or behavior towards a certain context or issue. Twenty five participants have completed the study in 6 weeks by using two types of persuasive technology that were assessed at three different interaction stages: pre, during and post. Result shows that emotions have a significant effect on trust, whereas the effect of emotions on persuasion using the persuasive technology was mediated by trust. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T07:58:49Z No. of bitstreams: 1 ijimai_5_1_7_pdf_14509.pdf: 372681 bytes, checksum: df5fb93cf1b9580e8b37412c5f4b0911 (MD5); Made available in DSpace on 2022-01-27T07:58:49Z (GMT). No. of bitstreams: 1 ijimai_5_1_7_pdf_14509.pdf: 372681 bytes, checksum: df5fb93cf1b9580e8b37412c5f4b0911 (MD5)

A Novel Smart Grid State Estimation Method Based on Neural Networks

A Novel Smart Grid State Estimation Method Based on Neural Networks Abdel-Nasser, Mohamed; Mahmoud, Karar; Kashef, Heba The rapid development in smart grids needs efficient state estimation methods. This paper presents a novel method for smart grid state estimation (e.g., voltages, active and reactive power loss) using artificial neural networks (ANNs). The proposed method which is called SE-NN (state estimation using neural network) can evaluate the state at any point of smart grid systems considering fluctuated loads. To demonstrate the effectiveness of the proposed method, it has been applied on IEEE 33-bus distribution system with different data resolutions. The accuracy of the proposed method is validated by comparing the results with an exact power flow method. The proposed SE-NN method is a very fast tool to estimate voltages and re/active power loss with a high accuracy compared to the traditional methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-26T13:26:31Z No. of bitstreams: 1 ijimai_5_1_12_pdf_18312.pdf: 980918 bytes, checksum: b29143ba71e0d61592542210c26c4ead (MD5); Made available in DSpace on 2022-01-26T13:26:31Z (GMT). No. of bitstreams: 1 ijimai_5_1_12_pdf_18312.pdf: 980918 bytes, checksum: b29143ba71e0d61592542210c26c4ead (MD5)

Users Integrity Constraints in SOLAP Systems. Application in Agroforestry

Users Integrity Constraints in SOLAP Systems. Application in Agroforestry Charef, Abdallah Bensalloua; Djamila, Hamdadou SpatialData Warehouse and Spatial On-Line Analytical Processing are decision support technologies which offer the spatial and multidimensional analysis of data stored in multidimensional structure. They are aimed also at supporting geographic knowledge discovery to help decision-maker in his job related to make the appropriate decision . However, if we don’t consider data quality in the spatial hypercubes and how it is explored, it may provide unreliable results. In this paper, we propose a system for the implementation of user integrity constraints in SOLAP namely “UIC-SOLAP”. It corresponds to a methodology for guaranteeing results quality in an analytical process effectuated by different users exploiting several facts tables within the same hypercube. We integrate users Integrity Constraints (IC) by specifying visualization ICs according to their preferences and we define inter-facts ICs in this case. In order to validate our proposition, we propose the multidimensional modeling by UML profile to support constellation schema of a hypercube with several fact tables related to subjects of analysis in forestry management. Then, we propose implementation of some ICs related to users of such a system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-26T13:15:19Z No. of bitstreams: 1 ijimai_5_1_6_pdf_24536.pdf: 2042302 bytes, checksum: 02a7329e6a5c358002e74e27dd226dd2 (MD5); Made available in DSpace on 2022-01-26T13:15:19Z (GMT). No. of bitstreams: 1 ijimai_5_1_6_pdf_24536.pdf: 2042302 bytes, checksum: 02a7329e6a5c358002e74e27dd226dd2 (MD5)

Spectral Restoration Based Speech Enhancement for Robust Speaker Identification

Spectral Restoration Based Speech Enhancement for Robust Speaker Identification Saleem, Nasir; Tareen, Tayyaba Gul Spectral restoration based speech enhancement algorithms are used to enhance quality of noise masked speech for robust speaker identification. In presence of background noise, the performance of speaker identification systems can be severely deteriorated. The present study employed and evaluated the Minimum Mean-Square-Error Short-Time Spectral Amplitude Estimators with modified a priori SNR estimate prior to speaker identification to improve performance of the speaker identification systems in presence of background noise. For speaker identification, Mel Frequency Cepstral coefficient and Vector Quantization is used to extract the speech features and to model the extracted features respectively. The experimental results showed significant improvement in speaker identification rates when spectral restoration based speech enhancement algorithms are used as a pre-processing step. The identification rates are found to be higher after employing the speech enhancement algorithms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-26T13:07:47Z No. of bitstreams: 1 ijimai_5_1_4_pdf_16417.pdf: 1957139 bytes, checksum: 3b9701b41dca56086367fdaf9a829b94 (MD5); Made available in DSpace on 2022-01-26T13:07:47Z (GMT). No. of bitstreams: 1 ijimai_5_1_4_pdf_16417.pdf: 1957139 bytes, checksum: 3b9701b41dca56086367fdaf9a829b94 (MD5)

Spatial and Textural Aspects for Arabic Handwritten Characters Recognition

Spatial and Textural Aspects for Arabic Handwritten Characters Recognition Boulid, Youssef; Souhar, Abdelghani; Ouagague, Mly. The purpose of the present paper is the recognition of handwritten Arabic characters in their isolated form. The specificity of Arabic characters is taken into consideration, each of the proposed feature extraction method integrates one of the two aspects: spatial and textural. In the first step, a modified Bitmap Sampling method is proposed, which converts the character’s images into a binary Matrix and then constructs a Mask for each class. A matching rate is used between the input binary matrix and the masks to determinate the corresponding class. In the second step we investigate the use of an Artificial Neural Network as classifier with the binary matrices as features and then the histograms of Local Binary Patterns to capture the texture aspect of the characters. Finally, the results of these two methods are combined to take into consideration both aspects at the same time. Tested on the Arabic set of the Isolated Farsi Handwritten Character Database, the proposed method has 2.82% error rate. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-26T13:00:17Z No. of bitstreams: 1 ijimai_5_1_11_pdf_70968.pdf: 923665 bytes, checksum: 57bbc905e4dbac48be784f4a5b0c78e9 (MD5); Made available in DSpace on 2022-01-26T13:00:17Z (GMT). No. of bitstreams: 1 ijimai_5_1_11_pdf_70968.pdf: 923665 bytes, checksum: 57bbc905e4dbac48be784f4a5b0c78e9 (MD5)

Real Time Facial Expression Recognition Using Webcam and SDK Affectiva

Real Time Facial Expression Recognition Using Webcam and SDK Affectiva Magdin, Martin; Prikler, F Facial expression is an essential part of communication. For this reason, the issue of human emotions evaluation using a computer is a very interesting topic, which has gained more and more attention in recent years. It is mainly related to the possibility of applying facial expression recognition in many fields such as HCI, video games, virtual reality, and analysing customer satisfaction etc. Emotions determination (recognition process) is often performed in 3 basic phases: face detection, facial features extraction, and last stage - expression classification. Most often you can meet the so-called Ekman’s classification of 6 emotional expressions (or 7 - neutral expression) as well as other types of classification - the Russell circular model, which contains up to 24 or the Plutchik’s Wheel of Emotions. The methods used in the three phases of the recognition process have not only improved over the last 60 years, but new methods and algorithms have also emerged that can determine the ViolaJones detector with greater accuracy and lower computational demands. Therefore, there are currently various solutions in the form of the Software Development Kit (SDK). In this publication, we point to the proposition and creation of our system for real-time emotion classification. Our intention was to create a system that would use all three phases of the recognition process, work fast and stable in real time. That’s why we’ve decided to take advantage of existing Affectiva SDKs. By using the classic webcamera we can detect facial landmarks on the image automatically using the Software Development Kit (SDK) from Affectiva. Geometric feature based approach is used for feature extraction. The distance between landmarks is used as a feature, and for selecting an optimal set of features, the brute force method is used. The proposed system uses neural network algorithm for classification. The proposed system recognizes 6 (respectively 7) facial expressions, namely anger, disgust, fear, happiness, sadness, surprise and neutral. We do not want to point only to the percentage of success of our solution. We want to point out the way we have determined this measurements and the results we have achieved and how these results have significantly influenced our future research direction. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-25T10:54:44Z No. of bitstreams: 1 ijimai_5_1_1_pdf_82035.pdf: 1222831 bytes, checksum: be0c3502763a7b93fc0f6eb2d0b16408 (MD5); Made available in DSpace on 2022-01-25T10:54:44Z (GMT). No. of bitstreams: 1 ijimai_5_1_1_pdf_82035.pdf: 1222831 bytes, checksum: be0c3502763a7b93fc0f6eb2d0b16408 (MD5)

Novel Clustering Method Based on K-Medoids and Mobility Metric

Novel Clustering Method Based on K-Medoids and Mobility Metric Hamzaoui, Y; Amnai, M; Choukri, A; Fakhri, Y The structure and constraint of MANETS influence negatively the performance of QoS, moreover the main routing protocols proposed generally operate in flat routing. Hence, this structure gives the bad results of QoS when the network becomes larger and denser. To solve this problem we use one of the most popular methods named clustering. The present paper comes within the frameworks of research to improve the QoS in MANETs. In this paper we propose a new algorithm of clustering based on the new mobility metric and K-Medoid to distribute the nodes into several clusters. Intuitively our algorithm can give good results in terms of stability of the cluster, and can also extend life time of cluster head. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-25T10:29:55Z No. of bitstreams: 1 ijimai_5_1_3_pdf_41024.pdf: 785747 bytes, checksum: 12a58e3d9212a360c0165802e94d744e (MD5); Made available in DSpace on 2022-01-25T10:29:55Z (GMT). No. of bitstreams: 1 ijimai_5_1_3_pdf_41024.pdf: 785747 bytes, checksum: 12a58e3d9212a360c0165802e94d744e (MD5)

Object Detection and Tracking using Modified Diamond Search Block Matching Motion Estimation Algorithm

Object Detection and Tracking using Modified Diamond Search Block Matching Motion Estimation Algorithm Kamble, Shailesh; Thakur, Nileshsingh; Samdurkar, Apurva; Patharkar, Akshay Object tracking is one of the main fields within computer vision. Amongst various methods/ approaches for object detection and tracking, the background subtraction approach makes the detection of object easier. To the detected object, apply the proposed block matching algorithm for generating the motion vectors. The existing diamond search (DS) and cross diamond search algorithms (CDS) are studied and experiments are carried out on various standard video data sets and user defined data sets. Based on the study and analysis of these two existing algorithms a modified diamond search pattern (MDS) algorithm is proposed using small diamond shape search pattern in initial step and large diamond shape (LDS) in further steps for motion estimation. The initial search pattern consists of five points in small diamond shape pattern and gradually grows into a large diamond shape pattern, based on the point with minimum cost function. The algorithm ends with the small shape pattern at last. The proposed MDS algorithm finds the smaller motion vectors and fewer searching points than the existing DS and CDS algorithms. Further, object detection is carried out by using background subtraction approach and finally, MDS motion estimation algorithm is used for tracking the object in color video sequences. The experiments are carried out by using different video data sets containing a single object. The results are evaluated and compared by using the evaluation parameters like average searching points per frame and average computational time per frame. The experimental results show that the MDS performs better than DS and CDS on average search point and average computation time. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-25T10:15:07Z No. of bitstreams: 1 ijimai_5_1_10_pdf_12412.pdf: 1985614 bytes, checksum: 7b67fc33ccfae0a04afc9d3bf450af57 (MD5); Made available in DSpace on 2022-01-25T10:15:07Z (GMT). No. of bitstreams: 1 ijimai_5_1_10_pdf_12412.pdf: 1985614 bytes, checksum: 7b67fc33ccfae0a04afc9d3bf450af57 (MD5)

Hybrid Model for Passive Locomotion Control of a Biped Humanoid:The Artificial Neural Network Approach

Hybrid Model for Passive Locomotion Control of a Biped Humanoid:The Artificial Neural Network Approach Bhaskar-Semwal, Vijay; Raj, Manish; Nandi, G C Developing a correct model for a biped robot locomotion is extremely challenging due to its inherently unstable structure because of the passive joint located at the unilateral foot-ground contact and varying configurations throughout the gait cycle, resulting variation of dynamic descriptions and control laws from phase to phase. The present research describes the development of a hybrid biped model using an Open Dynamics Engine (ODE) based analytical three link leg model as a base model and, on top of it, an Artificial Neural Network based learning model which ensures better adaptability, better limits cycle behaviors and better generalization while negotiating along a down slope. The base model has been configured according to the individual subjects and data have been collected using a novel technique through an android app from those subjects while walking down a slope. The pattern between the deviation of the actual trajectories and the base model generated trajectories has been found using a back propagation based artificial neural network architecture. It has been observed that this base model with learning based compensation enables the biped to better adapt in a real walking environment, showing better limit cycle behaviors. We also observed the bounded nature of deviation which led us to conclude that the strategy for biped locomotion control is generic in nature and largely dominated by learning. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-25T09:46:34Z No. of bitstreams: 1 ijimai_5_1_5_pdf_20509.pdf: 1122752 bytes, checksum: 35d27cb4355b571f07b82e8ee741f9ee (MD5); Made available in DSpace on 2022-01-25T09:46:34Z (GMT). No. of bitstreams: 1 ijimai_5_1_5_pdf_20509.pdf: 1122752 bytes, checksum: 35d27cb4355b571f07b82e8ee741f9ee (MD5)

Self-Organized Hybrid Wireless Sensor Network for Finding Randomly Moving Target in Unknown Environment

Self-Organized Hybrid Wireless Sensor Network for Finding Randomly Moving Target in Unknown Environment Nighot, Mininath; Ghatol, Ashok; Thakare, Vilas Unknown target search, in an unknown environment, is a complex problem in Wireless Sensor Network (WSN). It does not have a linear solution when target’s location and searching space is unknown. For the past few years, many researchers have invented novel techniques for finding a target using either Static Sensor Node (SSN) or Mobile Sensor Node (MSN) in WSN i.e. Hybrid WSN. But there is a lack of research to find a solution using hybrid WSN. In the current research, the problem has been addressed mostly using non-biological techniques. Due to its complexity and having a non-linear solution, Bio-inspired techniques are most suited to solve the problem. This paper proposes a solution for searching of randomly moving target in unknown area using only Mobile sensor nodes and combination of both Static and Mobile sensor nodes. In proposed technique coverage area is determined and compared. To perform the work, novel algorithms like MSNs Movement Prediction Algorithm (MMPA), Leader Selection Algorithm (LSA), Leader’s Movement Prediction Algorithm (LMPA) and follower algorithm are implemented. Simulation results validate the effectiveness of proposed work. Through the result, it is shown that proposed hybrid WSN approach with less number of sensor nodes (combination of Static and Mobile sensor nodes) finds target faster than only MSN approach. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-25T09:19:28Z No. of bitstreams: 1 ijimai_5_1_2_pdf_19352.pdf: 3409132 bytes, checksum: c031351811b430c938336ec2b69ea658 (MD5); Made available in DSpace on 2022-01-25T09:19:29Z (GMT). No. of bitstreams: 1 ijimai_5_1_2_pdf_19352.pdf: 3409132 bytes, checksum: c031351811b430c938336ec2b69ea658 (MD5)

IJIMAI Editor's Note - Vol. 4 Issue 7

IJIMAI Editor's Note - Vol. 4 Issue 7 Mochón, Francisco; Elvira, Carlos The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on AI tools or tools that use AI with interactive multimedia techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T12:11:58Z No. of bitstreams: 1 ijimai_4_7_0_pdf_71516.pdf: 142423 bytes, checksum: 1dec9e26c34da0454ce5922653438c52 (MD5); Made available in DSpace on 2021-09-27T12:11:58Z (GMT). No. of bitstreams: 1 ijimai_4_7_0_pdf_71516.pdf: 142423 bytes, checksum: 1dec9e26c34da0454ce5922653438c52 (MD5)

Big Data and Public Health Systems: Issues and Opportunities

Big Data and Public Health Systems: Issues and Opportunities Rojas, David; Carnicero, Javier Over the last years, the need for changing the current model of European public health systems has been repeatedly addressed, in order to ensure their sustainability. Following this line, IT has always been referred to as one of the key instruments for enhancing the information management processes of healthcare organizations, thus contributing to the improvement and evolution of health systems. On the IT field, Big Data solutions are expected to play a main role, since they are designed for handling huge amounts of information in a fast and efficient way, allowing users to make important decisions quickly. This article reviews the main features of the European public health system model and the corresponding healthcare and management-related information systems, the challenges that these health systems are currently facing, and the possible contributions of Big Data solutions to this field. To that end, the authors share their professional experience on the Spanish public health system, and review the existing literature related to this topic. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T11:56:12Z No. of bitstreams: 1 ijimai_4_7_8_pdf_16011.pdf: 354882 bytes, checksum: c8b0581f35bddbebaf3c8623c0061f25 (MD5); Made available in DSpace on 2021-09-27T11:56:12Z (GMT). No. of bitstreams: 1 ijimai_4_7_8_pdf_16011.pdf: 354882 bytes, checksum: c8b0581f35bddbebaf3c8623c0061f25 (MD5)

Big Data and Health Economics: Opportunities, Challenges and Risks

Big Data and Health Economics: Opportunities, Challenges and Risks Bodas-Sagi, Diego; Labeaga, José Big Data offers opportunities in many fields. Healthcare is not an exception. In this paper we summarize the possibilities of Big Data and Big Data technologies to offer useful information to policy makers. In a world with tight public budgets and ageing populations we feel necessary to save costs in any production process. The use of outcomes from Big Data could be in the future a way to improve decisions at a lower cost than today. In addition to list the advantages of properly using data and technologies from Big Data, we also show some challenges and risks that analysts could face. We also present an hypothetical example of the use of administrative records with health information both for diagnoses and patients. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T11:43:48Z No. of bitstreams: 1 ijimai_4_7_7_pdf_86496.pdf: 519132 bytes, checksum: 61dfe095796c20f114246608ebd6ba8d (MD5); Made available in DSpace on 2021-09-27T11:43:48Z (GMT). No. of bitstreams: 1 ijimai_4_7_7_pdf_86496.pdf: 519132 bytes, checksum: 61dfe095796c20f114246608ebd6ba8d (MD5)

Generating Big Data Sets from Knowledge-based Decision Support Systems to Pursue Value-based Healthcare

Generating Big Data Sets from Knowledge-based Decision Support Systems to Pursue Value-based Healthcare González-Ferrer, Arturo; Seara, Germán; Cháfer, Joan; Mayol, Julio Talking about Big Data in healthcare we usually refer to how to use data collected from current electronic medical records, either structured or unstructured, to answer clinically relevant questions. This operation is typically carried out by means of analytics tools (e.g. machine learning) or by extracting relevant data from patient summaries through natural language processing techniques. From other perspective of research in medical informatics, powerful initiatives have emerged to help physicians taking decisions, in both diagnostics and therapeutics, built from the existing medical evidence (i.e. knowledge-based decision support systems). Much of the problems these tools have shown, when used in real clinical settings, are related to their implementation and deployment, more than failing in its support, but, technology is slowly overcoming interoperability and integration issues. Beyond the point-of-care decision support these tools can provide, the data generated when using them, even in controlled trials, could be used to further analyze facts that are traditionally ignored in the current clinical practice. In this paper, we reflect on the technologies available to make the leap and how they could help driving healthcare organizations shifting to a value-based healthcare philosophy. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T11:21:49Z No. of bitstreams: 1 ijimai_4_7_6_pdf_12585.pdf: 652666 bytes, checksum: 59b3a20d850628e9bffcea67d4417319 (MD5); Made available in DSpace on 2021-09-27T11:21:49Z (GMT). No. of bitstreams: 1 ijimai_4_7_6_pdf_12585.pdf: 652666 bytes, checksum: 59b3a20d850628e9bffcea67d4417319 (MD5)

Development of Injuries Prevention Policies in Mexico: A Big Data Approach

Development of Injuries Prevention Policies in Mexico: A Big Data Approach Cantón Croda, Rosa María; Gibaja Romero, Damián Emilio Considering that Mexican injuries prevention strategies have been focused on injuries caused by car accidents and gender violence, a whole analysis of the injuries registered are performed in this paper to have a wider overview of those agents that can cause injuries around the country. Taking into account the amount of information from both public and private sources, obtained from dynamic cubes reported by the Minister of Health, Big Data strategies are used with the objective of finding an appropriate extraction such as to identify the real correlations between the different variables registered by the Health Sector. The results of the analysis show areas of opportunity to improve the public policies on the subject, particularly in diminishing wounds at living place, public road (pedestrians) and work. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T10:59:26Z No. of bitstreams: 1 ijimai_4_7_5_pdf_14121.pdf: 509507 bytes, checksum: 776e0ad36e2a0376639079d8b58e9bba (MD5); Made available in DSpace on 2021-09-27T10:59:26Z (GMT). No. of bitstreams: 1 ijimai_4_7_5_pdf_14121.pdf: 509507 bytes, checksum: 776e0ad36e2a0376639079d8b58e9bba (MD5)

Machine-Learning-Based No Show Prediction in Outpatient Visits

Machine-Learning-Based No Show Prediction in Outpatient Visits Mochón, Francisco; Elvira, Carlos; Ochoa, Alberto; Gonzalvez, Juan Carlos A recurring problem in healthcare is the high percentage of patients who miss their appointment, be it a consultation or a hospital test. The present study seeks patient’s behavioural patterns that allow predicting the probability of no- shows. We explore the convenience of using Big Data Machine Learning models to accomplish this task. To begin with, a predictive model based only on variables associated with the target appointment is built. Then the model is improved by considering the patient’s history of appointments. In both cases, the Gradient Boosting algorithm was the predictor of choice. Our numerical results are considered promising given the small amount of information available. However, there seems to be plenty of room to improve the model if we manage to collect additional data for both patients and appointments. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T10:46:55Z No. of bitstreams: 1 ijimai_4_7_4_pdf_11885.pdf: 326853 bytes, checksum: 7660a249f6c0e17f88867c5837d3b718 (MD5); Made available in DSpace on 2021-09-27T10:46:55Z (GMT). No. of bitstreams: 1 ijimai_4_7_4_pdf_11885.pdf: 326853 bytes, checksum: 7660a249f6c0e17f88867c5837d3b718 (MD5)

Development of a Predictive Model for Induction Success of Labour

Development of a Predictive Model for Induction Success of Labour Pruenza, Cristina; Teurón, María; Lechuga, Luis; Díaz, Julia; González, Ana Induction of the labour process is an extraordinarily common procedure used in some pregnancies. Obstetricians face the need to end a pregnancy, for medical reasons usually (maternal or fetal requirements) or less frequently, social (elective inductions for convenience). The success of induction procedure is conditioned by a multitude of maternal and fetal variables that appear before or during pregnancy or birth process, with a low predictive value. The failure of the induction process involves performing a caesarean section. This project arises from the clinical need to resolve a situation of uncertainty that occurs frequently in our clinical practice. Since the weight of clinical variables is not adequately weighted, we consider very interesting to know a priori the possibility of success of induction to dismiss those inductions with high probability of failure, avoiding unnecessary procedures or postponing end if possible. We developed a predictive model of induced labour success as a support tool in clinical decision making. Improve the predictability of a successful induction is one of the current challenges of Obstetrics because of its negative impact. The identification of those patients with high chances of failure, will allow us to offer them better care improving their health outcomes (adverse perinatal outcomes for mother and newborn), costs (medication, hospitalization, qualified staff) and patient perceived quality. Therefore a Clinical Decision Support System was developed to give support to the Obstetricians. In this article, we had proposed a robust method to explore and model a source of clinical information with the purpose of obtaining all possible knowledge. Generally, in classification models are difficult to know the contribution that each attribute provides to the model. We had worked in this direction to offer transparency to models that may be considered as black boxes. The positive results obtained from both the information recovery system and the predictions and explanations of the classification show the effectiveness and strength of this tool. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T10:20:59Z No. of bitstreams: 1 ijimai_4_7_3_pdf_17377.pdf: 692348 bytes, checksum: 1994c566195b8d3e37ef9673c6a1852a (MD5); Made available in DSpace on 2021-09-27T10:20:59Z (GMT). No. of bitstreams: 1 ijimai_4_7_3_pdf_17377.pdf: 692348 bytes, checksum: 1994c566195b8d3e37ef9673c6a1852a (MD5)

DataCare: Big Data Analytics Solution for Intelligent Healthcare Management

DataCare: Big Data Analytics Solution for Intelligent Healthcare Management Saez, Yago; Baldominos Gómez, Alejandro; Rada, Fernando This paper presents DataCare, a solution for intelligent healthcare management. This product is able not only to retrieve and aggregate data from different key performance indicators in healthcare centers, but also to estimate future values for these key performance indicators and, as a result, fire early alerts when undesirable values are about to occur or provide recommendations to improve the quality of service. DataCare’s core processes are built over a free and open-source cross-platform document-oriented database (MongoDB), and Apache Spark, an open-source cluster-computing framework. This architecture ensures high scalability capable of processing very high data volumes coming at fast speed from a large set of sources. This article describes the architecture designed for this project and the results obtained after conducting a pilot in a healthcare center. Useful conclusions have been drawn regarding how key performance indicators change based on different situations, and how they affect patients’ satisfaction. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T09:37:40Z No. of bitstreams: 1 ijimai_4_7_2_pdf_16566.pdf: 784802 bytes, checksum: 14d2bb079d5b214a21c2acf687d97687 (MD5); Made available in DSpace on 2021-09-27T09:37:40Z (GMT). No. of bitstreams: 1 ijimai_4_7_2_pdf_16566.pdf: 784802 bytes, checksum: 14d2bb079d5b214a21c2acf687d97687 (MD5)

Savana: Re-using Electronic Health Records with Artificial Intelligence

Savana: Re-using Electronic Health Records with Artificial Intelligence Hernández Medrano, Ignacio; Tello Guijarro, Jorge; Belda, Cristóbal; Ureña, Alberto; Salcedo, Ignacio; Espinosa-Anke, Luis; Saggion, Horacio Health information grows exponentially (doubling every 5 years), thus generating a sort of inflation of science, i.e. the generation of more knowledge than we can leverage. In an unprecedented data-driven shift, today doctors have no longer time to keep updated. This fact explains why only one in every five medical decisions is based strictly on evidence, which inevitably leads to variability. A good solution lies on clinical decision support systems, based on big data analysis. As the processing of large amounts of information gains relevance, automatic approaches become increasingly capable to see and correlate information further and better than the human mind can. In this context, healthcare professionals are increasingly counting on a new set of tools in order to deal with the growing information that becomes available to them on a daily basis. By allowing the grouping of collective knowledge and prioritizing “mindlines” against “guidelines”, these support systems are among the most promising applications of big data in health. In this demo paper we introduce Savana, an AI-enabled system based on Natural Language Processing (NLP) and Neural Networks, capable of, for instance, the automatic expansion of medical terminologies, thus enabling the re-use of information expressed in natural language in clinical reports. This automatized and precise digital extraction allows the generation of a real time information engine, which is currently being deployed in healthcare institutions, as well as clinical research and management. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T09:09:25Z No. of bitstreams: 1 ijimai_4_7_1_pdf_22755.pdf: 935126 bytes, checksum: 8b6c3a77baefcab321a6826593d81f2e (MD5); Made available in DSpace on 2021-09-27T09:09:25Z (GMT). No. of bitstreams: 1 ijimai_4_7_1_pdf_22755.pdf: 935126 bytes, checksum: 8b6c3a77baefcab321a6826593d81f2e (MD5)

IJIMAI Editor's Note - Vol. 4 Issue 6

IJIMAI Editor's Note - Vol. 4 Issue 6 Verdú, Elena The International Journal of Interactive Multimedia and Artificial Intelligence provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence and Interactive Multimedia techniques. The research works presented in this regular issue are based on various topics of interest, among which are included: nature inspired optimization algorithms, multi-agent systems, fast motion estimation, handwritten recognition, supervised and unsupervised machine learning methods, or web mining. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-13T11:30:41Z No. of bitstreams: 1 ijimai20174_6_0_pdf_12161.pdf: 147971 bytes, checksum: c5a7d2b3e319393a8721eb48888c6806 (MD5); Made available in DSpace on 2021-09-13T11:30:41Z (GMT). No. of bitstreams: 1 ijimai20174_6_0_pdf_12161.pdf: 147971 bytes, checksum: c5a7d2b3e319393a8721eb48888c6806 (MD5)

A System to Generate SignWriting for Video Tracks Enhancing Accessibility of Deaf People

A System to Generate SignWriting for Video Tracks Enhancing Accessibility of Deaf People González-Crespo, Rubén; Pelayo García-Bustelo, B. Cristina; Verdú, Elena; Martínez, M A Video content has increased much on the Internet during last years. In spite of the efforts of different organizations and governments to increase the accessibility of websites, most multimedia content on the Internet is not accessible. This paper describes a system that contributes to make multimedia content more accessible on the Web, by automatically translating subtitles in oral language to SignWriting, a way of writing Sign Language. This system extends the functionality of a general web platform that can provide accessible web content for different needs. This platform has a core component that automatically converts any web page to a web page compliant with level AA of WAI guidelines. Around this core component, different adapters complete the conversion according to the needs of specific users. One adapter is the Deaf People Accessibility Adapter, which provides accessible web content for the Deaf, based on SignWritting. Functionality of this adapter has been extended with the video subtitle translator system. A first prototype of this system has been tested through different methods including usability and accessibility tests and results show that this tool can enhance the accessibility of video content available on the Web for Deaf people. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-13T11:17:15Z No. of bitstreams: 1 ijimai20174_6_15_pdf_19885.pdf: 509388 bytes, checksum: bc6be8da5a02719f674e16c04bab1b44 (MD5); Made available in DSpace on 2021-09-13T11:17:15Z (GMT). No. of bitstreams: 1 ijimai20174_6_15_pdf_19885.pdf: 509388 bytes, checksum: bc6be8da5a02719f674e16c04bab1b44 (MD5)

Design and Evaluation of a Short Version of the User Experience Questionnaire (UEQ-S)

Design and Evaluation of a Short Version of the User Experience Questionnaire (UEQ-S) Schrepp, Martin; chewski, Jörg Thomas; Hinderks, Andreas The user experience questionnaire (UEQ) is a widely used questionnaire to measure the subjective impression of users towards the user experience of products. The UEQ is a semantic differential with 26 items. Filling out the UEQ takes approximately 3-5 minutes, i.e. the UEQ is already reasonably efficient concerning the time required to answer all items. However, there exist several valid application scenarios, where filling out the entire UEQ appears impractical. This paper deals with the creation of an 8 item short version of the UEQ, which is optimized for these specific application scenarios. First validations of this short version are also described. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-13T10:56:52Z No. of bitstreams: 1 ijimai20174_6_14_pdf_20309.pdf: 457030 bytes, checksum: bc76f2abf4aa050ebab15c606d08c73d (MD5); Made available in DSpace on 2021-09-13T10:56:52Z (GMT). No. of bitstreams: 1 ijimai20174_6_14_pdf_20309.pdf: 457030 bytes, checksum: bc76f2abf4aa050ebab15c606d08c73d (MD5)

Segmentation of Arabic Handwritten Documents into Text Lines using Watershed Transform

Segmentation of Arabic Handwritten Documents into Text Lines using Watershed Transform Boulid, Youssef; Souhar, Abdelghani; Ouagague, Mly.; Ameur, E A crucial task in character recognition systems is the segmentation of the document into text lines and especially if it is handwritten. When dealing with non-Latin document such as Arabic, the challenge becomes greater since in addition to the variability of writing, the presence of diacritical points and the high number of ascender and descender characters complicates more the process of the segmentation. To remedy with this complexity and even to make this difficulty an advantage since the focus is on the Arabic language which is semi-cursive in nature, a method based on the Watershed Transform technique is proposed. Tested on «Handwritten Arabic Proximity Datasets» a segmentation rate of 93% for a 95% of matching score is achieved. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-13T10:51:23Z No. of bitstreams: 1 ijimai20174_6_13_pdf_11239.pdf: 1353036 bytes, checksum: ffcce3415d0c31442b4104c5c76f7780 (MD5); Made available in DSpace on 2021-09-13T10:51:23Z (GMT). No. of bitstreams: 1 ijimai20174_6_13_pdf_11239.pdf: 1353036 bytes, checksum: ffcce3415d0c31442b4104c5c76f7780 (MD5)

Smart Algorithms to Control a Variable Speed Wind Turbine

Smart Algorithms to Control a Variable Speed Wind Turbine Farhane, Nabil; Boumhidi, Ismail; Boumhidi, Jaouad In this paper, a robust adaptive fuzzy neural network sliding mode (AFNNSM) control design is proposed to maximize the captured energy for a variable speed wind turbine and to minimize the efforts of the drive shaft. Fuzzy neural network (FNN) is used to improve the mathematical system model, by the prediction of model unknown function, which is used by the Sliding mode control approach (SMC) and enables a lower switching gain to be used despite the presence of large uncertainties. As a result, the used robust control action did not exhibit any chattering behavior. This FNN is trained on-line using the backpropagation algorithm (BP). The particle swarm optimization (PSO) algorithm is used in this study to optimize the learning rate of BP algorithm in order to improve the network performance in term of the speed of convergence. The stability is shown by the Lyapunov theory and the trajectory tracking errors converge to zero without any oscillatory behavior. Simulations illustrate the effectiveness of the designed method. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-13T10:43:59Z No. of bitstreams: 1 ijimai20174_6_12_pdf_81478.pdf: 1496037 bytes, checksum: f1e3ef80abdee2cdabe29b7c46f95756 (MD5); Made available in DSpace on 2021-09-13T10:43:59Z (GMT). No. of bitstreams: 1 ijimai20174_6_12_pdf_81478.pdf: 1496037 bytes, checksum: f1e3ef80abdee2cdabe29b7c46f95756 (MD5)

Workforce Optimization for Bank Operation Centers: A Machine Learning Approach

Workforce Optimization for Bank Operation Centers: A Machine Learning Approach Serengil, Sefik Ilkin; Ozpinar, Alper Online Banking Systems evolved and improved in recent years with the use of mobile and online technologies, performing money transfer transactions on these channels can be done without delay and human interaction, however commercial customers still tend to transfer money on bank branches due to several concerns. Bank Operation Centers serve to reduce the operational workload of branches. Centralized management also offers personalized service by appointed expert employees in these centers. Inherently, workload volume of money transfer transactions changes dramatically in hours. Therefore, work-force should be planned instantly or early to save labor force and increase operational efficiency. This paper introduces a hybrid multi stage approach for workforce planning in bank operation centers by the application of supervised and unsu-pervised learning algorithms. Expected workload would be predicted as supervised learning whereas employees are clus-tered into different skill groups as unsupervised learning to match transactions and proper employees. Finally, workforce optimization is analyzed for proposed approach on production data. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-13T10:14:09Z No. of bitstreams: 1 ijimai20174_6_11_pdf_19584.pdf: 815676 bytes, checksum: 43290b32a9ba0d1337a9b70ad38cf8d9 (MD5); Made available in DSpace on 2021-09-13T10:14:09Z (GMT). No. of bitstreams: 1 ijimai20174_6_11_pdf_19584.pdf: 815676 bytes, checksum: 43290b32a9ba0d1337a9b70ad38cf8d9 (MD5)

Supporting Multi-agent Coordination and Computational Collective Intelligence in Enterprise 2.0 Platform

Supporting Multi-agent Coordination and Computational Collective Intelligence in Enterprise 2.0 Platform Taghezout, Noria; Reguieg, Seddik In this paper, we propose a novel approach utilizing a professional Social network (Pro Social Network) and a new coordination protocol (CordiNet). Our motivation behind this article is to convince Small and Medium Enterprises managers that current organizations have chosen to use Enterprise 2.0 tools because these latter have demonstrated remarkable innovation as well as successful collaboration and collective intelligence. The particularity of our work is that is allows employer to share diagnosis and fault repair procedures on the basis of some modeling agents. In fact, each enterprise is represented by a container of agents to ensure a secured and confidential information exchange between intra employers, and a central main container to connect all enterprises’ containers for a social information exchange. Enterprise’s container consists of a Checker Enterprise Agent (ChEA), a Coordinator Enterprise Agent (CoEA) and a Search Enterprise Agent (SeEA). Whereas the central main container comprises its proper agents such as Selection Agent (SA), and a Supervisor Agent (SuA). JADE platform is used to allow agents to communicate and collaborate. The FIPA-ACL performatives have been extended for this purpose. We conduct some experiments to demonstrate the feasibility of our approach. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-13T10:08:53Z No. of bitstreams: 1 ijimai20174_6_10_pdf_13892.pdf: 2147249 bytes, checksum: d940e70e5881c9b47c6583531959eba7 (MD5); Made available in DSpace on 2021-09-13T10:08:53Z (GMT). No. of bitstreams: 1 ijimai20174_6_10_pdf_13892.pdf: 2147249 bytes, checksum: d940e70e5881c9b47c6583531959eba7 (MD5)

Combining Fuzzy AHP with GIS and Decision Rules for Industrial Site Selection

Combining Fuzzy AHP with GIS and Decision Rules for Industrial Site Selection Taibi, Aissa; Atmani, Baghdad This study combines Fuzzy Analytic Hierarchy Process (FAHP), Geographic Information System (GIS) and Decision rules to provide decision makers with a ranking model for industrial sites in Algeria. A ranking of the suitable industrial areas is a crucial multi-criteria decision problem based on socio-economical and technical criteria as on environmental considerations. Fuzzy AHP is used for assessment of the candidate industrial sites by combining fuzzy set theory and analytic hierarchy process (AHP). The decision rule base serves as a filter that performs criteria pre-treatment involving a reduction of their numbers. GIS is used to overlay, generate criteria maps and for visualizing ranked zones on the map. The rank of a zone so obtained is an index that guides decision-makers to the best utilization of the zone in future. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-13T10:03:15Z No. of bitstreams: 1 ijimai20174_6_9_pdf_25718.pdf: 2057289 bytes, checksum: 2cdd1ca51b0dd44f43624715f32ec2e2 (MD5); Made available in DSpace on 2021-09-13T10:03:15Z (GMT). No. of bitstreams: 1 ijimai20174_6_9_pdf_25718.pdf: 2057289 bytes, checksum: 2cdd1ca51b0dd44f43624715f32ec2e2 (MD5)

Anomaly based Intrusion Detection using Modified Fuzzy Clustering

Anomaly based Intrusion Detection using Modified Fuzzy Clustering Harish, B S; Kumar, S V A This paper presents a network anomaly detection method based on fuzzy clustering. Computer security has become an increasingly vital field in computer science in response to the proliferation of private sensitive information. As a result, Intrusion Detection System has become an indispensable component of computer security. The proposed method consists of three steps: Pre-Processing, Feature Selection and Clustering. In pre-processing step, the duplicate samples are eliminated from the sample set. Next, principal component analysis is adopted to select the most discriminative features. In clustering step, the network samples are clustered using Robust Spatial Kernel Fuzzy C-Means (RSKFCM) algorithm. RSKFCM is a variant of traditional Fuzzy C-Means which considers the neighbourhood membership information and uses kernel distance metric. To evaluate the proposed method, we conducted experiments on standard dataset and compared the results with state-of-the-art methods. We used cluster validity indices, accuracy and false positive rate as performance metrics. Experimental results inferred that, the proposed method achieves better results compared to other methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-10T10:48:18Z No. of bitstreams: 1 ijimai20174_6_8_pdf_14933.pdf: 892561 bytes, checksum: 8609d74a869467482d52129a57756be0 (MD5); Made available in DSpace on 2021-09-10T10:48:18Z (GMT). No. of bitstreams: 1 ijimai20174_6_8_pdf_14933.pdf: 892561 bytes, checksum: 8609d74a869467482d52129a57756be0 (MD5)

Heuristics Considering UX and Quality Criteria for Heuristics

Heuristics Considering UX and Quality Criteria for Heuristics Schön, Eva-Maria; Thomaschewski, Jörg; Bader, Frederik Heuristic evaluation is a cheap tool with which one can take qualitative measures of a product’s usability. However, since the methodology was first presented, the User Experience (UX) has become more popular but the heuristics have remained the same. In this paper, we analyse the current state of heuristic evaluation in terms of heuristics for measuring the UX. To do so, we carried out a literature review. In addition, we had a look at different heuristics and mapped them with the UX dimensions of the User Experience Questionnaire (UEQ). Moreover, we proposed a quality model for heuristic evaluation and a list of quality criteria for heuristics. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-10T10:38:40Z No. of bitstreams: 1 ijimai20174_6_7_pdf_74071.pdf: 438337 bytes, checksum: ec06a6b8ded9a8a8adba84030672a4ba (MD5); Made available in DSpace on 2021-09-10T10:38:40Z (GMT). No. of bitstreams: 1 ijimai20174_6_7_pdf_74071.pdf: 438337 bytes, checksum: ec06a6b8ded9a8a8adba84030672a4ba (MD5)

A Topic Modeling Guided Approach for Semantic Knowledge Discovery in e-Commerce

A Topic Modeling Guided Approach for Semantic Knowledge Discovery in e-Commerce Anoop, V S; Asharaf, S The task of mining large unstructured text archives, extracting useful patterns and then organizing them into a knowledgebase has attained a great attention due to its vast array of immediate applications in business. Businesses thus demand new and efficient algorithms for leveraging potentially useful patterns from heterogeneous data sources that produce huge volumes of unstructured data. Due to the ability to bring out hidden themes from large text repositories, topic modeling algorithms attained significant attention in the recent past. This paper proposes an efficient and scalable method which is guided by topic modeling for extracting concepts and relationships from e-commerce product descriptions and organizing them into knowledgebase. Semantic graphs can be generated from such a knowledgebase on which meaning aware product discovery experience can be built for potential buyers. Extensive experiments using proposed unsupervised algorithms with e-commerce product descriptions collected from open web shows that our proposed method outperforms some of the existing methods of leveraging concepts and relationships so that efficient knowledgebase construction is possible. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-10T10:03:40Z No. of bitstreams: 1 ijimai20174_6_6_pdf_86078.pdf: 855397 bytes, checksum: 73bde5096266e8180b4adc9ba7893928 (MD5); Made available in DSpace on 2021-09-10T10:03:40Z (GMT). No. of bitstreams: 1 ijimai20174_6_6_pdf_86078.pdf: 855397 bytes, checksum: 73bde5096266e8180b4adc9ba7893928 (MD5)

N-grams Based Supervised Machine Learning Model for Mobile Agent Platform Protection against Unknown Malicious Mobile Agents

N-grams Based Supervised Machine Learning Model for Mobile Agent Platform Protection against Unknown Malicious Mobile Agents Bagga, Pallavi; Hans, Rahul; Sharma, Vipul From many past years, the detection of unknown malicious mobile agents before they invade the Mobile Agent Platform has been the subject of much challenging activity. The ever-growing threat of malicious agents calls for techniques for automated malicious agent detection. In this context, the machine learning (ML) methods are acknowledged more effective than the Signature-based and Behavior-based detection methods. Therefore, in this paper, the prime contribution has been made to detect the unknown malicious mobile agents based on n-gram features and supervised ML approach, which has not been done so far in the sphere of the Mobile Agents System (MAS) security. To carry out the study, the n-grams ranging from 3 to 9 are extracted from a dataset containing 40 malicious and 40 non-malicious mobile agents. Subsequently, the classification is performed using different classifiers. A nested 5-fold cross validation scheme is employed in order to avoid the biasing in the selection of optimal parameters of classifier. The observations of extensive experiments demonstrate that the work done in this paper is suitable for the task of unknown malicious mobile agent detection in a Mobile Agent Environment, and also adds the ML in the interest list of researchers dealing with MAS security. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-10T09:51:53Z No. of bitstreams: 1 ijimai20174_6_5_pdf_30833.pdf: 1429461 bytes, checksum: 45aa6e8dd850207a23654d5b48998143 (MD5); Made available in DSpace on 2021-09-10T09:51:53Z (GMT). No. of bitstreams: 1 ijimai20174_6_5_pdf_30833.pdf: 1429461 bytes, checksum: 45aa6e8dd850207a23654d5b48998143 (MD5)

Multi-agent Systems for Arabic Handwriting Recognition

Multi-agent Systems for Arabic Handwriting Recognition Boulid, Youssef; Souhar, Abdelghani; Elyoussfi Elkettani, Mohamed This paper aims to give a presentation of the PhD defended by Boulid Youssef on December 26th, 2016 at University Ibn Tofail, entitled “Arabic handwritten recognition in an offline mode”. The adopted approach is realized under the multi agent paradigm. The dissertation was held in Faculty of Science Kénitra in a publicly open presentation. After the presentation, Boulid was awarded with the highest grade (Très honorable avec félicitations de jury). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-10T09:42:40Z No. of bitstreams: 1 ijimai20174_6_4_pdf_18367.pdf: 254854 bytes, checksum: 5c3871677035a3a73992fdc60c8d8763 (MD5); Made available in DSpace on 2021-09-10T09:42:40Z (GMT). No. of bitstreams: 1 ijimai20174_6_4_pdf_18367.pdf: 254854 bytes, checksum: 5c3871677035a3a73992fdc60c8d8763 (MD5)

A Novel Hybrid Approach for Fast Block Based Motion Estimation

A Novel Hybrid Approach for Fast Block Based Motion Estimation Arora, Shaifali; Khanna, Kavita; Rajpal, Navin The current work presents a novel hybrid approach for motion estimation of various video sequences with a purpose to speed up the entire process without affecting the accuracy. The method integrates the dynamic Zero motion pre-judgment (ZMP) technique with Initial search centers (ISC) along with half way search termination and Small diamond search pattern. Calculation of the initial search centers has been shifted after the process of zero motion pre-judgment unlike most the previous approaches so that the search centers for stationary blocks need not be identified. Proper identification of ISC dismisses the need to use any fast block matching algorithm (BMA) to find the motion vectors (MV), rather a fixed search pattern such as small diamond search pattern is sufficient to use. Half way search termination has also been incorporated into the algorithm which helps in deciding whether the predicted ISC is the actual MV or not which further reduced the number of computations. Simulation results of the complete hybrid approach have been compared to other standard methods in the field. The method presented in the manuscript ensures better video quality with fewer computations. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-10T09:03:57Z No. of bitstreams: 1 ijimai20174_6_3_pdf_76601.pdf: 1871630 bytes, checksum: ee27d175a812428bf1021e57fad48cc2 (MD5); Made available in DSpace on 2021-09-10T09:03:57Z (GMT). No. of bitstreams: 1 ijimai20174_6_3_pdf_76601.pdf: 1871630 bytes, checksum: ee27d175a812428bf1021e57fad48cc2 (MD5)

A Solution Generator Algorithm for Decision Making based Automated Negotiation in the Construction Domain

A Solution Generator Algorithm for Decision Making based Automated Negotiation in the Construction Domain Mahmoud, Moamin; Ahmad, Mohd Sharifuddin; Idrus, Arazi; Yahya, Azani; Husen, Hapsa In this paper, we present our work-in-progress of a proposed framework for automated negotiation in the construction domain. The proposed framework enables software agents to conduct negotiations and autonomously make value-based decisions. The framework consists of three main components which are, solution generator algorithm, negotiation algorithm, and conflict resolution algorithm. This paper extends the discussion on the solution generator algorithm that enables software agents to generate solutions and rank them from 1st to nth solution for the negotiation stage of the operation. The solution generator algorithm consists of three steps which are, review solutions, rank solutions, and form ranked solutions. For validation purpose, we present a scenario that utilizes the proposed algorithm to rank solutions. The validation shows that the algorithm is promising, however, it also highlights the conflict between different parties that needs further negotiation action. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-10T08:15:29Z No. of bitstreams: 1 ijimai20174_6_2_pdf_84357.pdf: 1197109 bytes, checksum: 4733f9742396fe97686c9256c5442711 (MD5); Made available in DSpace on 2021-09-10T08:15:29Z (GMT). No. of bitstreams: 1 ijimai20174_6_2_pdf_84357.pdf: 1197109 bytes, checksum: 4733f9742396fe97686c9256c5442711 (MD5)

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms Arora, Sankalap; Kaur, Ranjit Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-10T07:48:10Z No. of bitstreams: 1 ijimai20174_6_1_pdf_64773.pdf: 5349860 bytes, checksum: 3dd346a6f5c9dec7e98d980163d4170c (MD5); Made available in DSpace on 2021-09-10T07:48:11Z (GMT). No. of bitstreams: 1 ijimai20174_6_1_pdf_64773.pdf: 5349860 bytes, checksum: 3dd346a6f5c9dec7e98d980163d4170c (MD5)

Virtual Planning and Intraoperative Navigation in Craniomaxillofacial Surgery

Virtual Planning and Intraoperative Navigation in Craniomaxillofacial Surgery Cebrian Carretero, Jose Luis; Guiñales, Jorge; Burgueño García, Miguel Surgery planning assisted by computer represents one important example of the collaboration between surgeons and engineers. Virtual planning allows surgeons to pre-do the surgery by working over a virtual 3D model of the patient obtained through a computer tomography. Through surgical navigation, surgeons are helped while working with deep structures and can check if they are following accurately the surgical plan. These assistive tools are crucial in the field of facial reconstructive surgery. This paper describes two cases, one related to orbital fractures and another one related to oncological patients, showing the advantages that these tools provide, specifically when used for craniomaxillofacial surgery. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T11:40:49Z No. of bitstreams: 1 ijimai20174_5_12_pdf_10848.pdf: 820447 bytes, checksum: 880ee80e4f70548f734c954ae242ab62 (MD5); Made available in DSpace on 2021-09-06T11:40:49Z (GMT). No. of bitstreams: 1 ijimai20174_5_12_pdf_10848.pdf: 820447 bytes, checksum: 880ee80e4f70548f734c954ae242ab62 (MD5)

Influence of Lymphocyte T CD4 Levels on the Neuropsychological Performance of Population Affected by HIV and with a Previous History of Substance Use

Influence of Lymphocyte T CD4 Levels on the Neuropsychological Performance of Population Affected by HIV and with a Previous History of Substance Use Vázquez-Justo, Enrique; García-Torres, Amalia; Vergara-Moragues, Esperanza The immunological markers help to know if there is a good recovery of the immunological system in patients infected with HIV. Among them, the lymphocyte T CD4 rate is the main indicator of the patient’s immunological state being used for staging HIV infection, evaluating the mortality or comorbidity risk and the vulnerability to certain oportunistic infections. However, its link with the presence of cognitive alterations is not clear. Therefore, the aim of this article is to study if lymphocyte T CD4 levels are connected with the neuropsychological performance of a group of people infected with HIV and with a previous history of substance use. The sample consisted of 80 seropositive males with a previous history of substance use. They were evaluated by means of a neuropsychological battery which assesses the most affected cognitive domains in HIV population. The results showed that the patients having a higher level of immunodeficiency (CD4 <200/ mm3) have a poorer performance in terms of attention, visuomotor dexterity, visual memory, visual perception, auditory-verbal learning and inhibition. Therefore, our results show a realtion between the lymphocyte T CD4 rate and the neuropsychological performance in seropositive people with a previsous history of substance use. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T11:18:29Z No. of bitstreams: 1 ijimai20174_5_11_pdf_11000.pdf: 408431 bytes, checksum: 0318313c2142559347edb713c73c8ecc (MD5); Made available in DSpace on 2021-09-06T11:18:29Z (GMT). No. of bitstreams: 1 ijimai20174_5_11_pdf_11000.pdf: 408431 bytes, checksum: 0318313c2142559347edb713c73c8ecc (MD5)

Comparison of Feedforward Network and Radial Basis Function to Detect Leukemia

Comparison of Feedforward Network and Radial Basis Function to Detect Leukemia Bijalwan, Vishwanath; Balodhi, Meenu; Bagwari, Pragya; Saxena, Bhavya Leukemia is a fast growing cancer also called as blood cancer. It normally originates near bone marrow. The need for automatic leukemia detection system rises ever since the existing working methods include labor-intensive inspection of the blood marking as the initial step in the direction of diagnosis. This is very time consuming and also the correctness of the technique rest on the worker’s capability. This paper describes few image segmentation and feature extraction methods used for leukemia detection. Analyzing through images is very important as from images; diseases can be detected and diagnosed at earlier stage. From there, further actions like controlling, monitoring and prevention of diseases can be done. Images are used as they are cheap and do not require expensive testing and lab equipment. The system will focus on white blood cells disease, leukemia. Changes in features will be used as a classifier input. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T11:13:36Z No. of bitstreams: 1 ijimai20174_5_10_pdf_10782.pdf: 473393 bytes, checksum: 477ecc52a0289460d3eda1637015ab77 (MD5); Made available in DSpace on 2021-09-06T11:13:36Z (GMT). No. of bitstreams: 1 ijimai20174_5_10_pdf_10782.pdf: 473393 bytes, checksum: 477ecc52a0289460d3eda1637015ab77 (MD5)

A Revision of Preventive Web-based Psychotherapies in Subjects at Risk of Mental Disorders

A Revision of Preventive Web-based Psychotherapies in Subjects at Risk of Mental Disorders Sánchez-Gutiérrez, Teresa; Barbeito, Sara; Calvo, Ana For the last years, the impulse of new technologies has overcome the traditional pathways of face-to-face clinical intervention and web-based psychological methodologies for intervention have started to gain success. This study aims to review the state-of-art about the effectiveness studies on preventive web- based interventions accomplished in samples of subjects at high risk for depressive, anxiety, eating behavior, problematic substance use symptoms and promotion of psychological well-being. Results showed that web-based psychological interventions for the prevention of mental disorders seemed to be effective for at risk individuals. Online health promotion in the general population was also effective to avoid the onset of clinical psychological circumstances. Future research should focus on personalized online intervention and on the evaluation of web-based engagement. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T10:58:29Z No. of bitstreams: 1 ijimai20174_5_9_pdf_14588.pdf: 365947 bytes, checksum: da787637c2959697525955c2c4939dcb (MD5); Made available in DSpace on 2021-09-06T10:58:29Z (GMT). No. of bitstreams: 1 ijimai20174_5_9_pdf_14588.pdf: 365947 bytes, checksum: da787637c2959697525955c2c4939dcb (MD5)

Diagnosis of Malignant Melanoma of Skin Cancer Types

Diagnosis of Malignant Melanoma of Skin Cancer Types Alasadi, Abbas Hassin; Alsafy, Baidaa Malignant melanoma is a kind of skin cancer that begins in melanocytes. It can influence on the skin only, or it may expand to the bones and organs. It is less common, but more serious and aggressive than other types of skin cancer. Malignant Melanoma can happen anywhere on the skin, but it is widespread in certain locations such as the legs in women, the back and chest in men, the face, the neck, mouth, eyes, and genitals. In this paper, a proposed algorithm is designed for diagnosing malignant melanoma types by using digital image processing techniques. The algorithm consists of four steps: preprocessing, separation, features extraction, and diagnosis. A neural network (NN) used to diagnosis malignant melanoma types. The total accuracy of the neural network was 100% for training and 93% for testing. The evaluation of the algorithm is done by using sensitivity, specificity, and accuracy. The sensitivity of NN in diagnosing malignant melanoma types was 95.6%, while the specificity was 92.2% and the accuracy was 93.9%. The experimental results are acceptable. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T10:50:21Z No. of bitstreams: 1 ijimai20174_5_8_pdf_89993.pdf: 1034684 bytes, checksum: 9329f73e3f37b6721b688b3fc500299c (MD5); Made available in DSpace on 2021-09-06T10:50:21Z (GMT). No. of bitstreams: 1 ijimai20174_5_8_pdf_89993.pdf: 1034684 bytes, checksum: 9329f73e3f37b6721b688b3fc500299c (MD5)

Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors

Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors Asawa, Krishna; Gargava, Parth A Brain Computer Interface (BCI) is developed to navigate a micro-controller based robot using Emotiv sensors. The BCI system has a pipeline of 5 stages- signal acquisition, pre-processing, feature extraction, classification and CUDA inter- facing. It shall aid in serving a prototype for physical movement of neurological patients who are unable to control or operate on their muscular movements. All stages of the pipeline are designed to process bodily actions like eye blinks to command navigation of the robot. This prototype works on features learning and classification centric techniques using support vector machine. The suggested pipeline, ensures successful navigation of a robot in four directions in real time with accuracy of 93 percent. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T10:29:42Z No. of bitstreams: 1 ijimai20174_5_7_pdf_15281.pdf: 382642 bytes, checksum: db4209eb609820684284ad3d36af1bbb (MD5); Made available in DSpace on 2021-09-06T10:29:42Z (GMT). No. of bitstreams: 1 ijimai20174_5_7_pdf_15281.pdf: 382642 bytes, checksum: db4209eb609820684284ad3d36af1bbb (MD5)

Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network

Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network Esmaeilpour, Mansour; Abbasi, Rezvan Electroencephalogram signals (EEG) have always been used in medical diagnosis. Evaluation of the statistical characteristics of EEG signals is actually the foundation of all brain signal processing methods. Since the correct prediction of disease status is of utmost importance, the goal is to use those models that have minimum error and maximum reliability. In anautomatic epileptic seizure detection system, we should be able to distinguish between EEG signals before, during and after seizure. Extracting useful characteristics from EEG data can greatly increase the classification accuracy. In this new approach, we first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform(DWT) and then we derive statistical characteristics such as maximum, minimum, average and standard deviation for each sub-band. A multilayer perceptron (MLP)neural network was used to assess the different scenarios of healthy and seizure among the collected signal sets. In order to assess the success and effectiveness of the proposed method, the confusion matrix was used and its accuracy was achieved98.33 percent. Due to the limitations and obstacles in analyzing EEG signals, the proposed method can greatly help professionals experimentally and visually in the classification and diagnosis of epileptic seizures. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T10:21:55Z No. of bitstreams: 1 ijimai20174_5_6_pdf_12024.pdf: 1856037 bytes, checksum: f270cd3180ae7d780c3f2fe4ed7f02f3 (MD5); Made available in DSpace on 2021-09-06T10:21:55Z (GMT). No. of bitstreams: 1 ijimai20174_5_6_pdf_12024.pdf: 1856037 bytes, checksum: f270cd3180ae7d780c3f2fe4ed7f02f3 (MD5)

Masticatory System Biomechanical Photoelastic Simulation fot the Comparision of the Conventional and Uni-Lock Systems in Mandibular Osteosynthesis

Masticatory System Biomechanical Photoelastic Simulation fot the Comparision of the Conventional and Uni-Lock Systems in Mandibular Osteosynthesis Cebrian Carretero, Jose Luis; Carrascal Morillo, María Teresa; Vincent Fraile, Germán The biomechanical consequences of the interaction between titanium trauma plates and screws and the fractured mandible are still a matter of investigation. The mathematical and biomechanical models that have been developed show limitations and the experimental studies are not able to reproduce muscle forces and internal stress distributions in the bone-implant interface and mandibular structure. In the present article we show a static simulator of the masticatory system to demonstrate in epoxy resin mandibular models, by means of 3D (three-dimensional) photoelasticity, the stress distribution using different osteosynthesis methods in the mandibular angle fractures. The results showed that the simulator and 3D photoelasticity were a useful method to study interactions between bone and osteosynthesis materials. The “Lock” systems can be considered the most favourable method due to their stress distribution in the epoxy resin mandible. 3D photoelasticity in epoxy resin models is a useful method to evaluate stress distribution for biomechanical studies. Regarding to mandibular osteosynthesis, “lock” plates offer the most favourable stress distribution due to being less aggressive to the bone Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T09:59:00Z No. of bitstreams: 1 ijimai20174_5_5_pdf_31565.pdf: 1098548 bytes, checksum: 66a87d3f8f0f38c91118b597bc46aa0a (MD5); Made available in DSpace on 2021-09-06T09:59:00Z (GMT). No. of bitstreams: 1 ijimai20174_5_5_pdf_31565.pdf: 1098548 bytes, checksum: 66a87d3f8f0f38c91118b597bc46aa0a (MD5)

Contour Detection of Mammogram Masses Using ChanVese Model and B-Spline Approximation

Contour Detection of Mammogram Masses Using ChanVese Model and B-Spline Approximation Youssef, Youssef Ben; El Abdelmounim, hassane; Lamnii, Abdellah ChanVese model segmentation has been applied for contour detection of mass region in mammogram in our previous work. Available information of the desired object contour is used, in this paper, for B-spline approximation. The mass region boundary (contour) is thereafter approximated by a B-spline curve. This approach allows synthesizing the shape of the suspected mass appearing in the mammogram. Experimental results show the accurateness of mass region contour in mammograms using B-spline approximation. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T09:36:20Z No. of bitstreams: 1 ijimai20174_5_4_pdf_17629.pdf: 445945 bytes, checksum: f613634a22df96d931dd324f8c843f9a (MD5); Made available in DSpace on 2021-09-06T09:36:20Z (GMT). No. of bitstreams: 1 ijimai20174_5_4_pdf_17629.pdf: 445945 bytes, checksum: f613634a22df96d931dd324f8c843f9a (MD5)

The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM

The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM Esmaeilpour, Mansour; Gohariyan, Elham; Shirmohammadi, Mohammad Mehdi Breast cancer is one of the common cancers among women so that early diagnosing of it can effectively help its treatment in this study, considering combination of Mammography and MRI pictures, we will try to recognize glands in existing pictures which identify all around of gland complete and precisely and separate it completely. In this method using artificial intelligence algorithm such as Affine transformation, Gabor filter, neural network, and support vector machine, image analysis will be carried out. The accuracy of proposed method is 98.14. In this work a special framework is presented which simplifies cancer diagnosis. The algorithm of proposed method is tested on z16 images. High speed and lack of human error are the most important factors in proposed intelligent system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T09:14:10Z No. of bitstreams: 1 ijimai20174_5_3_pdf_18248.pdf: 963291 bytes, checksum: d0b6e3d2a34a55c062a7031684d754cb (MD5); Made available in DSpace on 2021-09-06T09:14:10Z (GMT). No. of bitstreams: 1 ijimai20174_5_3_pdf_18248.pdf: 963291 bytes, checksum: d0b6e3d2a34a55c062a7031684d754cb (MD5)

Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation Rezaie, Afsaneh Abdollahzadeh; Habiboghli, Ali In the present paper, a method for the detection of malignant and benign tumors on the CT scan images has been proposed. In the proposed method, firstly the area of interest in which the tumor may exist is selected on the original image and by the use of image segmentation and determination of the image’s threshold limit, the tumor’s area is specified and then edge detection filters are used for detection of the tumor’s edge. After detection of area and by calculating the fractal dimensions with less percent of errors and better resolution, the areas where contain the tumor are determined. The images used in the proposed method have been extracted from cancer imaging archive database that is made available for public. Compared to other methods, our proposed method recognizes successfully benign and malignant tumors in all cases that have been clinically approved and belong to the database. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T09:01:36Z No. of bitstreams: 1 ijimai20174_5_2_pdf_65326.pdf: 695612 bytes, checksum: 11006db3211ec94a20264c13bf2a514e (MD5); Made available in DSpace on 2021-09-06T09:01:36Z (GMT). No. of bitstreams: 1 ijimai20174_5_2_pdf_65326.pdf: 695612 bytes, checksum: 11006db3211ec94a20264c13bf2a514e (MD5)

Difusion-Weighted MRI: from Brownian Motion to Head&Neck Tumor Characterization

Difusion-Weighted MRI: from Brownian Motion to Head&Neck Tumor Characterization Utrilla Contreras, Cristina; Buitrago Sánchez, Nelson Mauricio; Graessner, Joachim; García Raya, Pilar; Marin Aguilera, Begoña This paper describes basic physics as well as clinical applications of diffusion-weighted magnetic resonance imaging. This is a technique that provides complementary information to conventional imaging sequences and it is applied in the field of oncologic imaging. This paper focuses on its specific application in head and neck, mainly in cancer patients, for characterization of primary tumors, and also for monitoring and predicting treatment response after chemotherapy or radiation therapy. Last, although diffusion-weighted imaging is shown to add value in several areas by being part of the multi-parametric magnetic resonance imaging approach, there are some unsolved challenges, which are proposed as future work. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T08:20:12Z No. of bitstreams: 1 ijimai20174_5_1_pdf_76297.pdf: 2676778 bytes, checksum: cf31a69e09ed2ebd24c205d9a6a402cd (MD5); Made available in DSpace on 2021-09-06T08:20:12Z (GMT). No. of bitstreams: 1 ijimai20174_5_1_pdf_76297.pdf: 2676778 bytes, checksum: cf31a69e09ed2ebd24c205d9a6a402cd (MD5)

IJIMAI Editor's Note - Vol. 4 Issue 5

IJIMAI Editor's Note - Vol. 4 Issue 5 Cebrian Carretero, Jose Luis Although Medicine has always been considered a Health Science, today it is not possible to obviate its relationship with other disciplines as Humanities and Basic Sciences. Doctors from everywhere and everyday work with the most sophisticated technology are trying to make their profession more accurate and precise, taking in consideration, at the same time, the human part of their daily labour. In this volume of the Journal, we will try to explore the relation between different medical specialities, basic science and engineering. In fact, modern Medicine requires the participation of these professionals who are involved with doctors in multidisciplinary teams. In this sense, Medical Engineering, is a new degree that is offered in a vast number of Universities along the world. This relationship between Medicine and Sciences can be found in any medical speciality so that, our aim in this volume, is to show different examples of doctors working together with other scientifics in any area of Medical sciences.The volume consists on twelve papers. Each paper explores a particular area of this multidisciplinary approach. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T07:51:11Z No. of bitstreams: 1 ijimai20174_5_0_pdf_10979.pdf: 107565 bytes, checksum: 734a030c3e6030ccaec14e2a06369738 (MD5); Made available in DSpace on 2021-09-06T07:51:11Z (GMT). No. of bitstreams: 1 ijimai20174_5_0_pdf_10979.pdf: 107565 bytes, checksum: 734a030c3e6030ccaec14e2a06369738 (MD5)

IJIMAI Editor's Note - Vol. 4 Issue 4

IJIMAI Editor's Note - Vol. 4 Issue 4 González-Crespo, Rubén The research works presented in this issue are based on various topics of interest, among which are included: Radar Clutter, Radar Detectors Performance, Butterfly Optimization Algorithm, Artificial Bee Colony, Evolutionary strategy, Fractal Coding, User Experience, Handwritten Arabic Character Recognition, Feature Extraction, Embedded Hidden Markov Models, Artificial Immune System, Hopfield Neural Network, Browsers, Multimedia, MoCap and Animations. The International Journal of Interactive Multimedia and Artificial Intelligence provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence and Interactive Multimedia techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-01T09:22:59Z No. of bitstreams: 1 ijimai20174_4_0_pdf_21297.pdf: 136392 bytes, checksum: 09a117c5edcf5d5f60455017fdeeb1d0 (MD5); Made available in DSpace on 2021-09-01T09:22:59Z (GMT). No. of bitstreams: 1 ijimai20174_4_0_pdf_21297.pdf: 136392 bytes, checksum: 09a117c5edcf5d5f60455017fdeeb1d0 (MD5)

Electronic Health Record in Bolivia and ICT: A Perspective for Latin America

Electronic Health Record in Bolivia and ICT: A Perspective for Latin America Gil, Eugenio; Medinaceli Díaz, Karina The emergence of new technologies in society through its application to many areas and very diverse realities is a clear element in the time in which we live. The health sector has been unable to escape this reality and has been renovated many of its traditional structures with new options brought by the application of information technology and communication (ICT) in areas such as management and hospital administration. This paper focuses on analyzing from the point of view of medical diagnosis the importance of electronic medical records as a unifying element of the information essential for this type of diagnosis, and the use of artificial intelligence techniques in this field. To this end the current situation of electronic medical records is analyzed in a country like Bolivia exhaustively analyzing three of the most important health centers. Is used for this unstructured interview experts on the subject reflect the current status of electronic medical records from the point of view of protection of the right to privacy of individuals and will serve as a model for development, not only in Bolivia but also in other Latin American countries. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-01T09:15:09Z No. of bitstreams: 1 ijimai20174_4_12_pdf_10368.pdf: 708446 bytes, checksum: 51063803031283f60fe20acc4d555473 (MD5); Made available in DSpace on 2021-09-01T09:15:09Z (GMT). No. of bitstreams: 1 ijimai20174_4_12_pdf_10368.pdf: 708446 bytes, checksum: 51063803031283f60fe20acc4d555473 (MD5)

Distributed Search Systems with Self-Adaptive Organizational Setups

Distributed Search Systems with Self-Adaptive Organizational Setups Wall, Friederike This paper studies the effects of learning-induced alterations of distributed search systems’ organizations. In particular, scenarios where alterations of the search-systems’ organizational setup are based on a form of reinforcement learning are compared to scenarios where the organizational setup is kept constant and to scenarios where the setup is changed randomly. The results indicate that learning-induced alterations may lead to high levels of performance combined with high levels of efficiency in terms of reorganization-effort. However, the results also suggest that the complexity of the underlying search problem together with the aspiration level (which drives positive or negative reinforcement) considerably shapes the effects of learning. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-01T08:25:43Z No. of bitstreams: 1 ijimai20174_4_11_pdf_14655.pdf: 2163734 bytes, checksum: 0dd5c562734cd510fab6695a21118aa3 (MD5); Made available in DSpace on 2021-09-01T08:25:43Z (GMT). No. of bitstreams: 1 ijimai20174_4_11_pdf_14655.pdf: 2163734 bytes, checksum: 0dd5c562734cd510fab6695a21118aa3 (MD5)

Simple MoCap System for Home Usage

Simple MoCap System for Home Usage Magdin, Martin Nowadays many MoCap systems exist. Generating 3D facial animation of characters is currently realized by using the motion capture data (MoCap data), which is obtained by tracking the facial markers from an actor/actress. In general it is a professional solution that is sophisticated and costly. This paper presents a solution with a system that is inexpensive. We propose a new easy-to-use system for home usage, through which we are making character animation. In its implementation we paid attention to the elimination of errors from the previous solutions. In this paper the authors describe the method how motion capture characters on a treadmill and as well as an own Java application that processes the video for its further use in Cinema 4D. This paper describes the implementation of this technology of sensing in a way so that the animated character authentically imitated human movement on a treadmill. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-01T07:53:56Z No. of bitstreams: 1 ijimai20174_4_10_pdf_20085.pdf: 1323395 bytes, checksum: c0b805954cd5d60e06ef1e0d28967eb4 (MD5); Made available in DSpace on 2021-09-01T07:53:56Z (GMT). No. of bitstreams: 1 ijimai20174_4_10_pdf_20085.pdf: 1323395 bytes, checksum: c0b805954cd5d60e06ef1e0d28967eb4 (MD5)

Exploring the Relevance of Search Engines: An Overview of Google as a Case Study

Exploring the Relevance of Search Engines: An Overview of Google as a Case Study Gaona-García, Paulo Alonso; Montenegro-Marin, Carlos Enrique; Beltrán-Alfonso, Ricardo; Torres-Tautiva, Andres The huge amount of data on the Internet and the diverse list of strategies used to try to link this information with relevant searches through Linked Data have generated a revolution in data treatment and its representation. Nevertheless, the conventional search engines like Google are kept as strategies with good reception to do search processes. The following article presents a study of the development and evolution of search engines, more specifically, to analyze the relevance of findings based on the number of results displayed in paging systems with Google as a case study. Finally, it is intended to contribute to indexing criteria in search results, based on an approach to Semantic Web as a stage in the evolution of the Web. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-01T07:35:15Z No. of bitstreams: 1 ijimai20174_4_9_pdf_20273.pdf: 997606 bytes, checksum: 45b3ccecea9408667a0fbc3290693e04 (MD5); Made available in DSpace on 2021-09-01T07:35:15Z (GMT). No. of bitstreams: 1 ijimai20174_4_9_pdf_20273.pdf: 997606 bytes, checksum: 45b3ccecea9408667a0fbc3290693e04 (MD5)

Robust Artificial Immune System in the Hopfield network for Maximum k-Satisfiability

Robust Artificial Immune System in the Hopfield network for Maximum k-Satisfiability Bin Mohd Kasihmuddin, Mohd Shareduwan; Bin Mansor, Mohd Asyraf; Sathasivam, Saratha Artificial Immune System (AIS) algorithm is a novel and vibrant computational paradigm, enthused by the biological immune system. Over the last few years, the artificial immune system has been sprouting to solve numerous computational and combinatorial optimization problems. In this paper, we introduce the restricted MAX-kSAT as a constraint optimization problem that can be solved by a robust computational technique. Hence, we will implement the artificial immune system algorithm incorporated with the Hopfield neural network to solve the restricted MAX-kSAT problem. The proposed paradigm will be compared with the traditional method, Brute force search algorithm integrated with Hopfield neural network. The results demonstrate that the artificial immune system integrated with Hopfield network outperforms the conventional Hopfield network in solving restricted MAX-kSAT. All in all, the result has provided a concrete evidence of the effectiveness of our proposed paradigm to be applied in other constraint optimization problem. The work presented here has many profound implications for future studies to counter the variety of satisfiability problem. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-01T07:26:43Z No. of bitstreams: 1 ijimai20174_4_8_pdf_92087.pdf: 879559 bytes, checksum: 6abe6d4ff7fc8d59886dfd6a78da9047 (MD5); Made available in DSpace on 2021-09-01T07:26:43Z (GMT). No. of bitstreams: 1 ijimai20174_4_8_pdf_92087.pdf: 879559 bytes, checksum: 6abe6d4ff7fc8d59886dfd6a78da9047 (MD5)

A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems Jalal, Ahmad; Kamal, Shaharyar; Kim, Daijin Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR) realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs) to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-30T09:57:59Z No. of bitstreams: 1 ijimai20174_4_7_pdf_14110.pdf: 1636117 bytes, checksum: f04c6ead3ff8b03e8a3170838932db5d (MD5); Made available in DSpace on 2021-08-30T09:57:59Z (GMT). No. of bitstreams: 1 ijimai20174_4_7_pdf_14110.pdf: 1636117 bytes, checksum: f04c6ead3ff8b03e8a3170838932db5d (MD5)

Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language

Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language Boulid, Youssef; Souhar, Abdelghani; Elyoussfi Elkettani, Mohamed A good Arabic handwritten recognition system must consider the characteristics of Arabic letters which can be explicit such as the presence of diacritics or implicit such as the baseline information (a virtual line on which cursive text are aligned and/join). In order to find an adequate method of features extraction, we have taken into consideration the nature of the Arabic characters. The paper investigate two methods based on two different visions: one describes the image in terms of the distribution of pixels, and the other describes it in terms of local patterns. Spatial Distribution of Pixels (SDP) is used according to the first vision; whereas Local Binary Patterns (LBP) are used for the second one. Tested on the Arabic portion of the Isolated Farsi Handwritten Character Database (IFHCDB) and using neural networks as a classifier, SDP achieve a recognition rate around 94% while LBP achieve a recognition rate of about 96%. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-30T09:40:28Z No. of bitstreams: 1 ijimai20174_4_6_pdf_26575.pdf: 2120039 bytes, checksum: 41a5c999103b8f0a5c30c313e02f503a (MD5); Made available in DSpace on 2021-08-30T09:40:28Z (GMT). No. of bitstreams: 1 ijimai20174_4_6_pdf_26575.pdf: 2120039 bytes, checksum: 41a5c999103b8f0a5c30c313e02f503a (MD5)

Construction of a Benchmark for the User Experience Questionnaire (UEQ)

Construction of a Benchmark for the User Experience Questionnaire (UEQ) Schrepp, Martin; Thomaschewski, Jörg; Hinderks, Andreas Questionnaires are a cheap and highly efficient tool for achieving a quantitative measure of a product’s user experience (UX). However, it is not always easy to decide, if a questionnaire result can really show whether a product satisfies this quality aspect. So a benchmark is useful. It allows comparing the results of one product to a large set of other products. In this paper we describe a benchmark for the User Experience Questionnaire (UEQ), a widely used evaluation tool for interactive products. We also describe how the benchmark can be applied to the quality assurance process for concrete projects. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-30T08:57:55Z No. of bitstreams: 1 ijimai20174_4_5_pdf_94297.pdf: 816087 bytes, checksum: eb41ed08fbc7ba9cbdd397f9ca996b41 (MD5); Made available in DSpace on 2021-08-30T08:57:55Z (GMT). No. of bitstreams: 1 ijimai20174_4_5_pdf_94297.pdf: 816087 bytes, checksum: eb41ed08fbc7ba9cbdd397f9ca996b41 (MD5)

Modified Three-Step Search Block Matching Motion Estimation and Weighted Finite Automata based Fractal Video Compression

Modified Three-Step Search Block Matching Motion Estimation and Weighted Finite Automata based Fractal Video Compression Kamble, Shailesh; Thakur, Nileshsingh; Bajaj, Preeti The major challenge with fractal image/video coding technique is that, it requires more encoding time. Therefore, how to reduce the encoding time is the research component remains in the fractal coding. Block matching motion estimation algorithms are used, to reduce the computations performed in the process of encoding. The objective of the proposed work is to develop an approach for video coding using modified three step search (MTSS) block matching algorithm and weighted finite automata (WFA) coding with a specific focus on reducing the encoding time. The MTSS block matching algorithm are used for computing motion vectors between the two frames i.e. displacement of pixels and WFA is used for the coding as it behaves like the Fractal Coding (FC). WFA represents an image (frame or motion compensated prediction error) based on the idea of fractal that the image has self-similarity in itself. The self-similarity is sought from the symmetry of an image, so the encoding algorithm divides an image into multi-levels of quad-tree segmentations and creates an automaton from the sub-images. The proposed MTSS block matching algorithm is based on the combination of rectangular and hexagonal search pattern and compared with the existing New Three-Step Search (NTSS), Three-Step Search (TSS), and Efficient Three-Step Search (ETSS) block matching estimation algorithm. The performance of the proposed MTSS block matching algorithm is evaluated on the basis of performance evaluation parameters i.e. mean absolute difference (MAD) and average search points required per frame. Mean of absolute difference (MAD) distortion function is used as the block distortion measure (BDM). Finally, developed approaches namely, MTSS and WFA, MTSS and FC, and Plane FC (applied on every frame) are compared with each other. The experimentations are carried out on the standard uncompressed video databases, namely, akiyo, bus, mobile, suzie, traffic, football, soccer, ice etc. Developed approaches are compared on the basis of performance evaluation parameters, namely, encoding time, decoding time, compression ratio and Peak Signal to Noise Ratio (PSNR). The video compression using MTSS and WFA coding performs better than MTSS and fractal coding, and frame by frame fractal coding in terms of achieving reduced encoding time and better quality of video. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-30T08:08:58Z No. of bitstreams: 1 ijimai20174_4_4_pdf_24716.pdf: 2654890 bytes, checksum: 09f25512e539637b268c528d4d25831a (MD5); Made available in DSpace on 2021-08-30T08:08:58Z (GMT). No. of bitstreams: 1 ijimai20174_4_4_pdf_24716.pdf: 2654890 bytes, checksum: 09f25512e539637b268c528d4d25831a (MD5)

A Solution to the N-Queens Problem Using Biogeography-Based Optimization

A Solution to the N-Queens Problem Using Biogeography-Based Optimization Habiboghli, Ali; Jalali, Tayebeh Biogeography-based Optimization (BBO) is a global optimization algorithm based on population, governed by mathematics of biogeography, and dealing with geographical distribution of biological organisms. The BBO algorithm was used in the present study to provide a solution for the N-queens problem. The performance of the proposed algorithm has been evaluated in terms of the quality of the obtained results, cost function, and execution time. Furthermore, the results of this algorithm were compared against those of genetic and particle swarm algorithms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-26T12:31:22Z No. of bitstreams: 1 ijimai20174_4_3_pdf_22761.pdf: 865721 bytes, checksum: 2e7b7ce8732b9d0b13050789489be65f (MD5); Made available in DSpace on 2021-08-26T12:31:22Z (GMT). No. of bitstreams: 1 ijimai20174_4_3_pdf_22761.pdf: 865721 bytes, checksum: 2e7b7ce8732b9d0b13050789489be65f (MD5)

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization Arora, Sankalap; Singh, Satvir In this paper, a new hybrid optimization algorithm which combines the standard Butterfly Optimization Algorithm (BOA) with Artificial Bee Colony (ABC) algorithm is proposed. The proposed algorithm used the advantages of both the algorithms in order to balance the trade-off between exploration and exploitation. Experiments have been conducted on the proposed algorithm using ten benchmark problems having a broad range of dimensions and diverse complexities. The simulation results demonstrate that the convergence speed and accuracy of the proposed algorithm in finding optimal solutions is significantly better than BOA and ABC. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-26T12:09:10Z No. of bitstreams: 1 ijimai20174_4_2_pdf_16914.pdf: 1855596 bytes, checksum: 61f04bbd0ce67d809c13ab724dc52518 (MD5); Made available in DSpace on 2021-08-26T12:09:10Z (GMT). No. of bitstreams: 1 ijimai20174_4_2_pdf_16914.pdf: 1855596 bytes, checksum: 61f04bbd0ce67d809c13ab724dc52518 (MD5)

CA-CFAR Adjustment Factor Correction with a priori Knowledge of the Clutter Distribution Shape Parameter

CA-CFAR Adjustment Factor Correction with a priori Knowledge of the Clutter Distribution Shape Parameter Machado-Fernández, José Raúl; Bacallao-Vidal, Jesús Concepción; Torres Martinez, Shirley Oceanic and coastal radars operation is affected because the targets information is received mixed with and undesired contribution called sea clutter. Specifically, the popular CA-CFAR processor is incapable of maintaining its design false alarm probability when facing clutter with statistical variations. In opposition to the classic alternative suggesting the use of a fixed adjustment factor, the authors propose a modification of the CA- CFAR scheme where the factor is constantly corrected according on the background signal statistical changes. Mathematically translated as a variation in the shape parameter of the clutter distribution, the background signal changes were simulated through the Weibull, Log-Normal and K distributions, deriving expressions which allow choosing an appropriate factor for each possible statistical state. The investigation contributes to the improvement of radar detection by suggesting the application of an adaptive scheme which assumes the clutter shape parameter is known a priori. The offered mathematical expressions are valid for three false alarm probabilities and several windows sizes, covering also a wide range of clutter conditions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-26T11:59:27Z No. of bitstreams: 1 ijimai20174_4_1_pdf_69507.pdf: 957739 bytes, checksum: 13c49f7fe5ae6e09039492fe600bbbd6 (MD5); Made available in DSpace on 2021-08-26T11:59:27Z (GMT). No. of bitstreams: 1 ijimai20174_4_1_pdf_69507.pdf: 957739 bytes, checksum: 13c49f7fe5ae6e09039492fe600bbbd6 (MD5)

Techniques to Detect DoS and DDoS Attacks and an Introduction of a Mobile Agent System to Enhance it in Cloud Computing

Techniques to Detect DoS and DDoS Attacks and an Introduction of a Mobile Agent System to Enhance it in Cloud Computing Saidi, Abdelali; Bendriss, Elmehdi; Kartit, Ali; El Marraki, Mohamed Security in cloud computing is the ultimate question that every potential user studies before adopting it. Among the important points that the provider must ensure is that the Cloud will be available anytime the consumer tries to access it. Generally, the Cloud is accessible via the Internet, what makes it subject to a large variety of attacks. Today, the most striking cyber-attacks are the flooding DoS and its variant DDoS. This type of attacks aims to break down the availability of a service to its legitimate clients. In this paper, we underline the most used techniques to stand up against DoS flooading attacks in the Cloud. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-25T11:28:39Z No. of bitstreams: 1 ijimai20174_3_12_pdf_18434.pdf: 424695 bytes, checksum: 80619174bcf2b426a578ebe1cea358e9 (MD5); Made available in DSpace on 2021-08-25T11:28:39Z (GMT). No. of bitstreams: 1 ijimai20174_3_12_pdf_18434.pdf: 424695 bytes, checksum: 80619174bcf2b426a578ebe1cea358e9 (MD5)

Use Trust Management Framework to Achieve Effective Security Mechanisms in Cloud Environment

Use Trust Management Framework to Achieve Effective Security Mechanisms in Cloud Environment Toumi, Hicham; Marzak, Bouchra; Talea, Amal; Eddaoui, Ahmed; Talea, Mohamed Cloud Computing is an Internet based Computing where virtual shared servers provide software, infrastructure, platform and other resources to the customer on pay-as-you-use basis. Cloud Computing is increasingly becoming popular as many enterprise applications and data are moving into cloud platforms. However, with the enormous use of Cloud, the probability of occurring intrusion also increases. There is a major need of bringing security, transparency and reliability in cloud model for client satisfaction. One of the security issues is how to reduce the impact of any type of intrusion in this environment. To address this issue, a security solution is proposed in this paper. We provide a collaborative framework between our Hybrid Intrusion Detection System (Hy-IDS) based on Mobile Agents and virtual firewalls. Therefore, our hybrid intrusion detection system consists of three types of IDS namely IDS-C, IDS-Cr and IDS-M, which are dispatched over three layer of cloud computing. In the first layer, we use IDS-C over our framework to collect, analyze and detect malicious data using Mobile Agents. In case of attack, we collect at the level of the second layer all the malicious data detected in the first layer for the generation of new signatures using IDS-Cr, which is based on a Signature Generation Algorithm (SGA) and network intrusion detection system (NIDS). Finally, through an IDS-M placed in the third layer, the new signatures will be used to update the database NIDS belonging to IDS-Cr, then the database to NIDS belonging of IDS-Cr the cluster neighboring and also their IDS-C. Hardware firewall is unable to control communication between virtual machines on the same hypervisor. Moreover, they are blind to virtual traffic. Mostly, they are deployed at Virtual Machine Monitor- level (VMM) under Cloud provider’s control. Equally, the mobile agents play an important role in this collaboration. They are used in our framework for investigation of hosts, transfer data malicious and transfer update of a database of neighboring IDS in the cloud. With this technique, the neighboring IDS will use these new signatures to protect their area of control against the same type of attack. By this type of close-loop control, the collaborative network security management framework can identify and address new distributed attacks more quickly and effectively. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-25T11:03:57Z No. of bitstreams: 1 ijimai20174_3_11_pdf_15245.pdf: 582327 bytes, checksum: 455f95d6419a11d6f17fbdc31c3ac6e3 (MD5); Made available in DSpace on 2021-08-25T11:03:57Z (GMT). No. of bitstreams: 1 ijimai20174_3_11_pdf_15245.pdf: 582327 bytes, checksum: 455f95d6419a11d6f17fbdc31c3ac6e3 (MD5)

A MAS-Based Cloud Service Brokering System to Respond Security Needs of Cloud Customers

A MAS-Based Cloud Service Brokering System to Respond Security Needs of Cloud Customers Talbi, Jamal; Haqiq, Abdelkrim Cloud computing is becoming a key factor in computer science and an important technology for many organizations to deliver different types of services. The companies which provide services to customers are called as cloud service providers. The cloud users (CUs) increase and require secure, reliable and trustworthy cloud service providers (CSPs) from the market. So, it’s a challenge for a new customer to choose the highly secure provider. This paper presents a cloud service brokering system in order to analyze and rank the secured cloud service provider among the available providers list. This model uses an autonomous and flexible agent in multi-agent system (MASs) that have an intelligent behavior and suitable tools for helping the brokering system to assess the security risks for the group of cloud providers which make decision of the more secured provider and justify the business needs of users in terms of security and reliability. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-25T10:45:34Z No. of bitstreams: 1 ijimai20174_3_10_pdf_18425.pdf: 514212 bytes, checksum: d119adcda0e3154776289465b4abf421 (MD5); Made available in DSpace on 2021-08-25T10:45:34Z (GMT). No. of bitstreams: 1 ijimai20174_3_10_pdf_18425.pdf: 514212 bytes, checksum: d119adcda0e3154776289465b4abf421 (MD5)

Securing Cloud Computing from Different Attacks Using Intrusion Detection Systems

Securing Cloud Computing from Different Attacks Using Intrusion Detection Systems Achbarou, Omar; El kiram, My Ahmed; El Bouanani, Salim Cloud computing is a new way of integrating a set of old technologies to implement a new paradigm that creates an avenue for users to have access to shared and configurable resources through internet on-demand. This system has many common characteristics with distributed systems, hence, the cloud computing also uses the features of networking. Thus the security is the biggest issue of this system, because the services of cloud computing is based on the sharing. Thus, a cloud computing environment requires some intrusion detection systems (IDSs) for protecting each machine against attacks. The aim of this work is to present a classification of attacks threatening the availability, confidentiality and integrity of cloud resources and services. Furthermore, we provide literature review of attacks related to the identified categories. Additionally, this paper also introduces related intrusion detection models to identify and prevent these types of attacks. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-25T10:26:57Z No. of bitstreams: 1 ijimai20174_3_9_pdf_60303.pdf: 695166 bytes, checksum: 410cca118f02b28b0ed757db4843a535 (MD5); Made available in DSpace on 2021-08-25T10:26:57Z (GMT). No. of bitstreams: 1 ijimai20174_3_9_pdf_60303.pdf: 695166 bytes, checksum: 410cca118f02b28b0ed757db4843a535 (MD5)

Analysis of Security Mechanisms Based on Clusters IoT Environments

Analysis of Security Mechanisms Based on Clusters IoT Environments Montenegro-Marin, Carlos Enrique; Gaona-García, Paulo Alonso; Prieto, Juan David; Nieto Acevedo, Yuri Vanessa Internet of things is based on sensors, communication networks and intelligence that manages the entire process and the generated data. Sensors are the senses of systems, because of this, they can be used in large quantities. Sensors must have low power consumption and cost, small size and great flexibility for its use in all circumstances. Therefore, the security of these network devices, data sensors and other devices, is a major concern as it grows rapidly in terms of nodes interconnected via sensor data. This paper presents an analysis from a systematic review point of view of articles on Internet of Things (IoT), security aspects specifically at privacy level and control access in this type of environment. Finally, it presents an analysis of security issues that must be addressed, from different clusters and identified areas within the fields of application of this technology. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-25T08:59:45Z No. of bitstreams: 1 ijimai20174_3_8_pdf_20224.pdf: 545586 bytes, checksum: e893e697bb6a2e05e4455d1da03f0c68 (MD5); Made available in DSpace on 2021-08-25T08:59:45Z (GMT). No. of bitstreams: 1 ijimai20174_3_8_pdf_20224.pdf: 545586 bytes, checksum: e893e697bb6a2e05e4455d1da03f0c68 (MD5)

Taxonomies for Reasoning About Cyber-physical Attacks in IoT-based Manufacturing Systems

Taxonomies for Reasoning About Cyber-physical Attacks in IoT-based Manufacturing Systems Pan, Yao; White, Jules; Schmidt, Douglas; Elhabashy, Ahmad; Sturm, Logan; Camelio, Jaime; Williams, Christopher The Internet of Things (IoT) has transformed many aspects of modern manufacturing, from design to production to quality control. In particular, IoT and digital manufacturing technologies have substantially accelerated product development- cycles and manufacturers can now create products of a complexity and precision not heretofore possible. New threats to supply chain security have arisen from connecting machines to the Internet and introducing complex IoT-based systems controlling manufacturing processes. By attacking these IoT-based manufacturing systems and tampering with digital files, attackers can manipulate physical characteristics of parts and change the dimensions, shapes, or mechanical properties of the parts, which can result in parts that fail in the field. These defects increase manufacturing costs and allow silent problems to occur only under certain loads that can threaten safety and/or lives. To understand potential dangers and protect manufacturing system safety, this paper presents two taxonomies: one for classifying cyber-physical attacks against manufacturing processes and another for quality control measures for counteracting these attacks. We systematically identify and classify possible cyber-physical attacks and connect the attacks with variations in manufacturing processes and quality control measures. Our taxonomies also provide a scheme for linking emerging IoT-based manufacturing system vulnerabilities to possible attacks and quality control measures. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-19T12:36:05Z No. of bitstreams: 1 ijimai20174_3_7_pdf_53997.pdf: 2228524 bytes, checksum: a307ab650960548be2a91cb5462ecf59 (MD5); Made available in DSpace on 2021-08-19T12:36:05Z (GMT). No. of bitstreams: 1 ijimai20174_3_7_pdf_53997.pdf: 2228524 bytes, checksum: a307ab650960548be2a91cb5462ecf59 (MD5)

Migrating C/C++ Software to Mobile Platforms in the ADM Context

Migrating C/C++ Software to Mobile Platforms in the ADM Context Martinez, Liliana; Pereira, Claudia; Favre, Liliana Software technology is constantly evolving and therefore the development of applications requires adapting software components and applications in order to be aligned to new paradigms such as Pervasive Computing, Cloud Computing and Internet of Things. In particular, many desktop software components need to be migrated to mobile technologies. This migration faces many challenges due to the proliferation of different mobile platforms. Developers usually make applications tailored for each type of device expending time and effort. As a result, new programming languages are emerging to integrate the native behaviors of the different platforms targeted in development projects. In this direction, the Haxe language allows writing mobile applications that target all major mobile platforms. Novel technical frameworks for information integration and tool interoperability such as Architecture-Driven Modernization (ADM) proposed by the Object Management Group (OMG) can help to manage a huge diversity of mobile technologies. The Architecture-Driven Modernization Task Force (ADMTF) was formed to create specifications and promote industry consensus on the modernization of existing applications. In this work, we propose a migration process from C/C++ software to different mobile platforms that integrates ADM standards with Haxe. We exemplify the different steps of the process with a simple case study, the migration of “the Set of Mandelbrot” C++ application. The proposal was validated in Eclipse Modeling Framework considering that some of its tools and run-time environments are aligned with ADM standards. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-19T12:00:49Z No. of bitstreams: 1 ijimai20174_3_6_pdf_64266.pdf: 1297721 bytes, checksum: 160f133fe6d0512742abe6ea1479d3dc (MD5); Made available in DSpace on 2021-08-19T12:00:49Z (GMT). No. of bitstreams: 1 ijimai20174_3_6_pdf_64266.pdf: 1297721 bytes, checksum: 160f133fe6d0512742abe6ea1479d3dc (MD5)

Conceptual Model for Smart Cities: Irrigation and Highway Lamps using IoT

Conceptual Model for Smart Cities: Irrigation and Highway Lamps using IoT Solanki, Vijender Kumar; Venkaesan, M; Katiyar, Somesh Keeping in mind the need to preserve energy as well as utilize the available at its best the need was felt to develop a module that would be able to sort out the problem where resources such as water and electricity were wasted, in urban as well as rural area. Resource (electricity) was wasted as beside the point operation of Highway & High Mast Lamp; while wastage of water followed by improper trends and methodologies imparted for watering of city park, road side plantation and highway plantation. Thus as per Energy survey statistics of a City (Lucknow, India) it was found that major portion of resources (water and electricity) were being wasted due to negligent activities of officials who were in charge of resource management. So to facilitate energy saving trends and to completely modernize it to autonomous system, module below is proposed which incorporates modern technological peripheral and has its base ingrained in IoT (Internet of Things) which when put into consideration would result in large scale resource and energy saving.This developed module incorporates the peripherals such as Arduino, Texas Instruments ultra low power kits etc. in accordance with software technology including Lab View which help to monitor as well as control the various operation from the base station, located far away from the site. Lab View Interface interacts with all the module located at various city parks, subways and highway lighting modules. Later below in several section a detailed pattern and application frame has been put up. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-19T11:27:41Z No. of bitstreams: 1 ijimai20174_3_5_pdf_17263.pdf: 754383 bytes, checksum: dc63671370052c38dc8d0b3dc17dcd25 (MD5); Made available in DSpace on 2021-08-19T11:27:41Z (GMT). No. of bitstreams: 1 ijimai20174_3_5_pdf_17263.pdf: 754383 bytes, checksum: dc63671370052c38dc8d0b3dc17dcd25 (MD5)

Performance Evaluation of AODV Routing Protocol in VANET with NS2

Performance Evaluation of AODV Routing Protocol in VANET with NS2 Rathi, Divya; Welekar, Rashmi In intelligent transportation systems, the collaboration between vehicles and the road side units is essential to bring these systems to realization. The emerging Vehicular Ad Hoc Network (VANET) is becoming more and more important as it provides intelligent transportation application, comfort, safety, entertainment for people in vehicles. In order to provide stable routes and to get good performance in VANET, there is a need of proper routing protocols must be designed. In this paper, we are working with the very well-known ad-hoc on-demand distance vector (AODV) routing protocol. The existing Routing protocol AODV-L which is based on the Link expiration time is extended to propose a more reliable AODV-AD which is based on multichannel MAC protocol. For the performance evaluation of routing protocols, a simulation tool ‘NS2’ has been used. Simulation results show that the proposed AODV-AD protocol can achieves better performances in forms of high Route stability, Packet Delivery ratio and packet loss rate than traditional AODV-L and traditional AODV. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-19T10:46:54Z No. of bitstreams: 1 ijimai20174_3_4_pdf_60277.pdf: 644997 bytes, checksum: e03050ebe8ba07cdbd9fda3b09d3ae21 (MD5); Made available in DSpace on 2021-08-19T10:46:54Z (GMT). No. of bitstreams: 1 ijimai20174_3_4_pdf_60277.pdf: 644997 bytes, checksum: e03050ebe8ba07cdbd9fda3b09d3ae21 (MD5)

An IoT Based Predictive Connected Car Maintenance Approach

An IoT Based Predictive Connected Car Maintenance Approach Solanki, Vijender Kumar; Dhall, Rohit Internet of Things (IoT) is fast emerging and becoming an almost basic necessity in general life. The concepts of using technology in our daily life is not new, but with the advancements in technology, the impact of technology in daily activities of a person can be seen in almost all the aspects of life. Today, all aspects of our daily life, be it health of a person, his location, movement, etc. can be monitored and analyzed using information captured from various connected devices. This paper discusses one such use case, which can be implemented by the automobile industry, using technological advancements in the areas of IoT and Analytics. ‘Connected Car’ is a terminology, often associated with cars and other passenger vehicles, which are capable of internet connectivity and sharing of various kinds of data with backend applications. The data being shared can be about the location and speed of the car, status of various parts/lubricants of the car, and if the car needs urgent service or not. Once data are transmitted to the backend services, various workflows can be created to take necessary actions, e.g. scheduling a service with the car service provider, or if large numbers of care are in the same location, then the traffic management system can take necessary action. ’Connected cars’ can also communicate with each other, and can send alerts to each other in certain scenarios like possible crash etc. This paper talks about how the concept of ‘connected cars’ can be used to perform ‘predictive car maintenance’. It also discusses how certain technology components, i.e., Eclipse Mosquito and Eclipse Paho can be used to implement a predictive connected car use case. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-19T09:39:38Z No. of bitstreams: 1 ijimai20174_3_3_pdf_12469.pdf: 1749944 bytes, checksum: a97a1cce138bc42de1309c519b43184a (MD5); Made available in DSpace on 2021-08-19T09:39:38Z (GMT). No. of bitstreams: 1 ijimai20174_3_3_pdf_12469.pdf: 1749944 bytes, checksum: a97a1cce138bc42de1309c519b43184a (MD5)

An analytic Study of the Key Factors Influencing the Design and Routing Techniques of a Wireless Sensor Network

An analytic Study of the Key Factors Influencing the Design and Routing Techniques of a Wireless Sensor Network Punetha, Deepak; Bahuguna, Yogita; Verma, Pooja A wireless sensor network contains various nodes having certain sensing, processing & communication capabilities. Actually they are multifunctional battery operated nodes called motes. These motes are small in size & battery constrained. They are operated by a power source. A wireless sensor network consists of a huge number of tiny sensor nodes which are deployed either randomly or according to some predefined distribution. The sensors nodes in a sensor network are cooperative among themselves having self-organizing ability. This ensures that a wireless network serves a wide variety of applications. Few of them are weather monitoring, health, security & military etc. As their applications are wide, this requires that sensors in a sensor network must play their role very efficiently. But, as discussed above, the sensor nodes have energy limitation. This limitation leads failure of nodes after certain round of communication. So, a sensor network suffers with sensors having energy limitations. Beside this, sensor nodes in a sensor network must fulfill connectivity & coverage requirements. In this paper, we have discussed various issues affecting the design of a wireless sensor network. This provides the readers various research issues in designing a wireless sensor network. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-19T09:33:38Z No. of bitstreams: 1 ijimai20174_3_2_pdf_19641.pdf: 685669 bytes, checksum: 4ae65293a62857a1617c571a1d0c374a (MD5); Made available in DSpace on 2021-08-19T09:33:38Z (GMT). No. of bitstreams: 1 ijimai20174_3_2_pdf_19641.pdf: 685669 bytes, checksum: 4ae65293a62857a1617c571a1d0c374a (MD5)

A review about Smart Objects, Sensors, and Actuators

A review about Smart Objects, Sensors, and Actuators González García, Cristian; Meana-Llorián, Daniel; Pelayo García-Bustelo, B. Cristina; Cueva-Lovelle, Juan Manuel Smart Objects and the Internet of Things are two ideas which describe the future, walk together, and complement each other. Thus, the interconnection among objects can make them more intelligent or expand their intelligence to unsuspected limits. This could be achieved with a new network that interconnects each object around the world. However, to achieve this goal, the objects need a network that supports heterogeneous and ubiquitous objects, a network where exists more traffic among objects than among humans, but supporting for both types. For these reasons, both concepts are very close. Cities, houses, cars, machines, or any another object that can sense, respond, work, or make easier the lives of their owner. This is a part of the future, an immediate future. Notwithstanding, first of all, there are to resolve a series of problems. The most important problem is the heterogeneity of objects. This article is going to show a theoretical frame and the related work about Smart Object. The article will explain what are Smart Objects, doing emphasis in their difference with Not- Smart Objects. After, we will present one of the different object classification system, in our opinion, the most complete. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2021-08-19T08:39:57Z No. of bitstreams: 1 ijimai20174_3_1_pdf_15525.pdf: 429222 bytes, checksum: 17a5ecca66ee772722b067a54ca1dcf9 (MD5); Made available in DSpace on 2021-08-19T08:39:57Z (GMT). No. of bitstreams: 1 ijimai20174_3_1_pdf_15525.pdf: 429222 bytes, checksum: 17a5ecca66ee772722b067a54ca1dcf9 (MD5)

IJIMAI Editor's Note - Vol. 4 Issue 3

IJIMAI Editor's Note - Vol. 4 Issue 3 García-Díaz, Vicente The Internet of Things is the networks of physical devices, embedded with electronics, software, sensors, actuators and security and connectivity mechanisms that enables them to collect and exchange data. It is a very important research topic nowadays in which many scientific papers are focusing on its bases. This Special Issue tries to show some of the latest researches related to IoT with special emphasis on the basic components of IoT, some of the major applications in which researchers and practitioners are working and especially in aspects related to security, one of the main areas of research related to IoT, with a special emphasis on cloud-based systems. Next, I present a summary of the works that are included in this special issue. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2021-08-19T08:13:17Z No. of bitstreams: 1 ijimai20174_3_0_pdf_20281.pdf: 129462 bytes, checksum: ce7330eed4a1912a329674ddbe406445 (MD5); Made available in DSpace on 2021-08-19T08:13:17Z (GMT). No. of bitstreams: 1 ijimai20174_3_0_pdf_20281.pdf: 129462 bytes, checksum: ce7330eed4a1912a329674ddbe406445 (MD5)

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding Kamble, Shailesh; Thakur, Nileshsingh; Bajaj, Preeti Fractal compression is the lossy compression technique in the field of gray/color image and video compression. It gives high compression ratio, better image quality with fast decoding time but improvement in encoding time is a challenge. This review paper/article presents the analysis of most significant existing approaches in the field of fractal based gray/color images and video compression, different block matching motion estimation approaches for finding out the motion vectors in a frame based on inter-frame coding and intra-frame coding i.e. individual frame coding and automata theory based coding approaches to represent an image/sequence of images. Though different review papers exist related to fractal coding, this paper is different in many sense. One can develop the new shape pattern for motion estimation and modify the existing block matching motion estimation with automata coding to explore the fractal compression technique with specific focus on reducing the encoding time and achieving better image/video reconstruction quality. This paper is useful for the beginners in the domain of video compression. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-18T12:21:32Z No. of bitstreams: 1 ijimai20164_2_14_pdf_10496.pdf: 2373672 bytes, checksum: e53e9e5ed2591a416635d72ffd9dc941 (MD5); Made available in DSpace on 2021-08-18T12:21:32Z (GMT). No. of bitstreams: 1 ijimai20164_2_14_pdf_10496.pdf: 2373672 bytes, checksum: e53e9e5ed2591a416635d72ffd9dc941 (MD5)

Design and Implementation of a Combinatorial Optimization Multi-population Meta-heuristic for Solving Vehicle Routing Problems

Design and Implementation of a Combinatorial Optimization Multi-population Meta-heuristic for Solving Vehicle Routing Problems Osaba, Eneko; Díaz, Fernando This paper aims to give a presentation of the PhD defended by Eneko Osaba on November 16th, 2015, at the University of Deusto. The thesis can be placed in the field of artificial intelligence. Specifically, it is related with multi- population meta-heuristics for solving vehicle routing problems. The dissertation was held in the main auditorium of the University, in a publicly open presentation. After the presentation, Eneko was awarded with the highest grade (cum laude). Additionally, Eneko obtained the PhD obtaining award granted by the Basque Government through. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-18T12:14:06Z No. of bitstreams: 1 ijimai20164_2_13_pdf_59073.pdf: 319910 bytes, checksum: 81d038ea5224138b810baf50dddf886c (MD5); Made available in DSpace on 2021-08-18T12:14:06Z (GMT). No. of bitstreams: 1 ijimai20164_2_13_pdf_59073.pdf: 319910 bytes, checksum: 81d038ea5224138b810baf50dddf886c (MD5)

Intelligent e-Learning Systems: An Educational Paradigm Shift

Intelligent e-Learning Systems: An Educational Paradigm Shift Bhattacharya, Suman; Nath, Sayan Learning is the long process of transforming information as well as experience into knowledge, skills, attitude and behaviors. To make up the wide gap between the demand of increasing higher education and comparatively limited resources, more and more educational institutes are looking into instructional technology. Use of online resources not only reduces the cost of education but also meet the needs of society. Intelligent e-learning has become one of the important channels to reach out to students exceeding geographic boundaries. Besides this, the characteristics of e-learning have complicated the process of education, and have brought challenges to both instructors and students. This paper will focus on the discussion of different discipline of intelligent e-learning like scaffolding based e-learning, personalized e-learning, confidence based e-learning, intelligent tutoring system, etc. to illuminate the educational paradigm shift in intelligent e-learning system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-18T12:02:38Z No. of bitstreams: 1 ijimai20164_2_12_pdf_14815.pdf: 338266 bytes, checksum: de197b27d52887839f622c09c08fef86 (MD5); Made available in DSpace on 2021-08-18T12:02:38Z (GMT). No. of bitstreams: 1 ijimai20164_2_12_pdf_14815.pdf: 338266 bytes, checksum: de197b27d52887839f622c09c08fef86 (MD5)

Novel Agent Based-approach for Industrial Diagnosis: A Combined use Between Case-based Reasoning and Similarity Measure

Novel Agent Based-approach for Industrial Diagnosis: A Combined use Between Case-based Reasoning and Similarity Measure Taghezout, Noria; Benkaddour, Fatima Zohra; Ascar, Bouabdellah In spunlace nonwovens industry, the maintenance task is very complex, it requires experts and operators collaboration. In this paper, we propose a new approach integrating an agent- based modelling with case-based reasoning that utilizes similarity measures and preferences module. The main purpose of our study is to compare and evaluate the most suitable similarity measure for our case. Furthermore, operators that are usually geographically dispersed, have to collaborate and negotiate to achieve mutual agreements, especially when their proposals (diagnosis) lead to a conflicting situation. The experimentation shows that the suggested agent-based approach is very interesting and efficient for operators and experts who collaborate in INOTIS enterprise. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-18T11:48:57Z No. of bitstreams: 1 ijimai20164_2_11_pdf_12710.pdf: 814074 bytes, checksum: 9b2ae5aef83afbb1eb8b3039a54fc2a9 (MD5); Made available in DSpace on 2021-08-18T11:48:57Z (GMT). No. of bitstreams: 1 ijimai20164_2_11_pdf_12710.pdf: 814074 bytes, checksum: 9b2ae5aef83afbb1eb8b3039a54fc2a9 (MD5)

Integrating Agents into a Collaborative Knowledge-based System for Business Rules Consistency Management

Integrating Agents into a Collaborative Knowledge-based System for Business Rules Consistency Management Houari, Nawal Sad; Taghezout, Noria Capitalization and reuse of expert knowledge are very important for the survival of an enterprise. This paper presents a collaborative approach that utilizes domain ontology and agents. Thanks to our knowledge formalizing process, we give to domain expert an opportunity to store different forms of retrieved knowledge from experiences, design rules, business rules, decision processes, etc. The ontology is built to support business rules management. The global architecture is mainly composed of agents such as Expert agent, Evaluator agent, Translator agent, Security agent and Supervisor agent. The Evaluator agent is at the heart of our functional architecture, its role is to detect the problems that may arise in the consistency management module and provides a solution to these problems in order to validate the accuracy of business rules. In addition, a Security agent is defined to handle both security aspects in rules modeling and multi-agent system. The proposed approach is different from the others in terms of the number of rule’s inconsistencies which are detected and treated like contradiction, redundancy, invalid rules, domain violation and rules never applicable, the collaboration that is initiated among business experts and the guarantee of security of the business rules and all the agents which constitute our system. The developed collaborative system is applied in an industrial case study.C Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-18T11:01:40Z No. of bitstreams: 1 ijimai20164_2_10_pdf_59746.pdf: 912441 bytes, checksum: ba6781da90f413e90b3c4611ee0d8de6 (MD5); Made available in DSpace on 2021-08-18T11:01:40Z (GMT). No. of bitstreams: 1 ijimai20164_2_10_pdf_59746.pdf: 912441 bytes, checksum: ba6781da90f413e90b3c4611ee0d8de6 (MD5)

Genetic Algorithm for Restricted Maximum k-Satisfiability in the Hopfield Network

Genetic Algorithm for Restricted Maximum k-Satisfiability in the Hopfield Network Kasihmuddin, Mohd Shareduwan Bin Mohd; Mansor, Mohd Asyraf Bin; Sathasivam, Saratha The restricted Maximum k-Satisfiability MAX- kSAT is an enhanced Boolean satisfiability counterpart that has attracted numerous amount of research. Genetic algorithm has been the prominent optimization heuristic algorithm to solve constraint optimization problem. The core motivation of this paper is to introduce Hopfield network incorporated with genetic algorithm in solving MAX-kSAT problem. Genetic algorithm will be integrated with Hopfield network as a single network. The proposed method will be compared with the conventional Hopfield network. The results demonstrate that Hopfield network with genetic algorithm outperforms conventional Hopfield networks. Furthermore, the outcome had provided a solid evidence of the robustness of our proposed algorithms to be used in other satisfiability problem. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-08-18T10:27:56Z No. of bitstreams: 1 ijimai20164_2_9_pdf_33484.pdf: 569824 bytes, checksum: 82fa89cb20fe45a2cb3f4b41004bcde5 (MD5); Made available in DSpace on 2021-08-18T10:27:56Z (GMT). No. of bitstreams: 1 ijimai20164_2_9_pdf_33484.pdf: 569824 bytes, checksum: 82fa89cb20fe45a2cb3f4b41004bcde5 (MD5)

Euclidean Distance Distortion Based Robust and Blind Mesh Watermarking

Euclidean Distance Distortion Based Robust and Blind Mesh Watermarking Dey, Nilanjan; Amar, Yesmine Ben; Trabelsi, Imen; Bouhlel, Salim The three-dimensional (3D) polygonal meshes are recently widely used in several domains, which necessitate the realistic visualization of the objects. Moreover, there is an urgent need to protect the 3D data properties for preventing unauthorized reproduction. The 3D digital watermarking technology is one of the best solutions to protect data from piracy during transmission through the internet. The current work proposed a novel robust watermarking scheme of polygonal meshes for copyright protection purposes. The proposed algorithm is based on the characteristics of the mesh geometry to embed a sequence of data bits into the object by slightly adjusting the vertex positions. Furthermore, the proposed method used a blind detection scheme. The watermarked model is perceptually indistinguishable from the original one and the embedded watermark is invariant to affine transformation. Through simulations, the quality of the watermarked object as well as the inserted watermark robustness against various types of attacks were tested and evaluated to prove the validity and the efficiency of our algorithm. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-30T12:19:30Z No. of bitstreams: 1 ijimai20164_2_8_pdf_17392.pdf: 566963 bytes, checksum: dfddc5c92ea1e7ec9754e8fbc61f48bf (MD5); Made available in DSpace on 2021-07-30T12:19:30Z (GMT). No. of bitstreams: 1 ijimai20164_2_8_pdf_17392.pdf: 566963 bytes, checksum: dfddc5c92ea1e7ec9754e8fbc61f48bf (MD5)

Temporal Information Processing and Stability Analysis of the MHSN Neuron Model in DDF

Temporal Information Processing and Stability Analysis of the MHSN Neuron Model in DDF Choudhary, Saket Kumar; Singh, Karan Implementation of a neuron like information processing structure at hardware level is a burning research problem. In this article, we analyze the modified hybrid spiking neuron model (the MHSN model) in distributed delay framework (DDF) for hardware level implementation point of view. We investigate its temporal information processing capability in term of inter-spike-interval (ISI) distribution. We also perform the stability analysis of the MHSN model, in which, we compute nullclines, steady state solution, eigenvalues corresponding the MHSN model. During phase plane analysis, we notice that the MHSN model generates limit cycle oscillations which is an important phenomenon in many biological processes. Qualitative behavior of these limit cycle does not changes due to the variation in applied input stimulus, however, delay effect the spiking activity and duration of cycle get altered. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-30T11:51:41Z No. of bitstreams: 1 ijimai20164_2_7_pdf_14081.pdf: 2129610 bytes, checksum: da3f388af06535374024b64a897909ec (MD5); Made available in DSpace on 2021-07-30T11:51:41Z (GMT). No. of bitstreams: 1 ijimai20164_2_7_pdf_14081.pdf: 2129610 bytes, checksum: da3f388af06535374024b64a897909ec (MD5)

Erythrocyte Features for Malaria Parasite Detection in Microscopic Images of Thin Blood Smear: A Review

Erythrocyte Features for Malaria Parasite Detection in Microscopic Images of Thin Blood Smear: A Review Devi, Salam Shuleenda; Sheikh, Shah Alam; Laskar, Rabul Hussain Microscopic image analysis of blood smear plays a very important role in characterization of erythrocytes in screening of malaria parasites. The characteristics feature of erythrocyte changes due to malaria parasite infection. The microscopic features of the erythrocyte include morphology, intensity and texture. In this paper, the different features used to differentiate the non- infected and malaria infected erythrocyte have been reviewed. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-30T11:13:39Z No. of bitstreams: 1 ijimai20164_2_6_pdf_16718.pdf: 322419 bytes, checksum: 6a69637a566b053de70598cbafaf3f31 (MD5); Made available in DSpace on 2021-07-30T11:13:39Z (GMT). No. of bitstreams: 1 ijimai20164_2_6_pdf_16718.pdf: 322419 bytes, checksum: 6a69637a566b053de70598cbafaf3f31 (MD5)

Push Recovery for Humanoid Robot in Dynamic Environment and Classifying the Data Using K-Mean

Push Recovery for Humanoid Robot in Dynamic Environment and Classifying the Data Using K-Mean Parashar, Anubha; Parashar, Apoorva; Goyal, Somya Push recovery is prime ability that is essential to be incorporated in the process of developing a robust humanoid robot to support bipedalism. In real environment it is very essential for humanoid robot to maintain balance. In this paper we are generating a control system and push recovery controller for humanoid robot walking. We apply different kind of pushes to humanoid robot and the algorithm that can bring a change in the walking stage to sustain walking. The simulation is done in 3D environment using Webots. This paper describes techniques for feature selection to foreshow push recovery for hip, ankle and knee joint. We train the system by K-Mean algorithm and testing is done on crouch data and tested results are reported. Random push data of humanoid robot is collected and classified to see whether push lie in safer region and then tested on given proposed system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-30T10:31:17Z No. of bitstreams: 1 ijimai20164_2_5_pdf_12005.pdf: 867542 bytes, checksum: 3d709f1ebe08c84a8087013a438ca16c (MD5); Made available in DSpace on 2021-07-30T10:31:17Z (GMT). No. of bitstreams: 1 ijimai20164_2_5_pdf_12005.pdf: 867542 bytes, checksum: 3d709f1ebe08c84a8087013a438ca16c (MD5)

Face Detection for Augmented Reality Application Using Boosting-based Techniques

Face Detection for Augmented Reality Application Using Boosting-based Techniques Hbali, Youssef; Ballihi, Lahoucine; Sadgal, Mohammed; Abdelaziz, El Fazziki Augmented reality has gained an increasing research interest over the few last years. Customers requirements have become more intense and more demanding, the need of the different industries to re-adapt their products and enhance them by recent advances in the computer vision and more intelligence has become a necessary. In this work we present a marker-less augmented reality application that can be used and expanded in the e-commerce industry. We take benefit of the well known boosting techniques to train and evaluate different face detectors using the multi-block local binary features. The work purpose is to select the more relevant training parameters in order to maximize the classification accuracy. Using the resulted face detector, the position of the face will serve as a marker in the proposed augmented reality. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-15T10:49:44Z No. of bitstreams: 1 ijimai20164_2_4_pdf_92526.pdf: 1689500 bytes, checksum: abda75ffcf1d33d37b4a091413f84ba2 (MD5); Made available in DSpace on 2021-07-15T10:49:44Z (GMT). No. of bitstreams: 1 ijimai20164_2_4_pdf_92526.pdf: 1689500 bytes, checksum: abda75ffcf1d33d37b4a091413f84ba2 (MD5)

Feature Selection for Image Retrieval based on Genetic Algorithm

Feature Selection for Image Retrieval based on Genetic Algorithm Welekar, Rashmi; Kushwaha, Preeti This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level co- occurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-15T10:10:11Z No. of bitstreams: 1 ijimai20164_2_3_pdf_46881.pdf: 643004 bytes, checksum: ca89f2beb5d1858e23721109561e8474 (MD5); Made available in DSpace on 2021-07-15T10:10:11Z (GMT). No. of bitstreams: 1 ijimai20164_2_3_pdf_46881.pdf: 643004 bytes, checksum: ca89f2beb5d1858e23721109561e8474 (MD5)

Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks

Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks Bacallao-Vidal, Jesús Concepción; Machado-Fernández, José Raúl The main problem faced by naval radars is the elimination of the clutter input which is a distortion signal appearing mixed with target reflections. Recently, the Pareto distribution has been related to sea clutter measurements suggesting that it may provide a better fit than other traditional distributions. The authors propose a new method for estimating the Pareto shape parameter based on artificial neural networks. The solution achieves a precise estimation of the parameter, having a low computational cost, and outperforming the classic method which uses Maximum Likelihood Estimates (MLE). The presented scheme contributes to the development of the NATE detector for Pareto clutter, which uses the knowledge of clutter statistics for improving the stability of the detection, among other applications. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-15T10:03:45Z No. of bitstreams: 1 ijimai20164_2_1_pdf_46986.pdf: 765872 bytes, checksum: 5ff45ba141750ebfea501766cb2441d6 (MD5); Made available in DSpace on 2021-07-15T10:03:45Z (GMT). No. of bitstreams: 1 ijimai20164_2_1_pdf_46986.pdf: 765872 bytes, checksum: 5ff45ba141750ebfea501766cb2441d6 (MD5)

IJIMAI Editor's Note - Vol. 4 Issue 2

IJIMAI Editor's Note - Vol. 4 Issue 2 Verdú, Elena The research works presented in this regular issue cover different fields of application such as medicine, industry or education, proposing solutions based on various topics of interest, as for example: neural networks, neuro-fuzzy systems, case-based reasoning systems, image retrieval, classification, feature selection, meta-heuristics, constraint satisfaction, or knowledge-based systems. The International Journal of Interactive Multimedia and Artificial Intelligence provides an interdisciplinary forum in which scientists and professionals share their research results and report new advances on Artificial Intelligence tools and tools that use Artificial Intelligence with interactive multimedia techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-15T09:48:22Z No. of bitstreams: 1 ijimai20164_2_0_pdf_27843.pdf: 133661 bytes, checksum: e81eb60ad7130c72351735585358d288 (MD5); Made available in DSpace on 2021-07-15T09:48:22Z (GMT). No. of bitstreams: 1 ijimai20164_2_0_pdf_27843.pdf: 133661 bytes, checksum: e81eb60ad7130c72351735585358d288 (MD5)

Analyzing the EEG Signals in Order to Estimate the Depth of Anesthesia using Wavelet and Fuzzy Neural Networks

Analyzing the EEG Signals in Order to Estimate the Depth of Anesthesia using Wavelet and Fuzzy Neural Networks Esmaeilpour, Mansour; Mohammadi, Ali Reis Ali Estimating depth of Anesthesia in patients with the objective to administer the right dosage of drug has always attracted the attention of specialists. To study Anesthesia, researchers analyze brain waves since this is the place which is directly affected by the drug. This study aimed to estimate the depth of Anesthesia using electroencephalogram (EGG) signals, wavelet transform, and adaptive Neuro Fuzzy inference system (ANFIS). ANFIS can estimate the depth of Anesthesia with high accuracy. A set of EEG signals regarding consciousness, moderate Anesthesia, deep Anesthesia, and iso-electric point were collected from the American Society of Anesthesiologists (ASA) and PhysioNet. First, the extracted features were combined using wavelet and spectral analysis after which the target features were selected. Later, the features were classified into four categories. The results obtained revealed that the accuracy of the proposed method was 98.45%. Since the visual analysis of EEG signals is difficult, the proposed method can significantly help anesthesiologists estimate the depth of Anesthesia. Further, the results showed that ANFIS could significantly increase the accuracy of Anesthesia depth estimation. Finally, the system was deemed to be advantageous since it was also capable of updating in real-time situations as well. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-15T09:24:44Z No. of bitstreams: 1 ijimai20164_2_2_pdf_16114.pdf: 2759022 bytes, checksum: b004239a3cca81ed2384e043bddb508b (MD5); Made available in DSpace on 2021-07-15T09:24:44Z (GMT). No. of bitstreams: 1 ijimai20164_2_2_pdf_16114.pdf: 2759022 bytes, checksum: b004239a3cca81ed2384e043bddb508b (MD5)

Comparison between Famous Game Engines and Eminent Games

Comparison between Famous Game Engines and Eminent Games Mishra, Prerna; Shrawankar, Urmila Nowadays game engines are imperative for building 3D applications and games. This is for the reason that the engines appreciably reduce resources for employing obligatory but intricate utilities. This paper elucidates about a game engine, popular games developed by these engines and its foremost elements. It portrays a number of special kinds of contemporary game developed by engines in the way of their aspects, procedure and deliberates their stipulations with comparison. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-15T09:07:34Z No. of bitstreams: 1 ijimai20164_1_13_pdf_63301.pdf: 1739519 bytes, checksum: 5c2c79aedf439d880ec344e9f946a3d3 (MD5); Made available in DSpace on 2021-07-15T09:07:34Z (GMT). No. of bitstreams: 1 ijimai20164_1_13_pdf_63301.pdf: 1739519 bytes, checksum: 5c2c79aedf439d880ec344e9f946a3d3 (MD5)

Correlation Between Coupling Metrics Values and Number of Classes in Multimedia Java Projects: A Case Study

Correlation Between Coupling Metrics Values and Number of Classes in Multimedia Java Projects: A Case Study Bivde, V S; Sarasu, P Coupling is an interdependence relationship between the modules of object-oriented software. It is a property with the most influence on quality attributes of the object-oriented software. Coupling with high values results in complex software design hence software professionals try to keep the coupling as low as possible. The values of coupling metrics are dependent on the type of input source code. Reusability is the main feature of object-oriented languages, so coupling occurs due to reuse of code modules. This paper investigates a correlation between the values of coupling metrics and the number of classes in the multimedia Java code. Here, a case study of a banking multimedia Java project with its forty different versions is conducted to comments on this correlation. The analysis of the results shows that, if the input source code is with a large number of classes then it results in high coupling values. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-14T12:34:03Z No. of bitstreams: 0; Made available in DSpace on 2021-07-14T12:34:03Z (GMT). No. of bitstreams: 0

Evaluating the Emotional State of a User Using a Webcam

Evaluating the Emotional State of a User Using a Webcam Magdin, Martin; Turcani, Milan; Hudec, Lukas In online learning is more difficult for teachers identify to see how individual students behave. Student’s emotions like self-esteem, motivation, commitment, and others that are believed to be determinant in student’s performance can not be ignored, as they are known (affective states and also learning styles) to greatly influence student’s learning. The ability of the computer to evaluate the emotional state of the user is getting bigger attention. By evaluating the emotional state, there is an attempt to overcome the barrier between man and non-emotional machine. Recognition of a real time emotion in e-learning by using webcams is research area in the last decade. Improving learning through webcams and microphones offers relevant feedback based upon learner’s facial expressions and verbalizations. The majority of current software does not work in real time – scans face and progressively evaluates its features. The designed software works by the use neural networks in real time which enable to apply the software into various fields of our lives and thus actively influence its quality. Validation of face emotion recognition software was annotated by using various experts. These expert findings were contrasted with the software results. An overall accuracy of our software based on the requested emotions and the recognized emotions is 78%. Online evaluation of emotions is an appropriate technology for enhancing the quality and efficacy of e-learning by including the learner´s emotional states. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-14T12:24:01Z No. of bitstreams: 1 ijimai20164_1_12_pdf_20682.pdf: 574723 bytes, checksum: 6754097ab397e538598834b8a217dd14 (MD5); Made available in DSpace on 2021-07-14T12:24:01Z (GMT). No. of bitstreams: 1 ijimai20164_1_12_pdf_20682.pdf: 574723 bytes, checksum: 6754097ab397e538598834b8a217dd14 (MD5)

IJIMAI Editor's Note - Vol. 4 Issue 1

IJIMAI Editor's Note - Vol. 4 Issue 1 Bahaj, Mohamed The research works presented in this issue are based on various topics of interest, among which are included: Pattern Recognition, Multimedia Information Retrieval, Knowledge extraction and knowledge mining, Data mining, Intelligent Systems & Artificial Intelligence, Wireless Technology, Network Telecommunication, Security & Network Management, Advanced Network Technologies. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-14T12:09:00Z No. of bitstreams: 1 ijimai20164_1_0_pdf_95835.pdf: 145706 bytes, checksum: 8fafdd96430bbebd399d73497e3fa642 (MD5); Made available in DSpace on 2021-07-14T12:09:00Z (GMT). No. of bitstreams: 1 ijimai20164_1_0_pdf_95835.pdf: 145706 bytes, checksum: 8fafdd96430bbebd399d73497e3fa642 (MD5)

Solving the Weighted Constraint Satisfaction Problems Via the Neural Network Approach

Solving the Weighted Constraint Satisfaction Problems Via the Neural Network Approach Ettaouil, Mohamed; Haddouch, Khalid; Elmoutaoukil, Karim A wide variety of real world optimization problems can be modelled as Weighted Constraint Satisfaction Problems (WCSPs). In this paper, we model this problem in terms of in original 0-1 quadratic programming subject to leaner constraints. View it performance, we use the continuous Hopfield network to solve the obtained model basing on original energy function. To validate our model, we solve several instance of benchmarking WCSP. In this regard, our approach recognizes the optimal solution of the said instances. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-14T11:34:23Z No. of bitstreams: 1 ijimai20164_1_11_pdf_42425.pdf: 1161738 bytes, checksum: a4a814ec90a8a9fa881788f7c869b1d6 (MD5); Made available in DSpace on 2021-07-14T11:34:23Z (GMT). No. of bitstreams: 1 ijimai20164_1_11_pdf_42425.pdf: 1161738 bytes, checksum: a4a814ec90a8a9fa881788f7c869b1d6 (MD5)

PI Stabilization for Congestion Control of AQM Routers with Tuning Parameter Optimization

PI Stabilization for Congestion Control of AQM Routers with Tuning Parameter Optimization Chebli, S; El Akkary, Ahmed In this paper, we consider the problem of stabilizing network using a new proportional- integral (PI) based congestion controller in active queue management (AQM) router; with appropriate model approximation in the first order delay systems, we seek a stability region of the controller by using the Hermite- Biehler theorem, which isapplicable to quasipolynomials. A Genetic Algorithm technique is employed to derive optimal or near optimal PI controller parameters. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-14T10:50:24Z No. of bitstreams: 1 ijimai20164_1_10_pdf_18562.pdf: 2282480 bytes, checksum: f9682b7011573900517e96c73e1a0373 (MD5); Made available in DSpace on 2021-07-14T10:50:24Z (GMT). No. of bitstreams: 1 ijimai20164_1_10_pdf_18562.pdf: 2282480 bytes, checksum: f9682b7011573900517e96c73e1a0373 (MD5)

Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Task

Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Task Settouti, Nesma; El Amine Bechar, Mohammed; Amine Chikh, Mohammed This work is builds on the study of the 10 top data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) community in December 2006. We address the same study, but with the application of statistical tests to establish, a more appropriate and justified ranking classifier for classification tasks. Current studies and practices on theoretical and empirical comparison of several methods, approaches, advocated tests that are more appropriate. Thereby, recent studies recommend a set of simple and robust non-parametric tests for statistical comparisons classifiers. In this paper, we propose to perform non-parametric statistical tests by the Friedman test with post-hoc tests corresponding to the comparison of several classifiers on multiple data sets. The tests provide a better judge for the relevance of these algorithms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-07T12:30:51Z No. of bitstreams: 1 ijimai20164_1_9_pdf_19943.pdf: 791619 bytes, checksum: 692cbe571e5c46d528cd66fb1a5d2e2b (MD5); Made available in DSpace on 2021-07-07T12:30:51Z (GMT). No. of bitstreams: 1 ijimai20164_1_9_pdf_19943.pdf: 791619 bytes, checksum: 692cbe571e5c46d528cd66fb1a5d2e2b (MD5)

A Distributed Intelligent System for Emergency Convoy

A Distributed Intelligent System for Emergency Convoy Benalla, Mohammed; Achchab, Boujemâa; Hrimech, Hamid The general problem that guides this research is the ability to design a distributed intelligent system for guiding the emergency convoys; a solution that will be based on a group of agents and on the analysis of traffic in order to generate collective functional response. It fits into the broader issue of Distributed Artificial System (DAI), which is to operate a cooperatively computer agent into multi-agents system (MAS). This article describes conceptually two fundamental questions of emergency convoys. The first question is dedicated to find a response to the traffic situation (i.e. fluid way), while the second is devoted to the convoy orientation; while putting the point on the distributed and cooperative resolution for the general problem. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-07T12:02:53Z No. of bitstreams: 1 ijimai20164_1_8_pdf_22976.pdf: 1051032 bytes, checksum: 59275d1c104b9e34ad38f85c61b490ff (MD5); Made available in DSpace on 2021-07-07T12:02:53Z (GMT). No. of bitstreams: 1 ijimai20164_1_8_pdf_22976.pdf: 1051032 bytes, checksum: 59275d1c104b9e34ad38f85c61b490ff (MD5)

MaxHopCount: A New Drop Policy to Optimize Messages Delivery Rate in Delay Tolerant Networks

MaxHopCount: A New Drop Policy to Optimize Messages Delivery Rate in Delay Tolerant Networks Harrati, Youssef; Abdali, Abdelmounaim Communication has become a necessity, not only between every point on the earth, but also on the globe. That includes hard topography, highlands, underwater areas, and also space- crafts on other planets. However, the classic wired internet cannot be implemented in such areas, hence, researchers have invented wireless networks. The big challenge for wireless networking nowadays, is maintaining nodes connected in some difficult conditions, such as intermittent connectivity, power failure, and lot of obstacles for the interplanetary networks. In these challenging circumstances, a new networking model arises; it is Delay Tolerant networking which is based on the Store-Carry-and-Forward mechanism. Thus, a node may keep a message in its buffer for long periods of time; until a delivery or forward chance arises then it transmit it to other nodes. One of the big issues that confront this mechanism is the congestion of nodes buffer due to the big number of messages and the limited buffer size. Here, researchers have proposed buffer management algorithms in order to deal with the buffer overload problem, and they called it Drop Policies. In our present work, we propose a new Drop policy which we have compared to other existing policies in different conditions and with different routing protocols, and it always shows good result in term of number of delivered messages, network overhead and also average of latency. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-07T11:20:28Z No. of bitstreams: 1 ijimai20164_1_7_pdf_15922.pdf: 1094550 bytes, checksum: 6ebe3e875ad2ec225b61eb3edec39c74 (MD5); Made available in DSpace on 2021-07-07T11:20:28Z (GMT). No. of bitstreams: 1 ijimai20164_1_7_pdf_15922.pdf: 1094550 bytes, checksum: 6ebe3e875ad2ec225b61eb3edec39c74 (MD5)

Detection of Text Lines of Handwritten Arabic Manuscripts using Markov Decision Processes

Detection of Text Lines of Handwritten Arabic Manuscripts using Markov Decision Processes Boulid, Youssef; Souhar, Abdelghani; Elyoussfi Elkettani, Mohamed In a character recognition systems, the segmentation phase is critical since the accuracy of the recognition depend strongly on it. In this paper we present an approach based on Markov Decision Processes to extract text lines from binary images of Arabic handwritten documents. The proposed approach detects the connected components belonging to the same line by making use of knowledge about features and arrangement of those components. The initial results show that the system is promising for extracting Arabic handwritten lines. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-07T11:02:10Z No. of bitstreams: 1 ijimai20164_1_6_pdf_34205.pdf: 1472159 bytes, checksum: d6ff52a2433f187fb0050fb9d5cfbefe (MD5); Made available in DSpace on 2021-07-07T11:02:10Z (GMT). No. of bitstreams: 1 ijimai20164_1_6_pdf_34205.pdf: 1472159 bytes, checksum: d6ff52a2433f187fb0050fb9d5cfbefe (MD5)

Multilayer Perceptron: Architecture Optimization and Training

Multilayer Perceptron: Architecture Optimization and Training Ramchoun, Hassan; Ghanou, Youssef; Ettaouil, Mohamed; Janati Idrissi, Mohammed Amine The multilayer perceptron has a large wide of classification and regression applications in many fields: pattern recognition, voice and classification problems. But the architecture choice has a great impact on the convergence of these networks. In the present paper we introduce a new approach to optimize the network architecture, for solving the obtained model we use the genetic algorithm and we train the network with a back-propagation algorithm. The numerical results assess the effectiveness of the theoretical results shown in this paper, and the advantages of the new modeling compared to the previous model in the literature. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-07T10:37:59Z No. of bitstreams: 1 ijimai20164_1_5_pdf_30533.pdf: 1396853 bytes, checksum: 86a1788f74a4d54fa0daf8990c74723b (MD5); Made available in DSpace on 2021-07-07T10:37:59Z (GMT). No. of bitstreams: 1 ijimai20164_1_5_pdf_30533.pdf: 1396853 bytes, checksum: 86a1788f74a4d54fa0daf8990c74723b (MD5)

A Self-Calibration Method of Zooming Camera

A Self-Calibration Method of Zooming Camera El Batteoui, Ismail; Saaidi, Abderrahim; Satori, Khalid In this article we proposed a novel approach to self- calibrate a camera with variable focal length. We show that the estimation of camera’s intrinsic parameters is possible from only two points of an unknown planar scene. The projection of these points by using the projection matrices in two images only permit us to obtain a system of equations according to the camera’s intrinsic parameters . From this system we formulated a nonlinear cost function which its minimization allows us to estimate the camera’s intrinsic parameters in each view. The results on synthetic and real data justify the robustness of our method in term of reliability and convergence. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-07T09:48:28Z No. of bitstreams: 1 ijimai20164_1_4_pdf_94156.pdf: 1459478 bytes, checksum: 40f444fd2dbb2363fc3be783e7b98399 (MD5); Made available in DSpace on 2021-07-07T09:48:28Z (GMT). No. of bitstreams: 1 ijimai20164_1_4_pdf_94156.pdf: 1459478 bytes, checksum: 40f444fd2dbb2363fc3be783e7b98399 (MD5)

Golden Ball Algorithm for solving Flow Shop Scheduling Problem

Golden Ball Algorithm for solving Flow Shop Scheduling Problem Sayoti, Fatima; Essaid Riffi, Mohammed The Flow Shop Scheduling Problem (FSSP) is notoriously NP-hard combinatorial optimization problem. The goal is to find a schedule that minimizes the makespan. This paper proposes an adaptation of a new approach called Golden Ball Algorithm (GBA). The proposed algorithm has been never tested with FSSP; it’s based on soccer concept to obtain the optimal solution. Numerical results are presented for 22 instances of OR- Library. The computational results indicate that this approach is practical for small OR-Library instances. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-07T09:38:36Z No. of bitstreams: 1 ijimai20164_1_3_pdf_32338.pdf: 776822 bytes, checksum: 58fdd873a689174d44ca4486a8b45861 (MD5); Made available in DSpace on 2021-07-07T09:38:36Z (GMT). No. of bitstreams: 1 ijimai20164_1_3_pdf_32338.pdf: 776822 bytes, checksum: 58fdd873a689174d44ca4486a8b45861 (MD5)

Offline Face Recognition System Based on GaborFisher Descriptors and Hidden Markov Models

Offline Face Recognition System Based on GaborFisher Descriptors and Hidden Markov Models Elgarrai, Zineb; Elmeslouhi, Othmane; Kardouchi, Mustapha; Allali, Hakim; Selouani, Sid-Ahmed This paper presents a new offline face recognition system. The proposed system is built on one dimensional left-to- right Hidden Markov Models (1D-HMMs). Facial image features are extracted using Gabor wavelets. The dimensionality of these features is reduced using the Fisher’s Discriminant Analysis method to keep only the most relevant information. Unlike existing techniques using 1D-HMMs, in classification step, the proposed system employs 1D-HMMs to find the relationship between reduced features components directly without any additional segmentation step of interest regions in the face image. The performance evaluation of the proposed method was performed with AR database and the proposed method showed a high recognition rate for this database. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-07T09:29:22Z No. of bitstreams: 1 ijimai20164_1_2_pdf_27472.pdf: 1109380 bytes, checksum: 103a2581b15386f95d5275d2dc31ae67 (MD5); Made available in DSpace on 2021-07-07T09:29:22Z (GMT). No. of bitstreams: 1 ijimai20164_1_2_pdf_27472.pdf: 1109380 bytes, checksum: 103a2581b15386f95d5275d2dc31ae67 (MD5)

Segmentation-free Word Spotting for Handwritten Arabic Documents

Segmentation-free Word Spotting for Handwritten Arabic Documents Khaissidi, Ghizlane; Elfakir, Youssef; Mrabti, Mostafa; El Yacoubi, Mounîm; Chenouni, Driss In this paper we present an unsupervised segmentation-free method for spotting and searching query, especially, for images documents in handwritten Arabic, for this, Histograms of Oriented Gradients (HOGs) are used as the feature vectors to represent the query and documents image. Then, we compress the descriptors with the product quantization method. Finally, a better representation of the query is obtained by using the Support Vector Machines (SVM). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-07-07T09:18:45Z No. of bitstreams: 1 ijimai20164_1_1_pdf_95245.pdf: 1694903 bytes, checksum: 08127b4ffd49d73236de8faf6cc1fa9d (MD5); Made available in DSpace on 2021-07-07T09:18:45Z (GMT). No. of bitstreams: 1 ijimai20164_1_1_pdf_95245.pdf: 1694903 bytes, checksum: 08127b4ffd49d73236de8faf6cc1fa9d (MD5)

IJIMAI Editor's Note - Vol. 3 Issue 7

IJIMAI Editor's Note - Vol. 3 Issue 7 Sanjuán Martínez, Óscar The research works presented in this issue are based on various topics of interest, among which are included: SVM and ANN Based Classification, Security in Android, Semantic Data, Planning and Software Agents, Mission Planning, Clustering in Text Mining, Mobile Networks, Weather Radars, Human Activity Recognition, LIF Neurons and DDF, Theft Prevention, Constraint Programming, Measuring Meditations Effects, Neural Networks and Deep Learning. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-06-03T12:25:15Z No. of bitstreams: 0; Made available in DSpace on 2021-06-03T12:25:15Z (GMT). No. of bitstreams: 0

Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning

Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning Machado Fernández, José Raúl; Bacallao Vidal, Jesús de la Concepción The discrimination of the clutter interfering signal is a current problem in modern radars’ design, especially in coastal or offshore environments where the histogram of the background signal often displays heavy tails. The statistical characterization of this signal is very important for the cancellation of sea clutter, whose behavior obeys a K distribution according to the commonly accepted criterion. By using neural networks, the authors propose a new method for estimating the K shape parameter, demonstrating its superiority over the classic alternative based on the Method of Moments. Whereas both solutions have a similar performance when the entire range of possible values of the shape parameter is evaluated, the neuronal alternative achieves a much more accurate estimation for the lower Fig.s of the parameter. This is exactly the desired behavior because the best estimate occurs for the most aggressive states of sea clutter. The final design, reached by processing three different sets of computer generated K samples, used a total of nine neural networks whose contribution is synthesized in the final estimate, thus the solution can be interpreted as a deep learning approximation. The results are to be applied in the improvement of radar detectors, particularly for maintaining the operational false alarm probability close to the one conceived in the design. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T14:41:13Z No. of bitstreams: 1 ijimai20163_7_14_pdf_14393.pdf: 7630656 bytes, checksum: 884a2afc180871cd0d8014c23667008e (MD5); Made available in DSpace on 2021-04-21T14:41:13Z (GMT). No. of bitstreams: 1 ijimai20163_7_14_pdf_14393.pdf: 7630656 bytes, checksum: 884a2afc180871cd0d8014c23667008e (MD5)

Chinese-chi and Kundalini yoga Meditations Effects on the Autonomic Nervous System: Comparative Study

Chinese-chi and Kundalini yoga Meditations Effects on the Autonomic Nervous System: Comparative Study Dey, Anilesh; Bhattacharya, D. K.; Tibarewala, D.N.; Dey, Nilanjan; Ashour, Amira S.; Le, Dac-Nhuong; Gospodinova, Evgeniya; Gospodinov, Mitko Cardiac disease is one of the major causes for death all over the world. Heart rate variability (HRV) is a significant parameter that used in assessing Autonomous Nervous System (ANS) activity. Generally, the 2D Poincare′ plot and 3D Poincaré plot of the HRV signals reflect the effect of different external stimuli on the ANS. Meditation is one of such external stimulus, which has different techniques with different types of effects on the ANS. Chinese Chi-meditation and Kundalini yoga are two different effective meditation techniques. The current work is interested with the analysis of the HRV signals under the effect of these two based on meditation techniques. The 2D and 3D Poincare′ plots are generally plotted by fitting respectively an ellipse/ellipsoid to the dense region of the constructed Poincare′ plot of HRV signals. However, the 2D and 3D Poincaré plots sometimes fail to describe the proper behaviour of the system. Thus in this study, a three-dimensional frequency-delay plot is proposed to properly distinguish these two famous meditation techniques by analyzing their effects on ANS. This proposed 3D frequency-delay plot is applied on HRV signals of eight persons practicing same Chi-meditation and four other persons practising same Kundalini yoga. To substantiate the result for larger sample of data, statistical Student t-test is applied, which shows a satisfactory result in this context. The experimental results established that the Chi-meditation has large impact on the HRV compared to the Kundalini yoga. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T14:36:50Z No. of bitstreams: 1 ijimai20163_7_13_pdf_34001.pdf: 512858 bytes, checksum: 21e0b3b1b8cf23d3f522581ff37366a4 (MD5); Made available in DSpace on 2021-04-21T14:36:50Z (GMT). No. of bitstreams: 1 ijimai20163_7_13_pdf_34001.pdf: 512858 bytes, checksum: 21e0b3b1b8cf23d3f522581ff37366a4 (MD5)

An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems

An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo In the context of Constraint Programming, a portfolio approach exploits the complementary strengths of a portfolio of different constraint solvers. The goal is to predict and run the best solver(s) of the portfolio for solving a new, unseen problem. In this work we reproduce, simulate, and evaluate the performance of different portfolio approaches on extensive benchmarks of Constraint Satisfaction Problems. Empirical results clearly show the benefits of portfolio solvers in terms of both solved instances and solving time. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T14:27:00Z No. of bitstreams: 1 ijimai20163_7_12_pdf_13932.pdf: 959307 bytes, checksum: ecf386323b85b967802feb9f5e188d0b (MD5); Made available in DSpace on 2021-04-21T14:27:00Z (GMT). No. of bitstreams: 1 ijimai20163_7_12_pdf_13932.pdf: 959307 bytes, checksum: ecf386323b85b967802feb9f5e188d0b (MD5)

A Real Time Approach to Theft Prevention in the field of Transportation System

A Real Time Approach to Theft Prevention in the field of Transportation System Mehta, Vartika; Punetha, Deepak; Bijalwan, Vishwanath This paper report discusses a theft prevention system, which can prevent the theft and also can be track the object. This system is capable to tracking the vehicle as well as theft prevention. An R.F. module is use to exchange the information regarding vehicle and owner of the vehicle with police control room or SOS services. The vehicle can be track with the help of R.F. receiver. A DTMF based fuel lock has been attached in this system. A cell phone with SIM card has been attached with DTMF IC. The fuel flow in the vehicle can be controlled by give a call to this cell phone. This system has been controlled by a microcontroller which can make the system cost effective, low power consumption, effective and reliable. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T14:22:39Z No. of bitstreams: 1 ijimai20163_7_11_pdf_18075.pdf: 802544 bytes, checksum: 8b627fa29f0458fd51cf708e90650d7a (MD5); Made available in DSpace on 2021-04-21T14:22:39Z (GMT). No. of bitstreams: 1 ijimai20163_7_11_pdf_18075.pdf: 802544 bytes, checksum: 8b627fa29f0458fd51cf708e90650d7a (MD5)

Spiking Activity of a LIF Neuron in Distributed Delay Framework

Spiking Activity of a LIF Neuron in Distributed Delay Framework Kumar Choudhary, Saket; Singh, Karan; Kumar Solanki, Vijender Evolution of membrane potential and spiking activity for a single leaky integrate-and-fire (LIF) neuron in distributed delay framework (DDF) is investigated. DDF provides a mechanism to incorporate memory element in terms of delay (kernel) function into a single neuron models. This investigation includes LIF neuron model with two different kinds of delay kernel functions, namely, gamma distributed delay kernel function and hypo-exponential distributed delay kernel function. Evolution of membrane potential for considered models is studied in terms of stationary state probability distribution (SPD). Stationary state probability distribution of membrane potential (SPDV) for considered neuron models are found asymptotically similar which is Gaussian distributed. In order to investigate the effect of membrane potential delay, rate code scheme for neuronal information processing is applied. Firing rate and Fano-factor for considered neuron models are calculated and standard LIF model is used for comparative study. It is noticed that distributed delay increases the spiking activity of a neuron. Increase in spiking activity of neuron in DDF is larger for hypo-exponential distributed delay function than gamma distributed delay function. Moreover, in case of hypo-exponential delay function, a LIF neuron generates spikes with Fano-factor less than 1. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T14:16:57Z No. of bitstreams: 1 ijimai20163_7_10_pdf_21051.pdf: 1315867 bytes, checksum: fb6aaf06642d190b25dfd2c65ed94ea3 (MD5); Made available in DSpace on 2021-04-21T14:16:57Z (GMT). No. of bitstreams: 1 ijimai20163_7_10_pdf_21051.pdf: 1315867 bytes, checksum: fb6aaf06642d190b25dfd2c65ed94ea3 (MD5)

Human Activity Recognition in Real-Times Environments using Skeleton Joints

Human Activity Recognition in Real-Times Environments using Skeleton Joints Kumar, Ajay; Kumar, Anil; Kumar Singh, Satish; Kala, Rahul In this research work, we proposed a most effective noble approach for Human activity recognition in real-time environments. We recognize several distinct dynamic human activity actions using kinect. A 3D skeleton data is processed from real-time video gesture to sequence of frames and getter skeleton joints (Energy Joints, orientation, rotations of joint angles) from selected setof frames. We are using joint angle and orientations, rotations information from Kinect therefore less computation required. However, after extracting the set of frames we implemented several classification techniques Principal Component Analysis (PCA) with several distance based classifiers and Artificial Neural Network (ANN) respectively with some variants for classify our all different gesture models. However, we conclude that use very less number of frame (10-15%) for train our system efficiently from the entire set of gesture frames. Moreover, after successfully completion of our classification methods we clinch an excellent overall accuracy 94%, 96% and 98% respectively. We finally observe that our proposed system is more useful than comparing to other existing system, therefore our model is best suitable for real-time application such as in video games for player action/gesture recognition. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T14:09:54Z No. of bitstreams: 1 ijimai20163_7_9_pdf_20470.pdf: 1379754 bytes, checksum: ab6eac94c29a79337157250a26aaac90 (MD5); Made available in DSpace on 2021-04-21T14:09:54Z (GMT). No. of bitstreams: 1 ijimai20163_7_9_pdf_20470.pdf: 1379754 bytes, checksum: ab6eac94c29a79337157250a26aaac90 (MD5)

Uncertainty Model For Quantitative Precipitation Estimation Using Weather Radars

Uncertainty Model For Quantitative Precipitation Estimation Using Weather Radars Gómez Vargas, A. Ernesto; Obregón N., Nelson; Álvarez Pomar, C. Lindsay This paper introduces an uncertainty model for the quantitatively estimate precipitation using weather radars. The model considers various key aspects associated to radar calibration, attenuation, and the tradeoff between accuracy and radar coverage. An S-band-radar case study is presented to illustrate particular fractional-uncertainty calculations obtained to adjust various typical radar-calibration elements such as antenna, transmitter, receiver, and some other general elements included in the radar equation. This paper is based in “Guide to the expression of Uncertainty in measurement” [1] and the results show that the fractional uncertainty calculated by the model was 40 % for the reflectivity and 30% for the precipitation using the Marshall Palmer Z-R relationship. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T14:03:15Z No. of bitstreams: 1 ijimai20163_7_8_pdf_38293.pdf: 9330025 bytes, checksum: a37e62275a5154b9111a93edf0936a4f (MD5); Made available in DSpace on 2021-04-21T14:03:15Z (GMT). No. of bitstreams: 1 ijimai20163_7_8_pdf_38293.pdf: 9330025 bytes, checksum: a37e62275a5154b9111a93edf0936a4f (MD5)

Design Methodology for Self-organized Mobile Networks Based

Design Methodology for Self-organized Mobile Networks Based Anzola, John; Bolaños Castro, Sandro Javier; Tarazona Bermúdez, Giovanny Mauricio The methodology proposed in this article enables a systematic design of routing algorithms based on schemes of biclustering, which allows you to respond with timely techniques, clustering heuristics proposed by a researcher, and a focused approach to routing in the choice of clusterhead nodes. This process uses heuristics aimed at improving the different costs in communication surface groups called biclusters. This methodology globally enables a variety of techniques and heuristics of clustering that have been addressed in routing algorithms, but we have not explored all possible alternatives and their different assessments. Therefore, the methodology oriented design research of routing algorithms based on biclustering schemes will allow new concepts of evolutionary routing along with the ability to adapt the topological changes that occur in self-organized data networks. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T13:55:35Z No. of bitstreams: 1 ijimai20163_7_7_pdf_10406.pdf: 1445296 bytes, checksum: d04da4743687f67d9c83712bd03bcf69 (MD5); Made available in DSpace on 2021-04-21T13:55:35Z (GMT). No. of bitstreams: 1 ijimai20163_7_7_pdf_10406.pdf: 1445296 bytes, checksum: d04da4743687f67d9c83712bd03bcf69 (MD5)

Comparative Study of Clustering Algorithms in Text Mining Context

Comparative Study of Clustering Algorithms in Text Mining Context JALIL, Abdennour Mohamed; HAFIDI, Imad; ALAMI, Lamiae; ENSA, Khouribga The spectacular increasing of Data is due to the appearance of networks and smartphones. Amount 42% of world population using internet [1]; have created a problem related of the processing of the data exchanged, which is rising exponentially and that should be automatically treated. This paper presents a classical process of knowledge discovery databases, in order to treat textual data. This process is divided into three parts: preprocessing, processing and postprocessing. In the processing step, we present a comparative study between several clustering algorithms such as KMeans, Global KMeans, Fast Global KMeans, Two Level KMeans and FWKmeans. The comparison between these algorithms is made on real textual data from the web using RSS feeds. Experimental results identified two problems: the first one quality results which remain for algorithms, which rapidly converge. The second problem is due to the execution time that needs to decrease for some algorithms. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T13:50:44Z No. of bitstreams: 1 ijimai20163_7_6_pdf_27159.pdf: 2390043 bytes, checksum: 8617cf316deab2187e36bf96ca08d1b7 (MD5); Made available in DSpace on 2021-04-21T13:50:44Z (GMT). No. of bitstreams: 1 ijimai20163_7_6_pdf_27159.pdf: 2390043 bytes, checksum: 8617cf316deab2187e36bf96ca08d1b7 (MD5)

Linear Temporal Logic-based Mission Planning

Linear Temporal Logic-based Mission Planning Kumar, Anil; Kala, Rahul In this paper, we describe the Linear Temporal Logic-based reactive motion planning. We address the problem of motion planning for mobile robots, wherein the goal specification of planning is given in complex environments. The desired task specification may consist of complex behaviors of the robot, including specifications for environment constraints, need of task optimality, obstacle avoidance, rescue specifications, surveillance specifications, safety specifications, etc. We use Linear Temporal Logic to give a representation for such complex task specification and constraints. The specifications are used by a verification engine to judge the feasibility and suitability of plans. The planner gives a motion strategy as output. Finally a controller is used to generate the desired trajectory to achieve such a goal. The approach is tested using simulations on the LTLMoP mission planning tool, operating over the Robot Operating System. Simulation results generated using high level planners and low level controllers work simultaneously for mission planning and controlling the physical behavior of the robot. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T13:42:09Z No. of bitstreams: 1 ijimai20163_7_5_pdf_16569.pdf: 9919883 bytes, checksum: 52531a5b73c0e5d02a08a6101e72c8b5 (MD5); Made available in DSpace on 2021-04-21T13:42:09Z (GMT). No. of bitstreams: 1 ijimai20163_7_5_pdf_16569.pdf: 9919883 bytes, checksum: 52531a5b73c0e5d02a08a6101e72c8b5 (MD5)

The Silver Lining Methodology

The Silver Lining Methodology Castillo Chamorro, José Miguel The way in which Strategic planning is designed is different depending on the organization. For that reason, no standard procedures can be given to develop Strategic planning. However, the scenarios analysis method is used in any field or organization. We could define a scenario as a set of variables or events that describes a future situation. Additionally, the continuous irruption of new technologies invites us to carry out a revision of the old methodologies and procedures with the intention of starting an innovation process to make them more efficient. The challenge presented in this article consists of the use of the agents technology within a new methodological approach to envision future possible scenarios more quickly and more accurately than the classical methods we currently use. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T12:01:47Z No. of bitstreams: 1 ijimai20163_7_4_pdf_36802.pdf: 822662 bytes, checksum: 72681da991ac08f9ec1edeb702004029 (MD5); Made available in DSpace on 2021-04-21T12:01:47Z (GMT). No. of bitstreams: 1 ijimai20163_7_4_pdf_36802.pdf: 822662 bytes, checksum: 72681da991ac08f9ec1edeb702004029 (MD5)

Query Migration from Object Oriented World to Semantic World

Query Migration from Object Oriented World to Semantic World SOUSSI, Nassima; BAHAJ, Mohamed — In the last decades, object-oriented approach was able to take a large share of databases market aiming to design and implement structured and reusable software through the composition of independent elements in order to have programs with a high performance. On the other hand, the mass of information stored in the web is increasing day after day with a vertiginous speed, exposing the currently web faced with the problem of creating a bridge so as to facilitate access to data between different applications and systems as well as to look for relevant and exact information wished by users. In addition, all existing approach of rewriting object oriented languages to SPARQL language rely on models transformation process to guarantee this mapping. All the previous raisons has prompted us to write this paper in order to bridge an important gap between these two heterogeneous worlds (object oriented and semantic web world) by proposing the first provably semantics preserving OQLto-SPARQL translation algorithm for each element of OQL Query (SELECT clause, FROM clause, FILTER constraint, implicit/ explicit join and union/intersection SELECT queries). Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T11:55:24Z No. of bitstreams: 1 ijimai20163_7_3_pdf_20232.pdf: 479196 bytes, checksum: 690ea97178136a909e868a0bb9aec212 (MD5); Made available in DSpace on 2021-04-21T11:55:24Z (GMT). No. of bitstreams: 1 ijimai20163_7_3_pdf_20232.pdf: 479196 bytes, checksum: 690ea97178136a909e868a0bb9aec212 (MD5)

A New Protection for Android Applications

A New Protection for Android Applications ER-RAJY, Latifa; El kiram, My Ahmed Today, Smartphones are very powerful, and many of its applications use wireless multimedia communications. Prevention from the external dangers (threats) has become a big concern for the experts these days. Android security has become a very important issue because of the free application it provides and the feature which make it very easy for anyone to develop and published it on Play store. Some work has already been done on the android security model, including several analyses of the model and frameworks aimed at enforcing security standards. In this article, we introduce a tool called PermisSecure that is able to perform both static and dynamic analysis on Android programs to automatically detect suspicious applications that request unnecessary or dangerous permissions. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T11:51:03Z No. of bitstreams: 1 ijimai20163_7_2_pdf_15309.pdf: 484497 bytes, checksum: 885e61b8d1cd487fa3f8e64f937f726b (MD5); Made available in DSpace on 2021-04-21T11:51:03Z (GMT). No. of bitstreams: 1 ijimai20163_7_2_pdf_15309.pdf: 484497 bytes, checksum: 885e61b8d1cd487fa3f8e64f937f726b (MD5)

SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique

SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique D. Pujari, Jagadeesh; Yakkundimath, Rajesh; Syedhusain Byadgi, Abdulmunaf Computers have been used for mechanization and automation in different applications of agriculture/horticulture. The critical decision on the agricultural yield and plant protection is done with the development of expert system (decision support system) using computer vision techniques. One of the areas considered in the present work is the processing of images of plant diseases affecting agriculture/horticulture crops. The first symptoms of plant disease have to be correctly detected, identified, and quantified in the initial stages. The color and texture features have been used in order to work with the sample images of plant diseases. Algorithms for extraction of color and texture features have been developed, which are in turn used to train support vector machine (SVM) and artificial neural network (ANN) classifiers. The study has presented a reduced feature set based approach for recognition and classification of images of plant diseases. The results reveal that SVM classifier is more suitable for identification and classification of plant diseases affecting agriculture/horticulture crops. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T11:43:40Z No. of bitstreams: 1 ijimai20163_7_1_pdf_75740.pdf: 1496497 bytes, checksum: ae2e63598f787536f9bb2bdebb0aea7f (MD5); Made available in DSpace on 2021-04-21T11:43:40Z (GMT). No. of bitstreams: 1 ijimai20163_7_1_pdf_75740.pdf: 1496497 bytes, checksum: ae2e63598f787536f9bb2bdebb0aea7f (MD5)

Text Analytics: the convergence of Big Data and Artificial Intelligence

Text Analytics: the convergence of Big Data and Artificial Intelligence Moreno, Antonio; Redondo, Teófilo The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyze millions of emails; you can analyze customers’ comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain’s representation, and many more. Several techniques are currently used and some of them have gained a lot of attention, such as Machine Learning, to show a semisupervised enhancement of systems, but they also present a number of limitations which make them not always the only or the best choice. We conclude with current and near future applications of Text Analytics. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T10:45:28Z No. of bitstreams: 1 ijimai20163_6_9_pdf_38545.pdf: 1031480 bytes, checksum: a726d724412337b545d5f2c20ebee8ae (MD5); Made available in DSpace on 2021-04-21T10:45:28Z (GMT). No. of bitstreams: 1 ijimai20163_6_9_pdf_38545.pdf: 1031480 bytes, checksum: a726d724412337b545d5f2c20ebee8ae (MD5)

Editor’s Note

Editor’s Note Mochón, Francisco; Gonzálvez, Juan Carlos Digital information has redefined the way in which both public and private organizations are faced with the use of data to improve decision making. The importance of Big Data lies in the huge amount of data generated every day, especially following the emergence of online social networks (Facebook, Twitter, Google Plus, etc.) and the exponential growth of devices such as smartphones, smartwatches and other wearables, sensor networks, etc. as well as the possibility of taking into account increasingly updated and more varied information for decision making. [1] With proper Big Data analysis we can spot trends, get models from historical data for predicting future events or extract patterns from user behaviour, and thus be able to tailor services to the needs of users in a better way. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-03-31T14:43:26Z No. of bitstreams: 1 ijimai20163_6_0_pdf_37905.pdf: 569978 bytes, checksum: 5bae1c0b5501b8ba6b1e3271bdfd4465 (MD5); Made available in DSpace on 2021-03-31T14:43:27Z (GMT). No. of bitstreams: 1 ijimai20163_6_0_pdf_37905.pdf: 569978 bytes, checksum: 5bae1c0b5501b8ba6b1e3271bdfd4465 (MD5)

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study Baldominos, Alejandro; Albacete, Esperanza; Marrero, Ignacio; Saez, Yago This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user’s actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS) and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-03-31T14:37:10Z No. of bitstreams: 1 ijimai20163_6_7_pdf_23096.pdf: 1621083 bytes, checksum: c62e8b559c291576e59538de895bbec3 (MD5); Made available in DSpace on 2021-03-31T14:37:10Z (GMT). No. of bitstreams: 1 ijimai20163_6_7_pdf_23096.pdf: 1621083 bytes, checksum: c62e8b559c291576e59538de895bbec3 (MD5)

Using GDELT Data to Evaluate the Confidence on the Spanish Government Energy Policy

Using GDELT Data to Evaluate the Confidence on the Spanish Government Energy Policy Bodas-Sagi, Diego; Labeaga, José The growing demand for affordable, reliable, domestically sourced, and low-carbon electricity is a matter of concern and it is driven by several causes including public policy priorities. Policy objectives and new technologies are changing wholesale market design. The analysis of different aspects of energy markets is increasingly on the agendas of academics, firms’ managers or policy makers. Some concerns are global and are related to the evolution of climate change phenomena. Others are regional or national and they strongly appear in countries like Spain with a high dependence on foreign energy sources and high potential of domestic renewable energy sources. We can find a relevant case in Spanish solar energy policy. A series of regulatory reforms since 2010 reduce revenues to existing renewable power generators and they end up the previous system of support to new renewable generation. This policy change has altered the composition of the energy market affecting investment decisions. In this paper, we analyze the public opinion about energy policy of the Spanish Government using the Global Database of Events, Language, and Tone (GDELT). The GDELT Project consists of over a quarter-billion event records in over 300 categories covering the entire world from 1979 to present, along with a massive network diagram connecting every person, organization, location, and theme to this event database. Our aim is to build sentiment indicators arising from this source of information and, in a final step, evaluate if positive and negative indexes have any effect on the evolution of key market variables as prices and demand. Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2021-02-03T12:42:50Z No. of bitstreams: 1 ijimai20163_6_6_pdf_24130.pdf: 1789873 bytes, checksum: f3947cd24c47b2e15d088763bcba1d83 (MD5); Made available in DSpace on 2021-02-03T12:42:50Z (GMT). No. of bitstreams: 1 ijimai20163_6_6_pdf_24130.pdf: 1789873 bytes, checksum: f3947cd24c47b2e15d088763bcba1d83 (MD5)

Social Network Analysis and Big Data tools applied to the Systemic Risk supervision

Social Network Analysis and Big Data tools applied to the Systemic Risk supervision Mochón, Mari-Carmen After the financial crisis initiated in 2008, international market supervisors of the G20 agreed to reinforce their systemic risk supervisory duties. For this purpose, several regulatory reporting obligations were imposed to the market participants. As a consequence, millions of trade details are now available to National Competent Authorities on a daily basis. Traditional monitoring tools may not be capable of analyzing such volumes of data and extracting the relevant information, in order to identify the potential risks hidden behind the market. Big Data solutions currently applied to the Social Network Analysis (SNA), can be successfully applied the systemic risk supervision. This case of study proposes how relations established between the financial market participants could be analyzed, in order to identify risk of propagation and market behavior, without the necessity of expensive and demanding technical architectures. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-24T11:35:11Z No. of bitstreams: 1 ijimai20163_6_5_pdf_14449.pdf: 1567937 bytes, checksum: 8c19f6197f0288b94ff5642b4560f14a (MD5); Made available in DSpace on 2020-06-24T11:35:11Z (GMT). No. of bitstreams: 1 ijimai20163_6_5_pdf_14449.pdf: 1567937 bytes, checksum: 8c19f6197f0288b94ff5642b4560f14a (MD5)

Big Data & eLearning: A Binomial to the Future of the Knowledge Society

Big Data & eLearning: A Binomial to the Future of the Knowledge Society Alonso Secades, Vidal; Arranz, Olga There is no doubt that in what refers to the educational area, technology is producing a series of changes that will greatly affect our near future. The increase of students experiences in the new educational systems in distance learning makes possible to have information related to the students ‘activities and how these can be dealt with automatic procedures. The implementation of these analytical methods is possible through the use of powerful new technologies such as Data Mining or Big Data. Relevant information is obtained of the use made by the students of the technological tools in a Learning Management System, thus, allowing us to infer a pattern of behavior of the students, to be used in the future. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-24T10:53:22Z No. of bitstreams: 1 ijimai20163_6_4_pdf_19453.pdf: 737597 bytes, checksum: ebc22f4becd2d764e142448d322e04cd (MD5); Made available in DSpace on 2020-06-24T10:53:22Z (GMT). No. of bitstreams: 1 ijimai20163_6_4_pdf_19453.pdf: 737597 bytes, checksum: ebc22f4becd2d764e142448d322e04cd (MD5)

A Fine Grain Sentiment Analysis with Semantics in Tweets

A Fine Grain Sentiment Analysis with Semantics in Tweets Navas-Delgado, Ismael; Aldana-Montes, Jose F.; Barba Gonzalez, Cristobal; García-Nieto, José Social networking is nowadays a major source of new information in the world. Microblogging sites like Twitter have millions of active users (320 million active users on Twitter on the 30th September 2015) who share their opinions in real time, generating huge amounts of data. These data are, in most cases, available to any network user. The opinions of Twitter users have become something that companies and other organisations study to see whether or not their users like the products or services they offer. One way to assess opinions on Twitter is classifying the sentiment of the tweets as positive or negative. However, this process is usually done at a coarse grain level and the tweets are classified as positive or negative. However, tweets can be partially positive and negative at the same time, referring to different entities. As a result, general approaches usually classify these tweets as “neutral”. In this paper, we propose a semantic analysis of tweets, using Natural Language Processing to classify the sentiment with regards to the entities mentioned in each tweet. We offer a combination of Big Data tools (under the Apache Hadoop framework) and sentiment analysis using RDF graphs supporting the study of the tweet’s lexicon. This work has been empirically validated using a sporting event, the 2014 Phillips 66 Big 12 Men’s Basketball Championship. The experimental results show a clear correlation between the predicted sentiments with specific events during the championship. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-24T09:27:02Z No. of bitstreams: 1 ijimai20163_6_3_pdf_14247.pdf: 1229376 bytes, checksum: eb1c79f224aab1acf6f5b1290b088b82 (MD5); Made available in DSpace on 2020-06-24T09:27:02Z (GMT). No. of bitstreams: 1 ijimai20163_6_3_pdf_14247.pdf: 1229376 bytes, checksum: eb1c79f224aab1acf6f5b1290b088b82 (MD5)

PInCom project: SaaS Big Data Platform for and Communication Channels

PInCom project: SaaS Big Data Platform for and Communication Channels Lombardo, Juan Manuel; López, Miguel Ángel; Mirón, Felipe; Velasco, Susana; Sevilla, Juan Pablo; Mellado, Juan The problem of optimization will be addressed in this article, based on the premise that the successful implementation of Big Data solutions requires as a determining factor not only effective -it is assumed- but the efficiency of the responsiveness of management information get the best value offered by the digital and technological environment for gaining knowledge. In adopting Big Data strategies should be identified storage technologies and appropriate extraction to enable professionals and companies from different sectors to realize the full potential of the data. A success story is the solution PInCom: Intelligent-Communications Platform that aims customer loyalty by sending multimedia communications across heterogeneous transmission channels. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-24T08:30:00Z No. of bitstreams: 1 ijimai20163_6_2_pdf_29854.pdf: 1482915 bytes, checksum: 49b9ea3359f9781c15f6227cbea8c602 (MD5); Made available in DSpace on 2020-06-24T08:30:00Z (GMT). No. of bitstreams: 1 ijimai20163_6_2_pdf_29854.pdf: 1482915 bytes, checksum: 49b9ea3359f9781c15f6227cbea8c602 (MD5)

Operating an Advertising Programmatic Buying Platform: A Case Study

Operating an Advertising Programmatic Buying Platform: A Case Study Mochón, Francisco; Gonzalvez-Cabañas, J.C. This paper analyses how new technological developments and the possibilities generated by the internet are shaping the online advertising market. More specifically it focuses on a programmatic advertising case study. The origin of the problem is how publishers resort to automated buying and selling when trying to shift unsold inventory. To carry out our case study, we will use a programmatic online advertising sales platform, which identifies the optimal way of promoting a given product. The platform executes, evaluates, manages and optimizes display advertising campaigns, all in real-time. The empirical analysis carried out in the case study reveals that the platform and its exclusion algorithms are suitable mechanisms for analysing the performance and efficiency of the various segments that might be used to promote products. Thanks to Big Data tools and artificial intelligence the platform performs automatically, providing information in a user-friendly and simple manner. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-22T08:13:09Z No. of bitstreams: 1 ijimai20163_6_1_pdf_29476.pdf: 1470590 bytes, checksum: 1df7f252b978d019f53ae215d923dda2 (MD5); Made available in DSpace on 2020-06-22T08:13:09Z (GMT). No. of bitstreams: 1 ijimai20163_6_1_pdf_29476.pdf: 1470590 bytes, checksum: 1df7f252b978d019f53ae215d923dda2 (MD5)

Artificial Intelligence Applied to Project Success: A Literature Review

Artificial Intelligence Applied to Project Success: A Literature Review Fernández Rodríguez, Juan Carlos; Magaña Martínez, Daniel Project control and monitoring tools are based on expert judgement and parametric tools. Projects are the means by which companies implement their strategies. However project success rates are still very low. This is a worrying situation that has a great economic impact so alternative tools for project success prediction must be proposed in order to estimate project success or identify critical factors of success. Some of these tools are based on Artificial Intelligence. In this paper we will carry out a literature review of those papers that use Artificial Intelligence as a tool for project success estimation or critical success factor identification. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-16T08:17:03Z No. of bitstreams: 1 ijimai20153_5_10_pdf_21674.pdf: 706271 bytes, checksum: d741a43b18b7a104e8ecee4db2b77751 (MD5); Made available in DSpace on 2020-06-16T08:17:03Z (GMT). No. of bitstreams: 1 ijimai20153_5_10_pdf_21674.pdf: 706271 bytes, checksum: d741a43b18b7a104e8ecee4db2b77751 (MD5)

A Network Based Methodology to Reveal Patterns in Knowledge Transfer

A Network Based Methodology to Reveal Patterns in Knowledge Transfer López-Cruz, Orlando; Obregón N., Nelson This paper motivates, presents and demonstrates in use a methodology based in complex network analysis to support research aimed at identification of sources in the process of knowledge transfer at the interorganizational level. The importance of this methodology is that it states a unified model to reveal knowledge sharing patterns and to compare results from multiple researches on data from different periods of time and different sectors of the economy. This methodology does not address the underlying statistical processes. To do this, national statistics departments (NSD) provide documents and tools at their websites. But this proposal provides a guide to model information inferences gathered from data processing revealing links between sources and recipients of knowledge being transferred and that the recipient detects as main source to new knowledge creation. Some national statistics departments set as objective for these surveys the characterization of innovation dynamics in firms and to analyze the use of public support instruments. From this characterization scholars conduct different researches. Measures of dimensions of the network composed by manufacturing firms and other organizations conform the base to inquiry the structure that emerges from taking ideas from other organizations to incept innovations. These two sets of data are actors of a two- mode-network. The link between two actors (network nodes, one acting as the source of the idea. The second one acting as the destination) comes from organizations or events organized by organizations that “provide” ideas to other group of firms. The resulting demonstrated design satisfies the objective of being a methodological model to identify sources in knowledge transfer of knowledge effectively used in innovation. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-16T07:33:47Z No. of bitstreams: 1 ijimai20153_5_9_pdf_10691.pdf: 1076853 bytes, checksum: ac7cf72bbd9aceb20ba67bc06fcf7363 (MD5); Made available in DSpace on 2020-06-16T07:33:47Z (GMT). No. of bitstreams: 1 ijimai20153_5_9_pdf_10691.pdf: 1076853 bytes, checksum: ac7cf72bbd9aceb20ba67bc06fcf7363 (MD5)

Agile Values and Their Implementation in Practice

Agile Values and Their Implementation in Practice Schön, Eva-Maria; Escalona, Maria J.; Thomaschewski, Jörg Today agile approaches are often used for the development of digital products. Since their development in the 90s, Agile Methodologies, such as Scrum and Extreme Programming, have evolved. Team collaboration is strongly influenced by the values and principles of the Agile Manifesto. The values and principles described in the Agile Manifesto support the optimization of the development process. In this article, the current operation is analyzed in Agile Product Development Processes. Both, the cooperation in the project team and the understanding of the roles and tasks will be analyzed. The results are set in relation to the best practices of Agile Methodologies. A quantitative questionnaire related to best practices in Agile Product Development was developed. The study was carried out with 175 interdisciplinary participants from the IT industry. For the evaluation of the results, 93 participants were included who have expertise in the subject area Agile Methodologies. On one hand, it is shown that the collaborative development of product-related ideas brings benefits. On the other hand, it is investigated which effect a good understanding of the product has on decisions made during the implementation. Furthermore, the skillset of product managers, the use of pair programming, and the advantages of cross-functional teams are analyzed. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-16T06:32:11Z No. of bitstreams: 1 ijimai20153_5_8_pdf_59735.pdf: 835867 bytes, checksum: a8cec92c0016d7926ee9600c97c88910 (MD5); Made available in DSpace on 2020-06-16T06:32:11Z (GMT). No. of bitstreams: 1 ijimai20153_5_8_pdf_59735.pdf: 835867 bytes, checksum: a8cec92c0016d7926ee9600c97c88910 (MD5)

Step Characterization using Sensor Information Fusion and Machine Learning

Step Characterization using Sensor Information Fusion and Machine Learning Anacleto, Ricardo; Figueiredo, Lino A pedestrian inertial navigation system is typically used to suppress the Global Navigation Satellite System limitation to track persons in indoor or in dense environments. However, low- cost inertial systems provide huge location estimation errors due to sensors and pedestrian dead reckoning inherent characteristics. To suppress some of these errors we propose a system that uses two inertial measurement units spread in person’s body, which measurements are aggregated using learning algorithms that learn the gait behaviors. In this work we present our results on using different machine learning algorithms which are used to characterize the step according to its direction and length. This characterization is then used to adapt the navigation algorithm according to the performed classifications. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-15T10:34:49Z No. of bitstreams: 1 ijimai20153_5_7_pdf_23101.pdf: 937011 bytes, checksum: 3d715901115edfac965fe80fbdd2d6de (MD5); Made available in DSpace on 2020-06-15T10:34:50Z (GMT). No. of bitstreams: 1 ijimai20153_5_7_pdf_23101.pdf: 937011 bytes, checksum: 3d715901115edfac965fe80fbdd2d6de (MD5)

Building Real-Time Collaborative Applications with a Federated Architecture

Building Real-Time Collaborative Applications with a Federated Architecture Ojanguren-Menendez, Pablo; Tenorio-Fornés, Antonio; Hassan, Samer Real-time collaboration is being offered by multiple libraries and APIs (Google Drive Real-time API, Microsoft Real-Time Communications API, TogetherJS, ShareJS), rapidly becoming a mainstream option for webservices developers. However, they are offered as centralised services running in a single server, regardless if they are free/open source or proprietary software. After re-engineering Apache Wave (former Google Wave), we can now provide the first decentralised and federated free/open source alternative. The new API allows to develop new real-time collaborative web applications in both JavaScript and Java environments. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-15T09:25:34Z No. of bitstreams: 1 ijimai20153_5_6_pdf_99256.pdf: 855113 bytes, checksum: af141b161a59ffe10aa0cd17e72dc976 (MD5); Made available in DSpace on 2020-06-15T09:25:34Z (GMT). No. of bitstreams: 1 ijimai20153_5_6_pdf_99256.pdf: 855113 bytes, checksum: af141b161a59ffe10aa0cd17e72dc976 (MD5)

“Wrapping” X3DOM around Web Audio API

“Wrapping” X3DOM around Web Audio API Stamoulias, Andreas; Lakka, Eftychia; Malamos, Athanasios Spatial sound has a conceptual role in the Web3D environments, due to highly realism scenes that can provide. Lately the efforts are concentrated on the extension of the X3D/ X3DOM through spatial sound attributes. This paper presents a novel method for the introduction of spatial sound components in the X3DOM framework, based on X3D specification and Web Audio API. The proposed method incorporates the introduction of enhanced sound nodes for X3DOM which are derived by the implementation of the X3D standard components, enriched with accessional features of Web Audio API. Moreover, several examples-scenarios developed for the evaluation of our approach. The implemented examples established the achievability of new registered nodes in X3DOM, for spatial sound characteristics in Web3D virtual worlds. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-12T11:20:44Z No. of bitstreams: 1 ijimai20153_5_5_pdf_26535.pdf: 1170250 bytes, checksum: 9133cb879b5b13716ad53f373ce8e69a (MD5); Made available in DSpace on 2020-06-12T11:20:44Z (GMT). No. of bitstreams: 1 ijimai20153_5_5_pdf_26535.pdf: 1170250 bytes, checksum: 9133cb879b5b13716ad53f373ce8e69a (MD5)

Checking RTECTL properties of STSs via SMT-based Bounded Model Checking

Checking RTECTL properties of STSs via SMT-based Bounded Model Checking Zbrzezny, Agnieszka; Zbrzezny, Andrzej We present an SMT-based bounded model checking (BMC) method for Simply-Timed Systems (STSs) and for the existential fragment of the Real-time Computation Tree Logic. We implemented the SMT-based BMC algorithm and compared it with the SAT-based BMC method for the same systems and the same property language on several benchmarks for STSs. For the SAT- based BMC we used the PicoSAT solver and for the SMT-based BMC we used the Z3 solver. The experimental results show that the SMT-based BMC performs quite well and is, in fact, sometimes significantly faster than the tested SAT-based BMC. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-12T10:51:19Z No. of bitstreams: 1 ijimai20153_5_4_pdf_87485.pdf: 2305850 bytes, checksum: a8ecf820c7ea6d56ba1cad6581d09a4c (MD5); Made available in DSpace on 2020-06-12T10:51:19Z (GMT). No. of bitstreams: 1 ijimai20153_5_4_pdf_87485.pdf: 2305850 bytes, checksum: a8ecf820c7ea6d56ba1cad6581d09a4c (MD5)

An Automated Negotiation-based Framework via Multi-Agent System for the Construction Domain

An Automated Negotiation-based Framework via Multi-Agent System for the Construction Domain Mahmoud, Moamin A.; Sharifuddin Ahmad, Mohd; Zaliman Yuso, Mohd; Idrus, Arazi In this paper, we propose an automated multi-agent negotiation framework for decision making in the construction domain. It enables software agents to conduct negotiations and autonomously make decisions. The proposed framework consists of two types of components, internal and external. Internal components are integrated into the agent architecture while the external components are blended within the environment to facilitate the negotiation process. The internal components are negotiation algorithm, negotiation style, negotiation protocol, and solution generators. The external components are the negotiation base and the conflict resolution algorithm. We also discuss the decision making process flow in such system. There are three main processes in decision making for specific projects, which are propose solutions, negotiate solutions and handling conflict outcomes (conflict resolution). We finally present the proposed architecture that enables software agents to conduct automated negotiation in the construction domain. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-12T10:15:39Z No. of bitstreams: 1 ijimai20153_5_3_pdf_28774.pdf: 855986 bytes, checksum: d42d07fa1c1ba8a166d3ca6e26aa5d29 (MD5); Made available in DSpace on 2020-06-12T10:15:39Z (GMT). No. of bitstreams: 1 ijimai20153_5_3_pdf_28774.pdf: 855986 bytes, checksum: d42d07fa1c1ba8a166d3ca6e26aa5d29 (MD5)

A New Approach for Hiding Image Based on the Signature of Coeficients

A New Approach for Hiding Image Based on the Signature of Coeficients Al-asadi, Tawfiq A.; Hadi Ali, Israa; kareem Abdul Abdul Abdul kadhem, kadhem This paper presents a new approach for hiding the secret image inside another image file, depending on the signature of coefficients. The proposed system consists of two general stages. The first one is the hiding stage which consist of the following steps (Read the cover image and message image, Block collections using the chain code and similarity measure, Apply DCT Transform, Signature of coefficients, Hiding algorithm , Save information of block in boundary, Reconstruct block to stego image and checking process). The second stage is extraction stage which consist of the following steps ( read the stego image, Extract information of block from boundary, Block collection, Apply DCT transform, Extract bits of message and save it to buffer, Extracting message). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-12T09:38:27Z No. of bitstreams: 1 ijimai20153_5_2_pdf_10078.pdf: 2070096 bytes, checksum: 62e0646fe614a8ebd62080d54f1852ce (MD5); Made available in DSpace on 2020-06-12T09:38:27Z (GMT). No. of bitstreams: 1 ijimai20153_5_2_pdf_10078.pdf: 2070096 bytes, checksum: 62e0646fe614a8ebd62080d54f1852ce (MD5)

Towards a Standard-based Domain-specific Platform to Solve Machine Learning-based Problems

Towards a Standard-based Domain-specific Platform to Solve Machine Learning-based Problems Cueva-Lovelle, Juan Manuel; García-Díaz, Vicente; Pelayo García-Bustelo, B. Cristina; Pascual-Espada, Jordán Machine learning is one of the most important subfields of computer science and can be used to solve a variety of interesting artificial intelligence problems. There are different languages, framework and tools to define the data needed to solve machine learning-based problems. However, there is a great number of very diverse alternatives which makes it difficult the intercommunication, portability and re-usability of the definitions, designs or algorithms that any developer may create. In this paper, we take the first step towards a language and a development environment independent of the underlying technologies, allowing developers to design solutions to solve machine learning-based problems in a simple and fast way, automatically generating code for other technologies. That can be considered a transparent bridge among current technologies. We rely on Model-Driven Engineering approach, focusing on the creation of models to abstract the definition of artifacts from the underlying technologies. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-12T09:23:03Z No. of bitstreams: 1 ijimai20153_5_1_pdf_15294.pdf: 816057 bytes, checksum: 4c2443e753a6fcd4309d08c195478608 (MD5); Made available in DSpace on 2020-06-12T09:23:03Z (GMT). No. of bitstreams: 1 ijimai20153_5_1_pdf_15294.pdf: 816057 bytes, checksum: 4c2443e753a6fcd4309d08c195478608 (MD5)

IJIMAI Editor's Note - Vol. 3 Issue 5

IJIMAI Editor's Note - Vol. 3 Issue 5 González-Crespo, Rubén The research works presented in this issue are based on various topics of interest, among which are included: DSL, Machine Learning, Information hiding, Steganography, SMA, RTECTL, SMT-based bounded model checking, STS, Spatial sound, X3D, X3DOM, Web Audio API, Web3D, Real-time, Realistic 3D, 3D Audio, Apache Wave, API, Collaborative, Pedestrian Inertial, Navigation System, Indoor Location, Learning Algorithms, Information Fusion, Agile development, Scrum, Cross Functional Teams, Knowledge Transfer, Technological Innovation, Technology Transfer, Social Networks Analysis, Project Management, Links in Social Networks, Rights of Knowledge Sharing and Web 2.0. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-12T08:56:53Z No. of bitstreams: 1 ijimai20153_5_0_pdf_21755.pdf: 491838 bytes, checksum: cdacceee6f8ac7d390a3fae8f321d0ff (MD5); Made available in DSpace on 2020-06-12T08:56:53Z (GMT). No. of bitstreams: 1 ijimai20153_5_0_pdf_21755.pdf: 491838 bytes, checksum: cdacceee6f8ac7d390a3fae8f321d0ff (MD5)

Ball convergence for Steffensen-type fourth-order methods

Ball convergence for Steffensen-type fourth-order methods Argyros, Ioannis K; George, Santhosh We present a local convergence analysis for a family of Steffensen-type fourth-order methods in order to approximate a solution of a nonlinear equation. We use hypotheses up to the first derivative in contrast to earlier studies such as [1], [5]-[28] using hypotheses up to the fifth derivative. This way the applicability of these methods is extended under weaker hypotheses. Moreover the radius of convergence and computable error bounds on the distances involved are also given in this study. Numerical examples are also presented in this study. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-12T07:59:05Z No. of bitstreams: 1 ijimai20153_4_7_pdf_73265.pdf: 10709146 bytes, checksum: feea520053c5a3973a14648ac472fe5b (MD5); Made available in DSpace on 2020-06-12T07:59:05Z (GMT). No. of bitstreams: 1 ijimai20153_4_7_pdf_73265.pdf: 10709146 bytes, checksum: feea520053c5a3973a14648ac472fe5b (MD5)

Towards a better learning models through OCWs and MOOCs

Towards a better learning models through OCWs and MOOCs Cordero, Alicia; Jordán, Cristina; Sanabria-Codesal, E.; Torregrosa, Juan Ramón echnological advances of XXth century have induced a profound change in society and, therefore, in the high education. Internet supposed a qualitative difference, as information and digital images flooded into homes around the world. The Universitat Politècnica de València (UPV) is a medium sized university of Spain that has been involved in the development of digital video content (Polimedia) to support teaching processes for several years. Joint with Polimedia and other learning objects (virtual laboratories, applets, etc.), the UPV promoted the construction of OCWs. Along with the improvement of technology, MOOCs appeared as e-learning material. In this work, we analyze the advantages and drawbacks of OCWs and MOOCs when they are used in our classroom. This experience has led us to incorporate in our methodology the flip teaching. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-12T07:09:02Z No. of bitstreams: 1 ijimai20153_4_5_pdf_16920.pdf: 2300813 bytes, checksum: f1da2b79c22fc8b8a2778be565e28e9c (MD5); Made available in DSpace on 2020-06-12T07:09:02Z (GMT). No. of bitstreams: 1 ijimai20153_4_5_pdf_16920.pdf: 2300813 bytes, checksum: f1da2b79c22fc8b8a2778be565e28e9c (MD5)

Local Convergence for an Improved Jarratt-type Method in Banach Space

Local Convergence for an Improved Jarratt-type Method in Banach Space Argyros, Ioannis K; González, Daniel We present a local convergence analysis for an improved Jarratt-type methods of order at least five to approximate a solution of a nonlinear equation in a Banach space setting. The convergence ball and error estimates are given using hypotheses up to the first Fréchet derivative in contrast to earlier studies using hypotheses up to the third Fréchet derivative. Numerical examples are also provided in this study, where the older hypotheses are not satisfied to solve equations but the new hypotheses are satisfied. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-10T10:15:33Z No. of bitstreams: 1 ijimai20153_4_4_pdf_25787.pdf: 3087468 bytes, checksum: 038e2ee8f72a348a09b9d233178f4fe1 (MD5); Made available in DSpace on 2020-06-10T10:15:33Z (GMT). No. of bitstreams: 1 ijimai20153_4_4_pdf_25787.pdf: 3087468 bytes, checksum: 038e2ee8f72a348a09b9d233178f4fe1 (MD5)

Unifying the Classical Approach with New Technologies: An Innovative Proposal for Teaching Mathematics in Engineering

Unifying the Classical Approach with New Technologies: An Innovative Proposal for Teaching Mathematics in Engineering Amat, Sergio; Busquier, Sonia; Legaz, Mª José; Ruiz, Juan The aim of this paper is to present a teaching experience developed in the Polytechnic University of Cartagena and, more specifically, in the subject of Mathematical Methods Applied to Civil Engineering, that belongs to the Master Degree of Paths, Channels and Ports. Our classes were a mix between the traditional system and the new educational system. Moreover, we tried to adapt the evaluation process to the new European Framework for Higher Education. We have used videos developed by us and by students in our classes. We have noticed that the interest and motivation in class has grown. Also the grades have improved. We did a survey during this academic year and the results were strongly positive for both students and teachers. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-10T08:42:38Z No. of bitstreams: 1 ijimai20153_4_3_pdf_19755.pdf: 1333099 bytes, checksum: 5913c7a2618354a3379cd9f6e92c9f9d (MD5); Made available in DSpace on 2020-06-10T08:42:38Z (GMT). No. of bitstreams: 1 ijimai20153_4_3_pdf_19755.pdf: 1333099 bytes, checksum: 5913c7a2618354a3379cd9f6e92c9f9d (MD5)

IJIMAI Editor's Note - Vol. 3 Issue 4

IJIMAI Editor's Note - Vol. 3 Issue 4 Magreñán, Á. Alberto This special issue, Teaching Mathematics using new and classic tools, concentrates on the practical and experimental teaching in advanced Mathematics in Engineering. The selected papers deal with the most relevant issues in the field, such as Mathematical learning and other different subjects in which Mathematics are needed, Advanced Mathematics, the development of different studied using Masive Open Online courser (MOOCs) or even the history of E-Learning and Mathematics. The result is a collection of experimental validations, mathematical papers and MOOCs studies which constitutes a clear contribution to the state of the art. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-10T07:56:50Z No. of bitstreams: 1 ijimai20153_4_0_pdf_73085.pdf: 545523 bytes, checksum: d3e287f9a4c9556424e60c13b04af8e8 (MD5); Made available in DSpace on 2020-06-10T07:56:50Z (GMT). No. of bitstreams: 1 ijimai20153_4_0_pdf_73085.pdf: 545523 bytes, checksum: d3e287f9a4c9556424e60c13b04af8e8 (MD5)

An Architecture Approach for 3D Render Distribution using Mobile Devices in Real Time

An Architecture Approach for 3D Render Distribution using Mobile Devices in Real Time Bolívar, Holman; Velandia, John Alexande; Torres, Jenny Natalia; Giménez de Ory, Elena Nowadays, video games such as Massively Multiplayer Online Game (MMOG) have become cultural mediators. Mobile games contribute to a large number of downloads and potential benefits in the applications market. Although processing power of mobile devices increases the bandwidth transmission, a poor network connectivity may bottleneck Gaming as a Service (GaaS). In order to enhance performance in digital ecosystem, processing tasks are distributed among thin client devices and robust servers. This research is based on the method ‘divide and rule’, that is, volumetric surfaces are subdivided using a tree-KD of sequence of scenes in a game, so reducing the surface into small sets of points. Reconstruction efficiency is improved, because the search of data is performed in local and small regions. Processes are modeled through a finite set of states that are built using Hidden Markov Models with domains configured by heuristics. Six test that control the states of each heuristic, including the number of intervals are carried out to validate the proposed model. This validation concludes that the proposed model optimizes response frames per second, in a sequence of interactions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-08T12:01:23Z No. of bitstreams: 1 ijimai20153_3_7_pdf_27908.pdf: 1211301 bytes, checksum: c0f674f00e028f9905dda6ba2f18079a (MD5); Made available in DSpace on 2020-06-08T12:01:23Z (GMT). No. of bitstreams: 1 ijimai20153_3_7_pdf_27908.pdf: 1211301 bytes, checksum: c0f674f00e028f9905dda6ba2f18079a (MD5)

The Use of Genetic Algorithms in UV Disinfection of Drinking Water

The Use of Genetic Algorithms in UV Disinfection of Drinking Water Zaldaña, Hugo R.; Castañeda, Emerson In order to have drinking water, some countries have to use chlorine. It is use cause is effective and it’s cheap. An alternative to this process is the UV disinfection of drinking water. Most of the devices in the market use UV bulbs or mercury lamps. The UV LED, which is cheaper and smaller, allows creating new smaller devices. The main contribution of this paper is the use of Genetic Algorithms to help design a drinking water device with UV LEDs. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-08T10:00:24Z No. of bitstreams: 1 ijimai20153_3_5_pdf_13955.pdf: 761367 bytes, checksum: e0ff2e4f15f21eab5a01b3b5af704888 (MD5); Made available in DSpace on 2020-06-08T10:00:24Z (GMT). No. of bitstreams: 1 ijimai20153_3_5_pdf_13955.pdf: 761367 bytes, checksum: e0ff2e4f15f21eab5a01b3b5af704888 (MD5)

OpinAIS: An Artificial Immune System-based Framework for Opinion Mining

OpinAIS: An Artificial Immune System-based Framework for Opinion Mining Baldominos, Alejandro; Luis Mingueza, Nerea; García del Pozo, Mª Cristina This paper proposes the design of an evolutionary algorithm for building classifiers specifically aimed towards performing classification and sentiment analysis over texts. Moreover, it has properties taken from Artificial Immune Systems, as it tries to resemble biological systems since they are able to discriminate harmful from innocuous bodies (in this case, the analogy could be established with negative and positive texts respectively). A framework, namely OpinAIS, is developed around the evolutionary algorithm, which makes it possible to distribute it as an open-source tool, which enables the scientific community both to extend it and improve it. The framework is evaluated with two different public datasets, the first involving voting records for the US Congress and the second consisting in a Twitter corpus with tweets about different technology brands, which can be polarized either towards positive or negative feelings; comparing the results with alternative machine learning techniques and concluding with encouraging results. Additionally, as the framework is publicly available for download, researchers can replicate the experiments from this paper or propose new ones. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-08T07:52:04Z No. of bitstreams: 1 ijimai20153_3_3_pdf_25958.pdf: 736708 bytes, checksum: dd6aa7fabf8fe7072ca349ba0352ebcc (MD5); Made available in DSpace on 2020-06-08T07:52:04Z (GMT). No. of bitstreams: 1 ijimai20153_3_3_pdf_25958.pdf: 736708 bytes, checksum: dd6aa7fabf8fe7072ca349ba0352ebcc (MD5)

Using Local Grammar for Entity Extraction from Clinical Reports

Using Local Grammar for Entity Extraction from Clinical Reports Ghoulam, Aicha; Barigou, Fatiha; Belalem, Ghalem; Meziane, Farid Information Extraction (IE) is a natural language processing (NLP) task whose aim is to analyze texts written in natural language to extract structured and useful information such as named entities and semantic relations linking these entities. Information extraction is an important task for many applications such as bio-medical literature mining, customer care, community websites, and personal information management. The increasing information available in patient clinical reports is difficult to access. As it is often in an unstructured text form, doctors need tools to enable them access to this information and the ability to search it. Hence, a system for extracting this information in a structured form can benefits healthcare professionals. The work presented in this paper uses a local grammar approach to extract medical named entities from French patient clinical reports. Experimental results show that the proposed approach achieved an F-Measure of 90. 06%. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-05T11:34:13Z No. of bitstreams: 1 ijimai20153_3_2_pdf_97545.pdf: 1023272 bytes, checksum: 30de4da55f4613acdd2fd10922479c81 (MD5); Made available in DSpace on 2020-06-05T11:34:13Z (GMT). No. of bitstreams: 1 ijimai20153_3_2_pdf_97545.pdf: 1023272 bytes, checksum: 30de4da55f4613acdd2fd10922479c81 (MD5)

A 3D Visual Interface for Critiquing-based Recommenders: Architecture and Interaction

A 3D Visual Interface for Critiquing-based Recommenders: Architecture and Interaction Contreras, David; Salamó, Maria; Rodríguez, Inmaculada; Puig, Anna Nowadays e-commerce websites offer users such a huge amount of products, which far from facilitating the buying process, actually make it more difficult. Hence, recommenders, which learn from users’ preferences, are consolidating as valuable instruments to enhance the buying process in the 2D Web. Indeed, 3D virtual environments are an alternative interface for recommenders. They provide the user with an immersive 3D social experience, enabling a richer visualisation and increasing the interaction possibilities with other users and with the recommender. In this paper, we focus on a novel framework to tightly integrate interactive recommendation systems in a 3D virtual environment. Specifically, we propose to integrate a Collaborative Conversational Recommender (CCR) in a 3D social virtual world. Our CCR Framework defines three layers: the user interaction layer (3D Collaborative Space Client), the communication layer (3D Collaborative Space Server), and the recommendation layer (Collaborative Conversational Recommender). Additionally, we evaluate the framework based on several usability criteria such as learnability, perceived efficiency and effectiveness. Results demonstrate that users positively valued the experience. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-05T10:42:43Z No. of bitstreams: 1 ijimai20153_3_1_pdf_15723.pdf: 841284 bytes, checksum: 407c808f63e2be8f29f55d3758102331 (MD5); Made available in DSpace on 2020-06-05T10:42:43Z (GMT). No. of bitstreams: 1 ijimai20153_3_1_pdf_15723.pdf: 841284 bytes, checksum: 407c808f63e2be8f29f55d3758102331 (MD5)

Recognizing Human Activities Based on Wearable Inertial Measurements - Methods and Applications

Recognizing Human Activities Based on Wearable Inertial Measurements - Methods and Applications Siirtola, Pekka On April 10 of 2015 Pekka Siirtola defended his PhD thesis, called “Recognizing Human Activities Based on Wearable Inertial Measurements - Methods and Applications” [1]. The thesis was supervised by Professor Juha Röning and pre-eximined by Associate Professors Ulf Johansson from University of Borås, Sweden, and Daniel Roggen from University of Sussex, United Kingdom. Pekka Siirtola successfully defended his thesis against his opponent Professor Barbara Hammer from University of Bielefeld, Germany. This publicly open defence was held in Auditorium TS101 at University of Oulu, Finland. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-05-21T11:47:17Z No. of bitstreams: 1 ijimai20153_3_6_pdf_11970.pdf: 268079 bytes, checksum: 0508e6396dda7e74e61f6982cdda853f (MD5); Made available in DSpace on 2020-05-21T11:47:17Z (GMT). No. of bitstreams: 1 ijimai20153_3_6_pdf_11970.pdf: 268079 bytes, checksum: 0508e6396dda7e74e61f6982cdda853f (MD5)

Variation of the Heartbeat and Activity as an Indicator of Drowsiness at the Wheel Using a Smartwatch

Variation of the Heartbeat and Activity as an Indicator of Drowsiness at the Wheel Using a Smartwatch Ríos Aguilar, Sergio; Miguel Merino, José Luis; Millán Sánchez, Andrés; Sánchez Valdivieso, Álvaro Sleepiness is one of the first causal factors of accidents. An estimated 10-30% of road deaths are related to fatigue driving. A large number of research studies have been conducted to reduce the risk of accidents while driving. Many of these studies are based on the detection of biological signals by drowsiness/sleepiness. The activity of the autonomic nervous system (ANS) presented alterations during different physical states such as stress or sleepiness. This activity is measured by ECG (electroencephalogram) and, in different studies, it can be measured with the variation of the heart beat (HRV-Heart Rate Variability) in order to analyze it and then detect drowsiness/sleepiness in drivers. The main advantage is that HRV can be performed using non invasive and uncomfortable means compared to EEG sensors. New Wearables technologies are capable of measuring the heart beat and, further, using other sensors like Accelerometer and Gyroscope, embedded on a simple clock allow us to monitor the physical activity of the user. Our main goal is to use the pulsations measurements in conjunction with the physical activity for the detection of driver drowsiness/sleepiness in advance in order to prevent accidents derived from fatigue. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-04-30T11:31:17Z No. of bitstreams: 1 ijimai20153_3_13_pdf_21406.pdf: 567666 bytes, checksum: fac1a68cde22feccadb1b94f23869944 (MD5); Made available in DSpace on 2020-04-30T11:31:17Z (GMT). No. of bitstreams: 1 ijimai20153_3_13_pdf_21406.pdf: 567666 bytes, checksum: fac1a68cde22feccadb1b94f23869944 (MD5)

Formalization of Event Perception and Event Appraisal Process

Formalization of Event Perception and Event Appraisal Process Jain, Shikha; Asawa, Krishna Integration of emotion in a virtual agent is a topic of research to depict human-like behavior in a simulated environment. For the last few decades, many researchers are working in the field of incorporating emotions in a virtual agent. In the emotion model, the behavior of an agent depends upon how the event is perceived by the agent with respect to the goal. Hence, perception of the event while considering the past experience, importance of event towards achieving goal, agent’s own capabilities and resources is an important process which directly influences the decision making and action selection. The proposed models, till date, are either too complex to adapt or are using a very few parameters to describe the event. So, in this paper, we propose an extension of perception process in an existing emotion model, EMIA and suggest the formalization of event perception and appraisal processes to make it adaptable. This has been carried out using five parameters for event description along-with fuzzy logic which makes the process more effective yet simple. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-04-30T08:49:57Z No. of bitstreams: 1 ijimai20153_3_9_pdf_30653.pdf: 530833 bytes, checksum: b7af708588dc8ebbd79b4b466e1e9daa (MD5); Made available in DSpace on 2020-04-30T08:49:57Z (GMT). No. of bitstreams: 1 ijimai20153_3_9_pdf_30653.pdf: 530833 bytes, checksum: b7af708588dc8ebbd79b4b466e1e9daa (MD5)

Security Framework for Agent-Based Cloud Computing

Security Framework for Agent-Based Cloud Computing Venkateshwaran, K.; Malviya, Anu; Dikshit, Utkarsha; Venkatesan, S. Agent can play a key role in bringing suitable cloud services to the customer based on their requirements. In agent based cloud computing, agent does negotiation, coordination, cooperation and collaboration on behalf of the customer to make the decisions in efficient manner. However the agent based cloud computing have some security issues like (a.) addition of malicious agent in the cloud environment which could demolish the process by attacking other agents, (b.) denial of service by creating flooding attacks on other involved agents. (c.) Some of the exceptions in the agent interaction protocol such as Not-Understood and Cancel_Meta protocol can be misused and may lead to terminating the connection of all the other agents participating in the negotiating services. Also, this paper proposes algorithms to solve these issues to ensure that there will be no intervention of any malicious activities during the agent interaction. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-04-30T08:19:40Z No. of bitstreams: 1 ijimai20153_3_4_pdf_21054.pdf: 820069 bytes, checksum: dadeb6f6f5cefb08422a07c3b656c83b (MD5); Made available in DSpace on 2020-04-30T08:19:40Z (GMT). No. of bitstreams: 1 ijimai20153_3_4_pdf_21054.pdf: 820069 bytes, checksum: dadeb6f6f5cefb08422a07c3b656c83b (MD5)

Swift vs. Objective-C: A New Programming Language

Swift vs. Objective-C: A New Programming Language González García, Cristian; Pascual Espada, Jordán; Pelayo García-Bustelo, B. Cristina; Cueva Lovelle, Juan Manuel The appearance of a new programming language gives the necessity to contrast its contribution with the existing programming languages to evaluate the novelties and improvements that the new programming language offers for developers. These kind of studies can show us the efficiency, improvements and useful or uselessness of the new programming languages. Also these studies can show us the good or bad properties of the existing programming languages. For these reasons, these studies allow us to know if the new programming language is offering improvements or relapses. In this article, we compare the new programming language of Apple, Swift, with the main programming language of Apple before Swift, Objective-C. We are going to show the differences, characteristics and novelties to verify the words of Apple about Swift. With that we want to answer the next question: Is Swift a new programming language easier, more secure and quicker to develop than Objective-C? Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-04-30T07:33:57Z No. of bitstreams: 1 ijimai20153_3_10_pdf_19818.pdf: 571480 bytes, checksum: 06479916f91cccccf6a804269633ecb4 (MD5); Made available in DSpace on 2020-04-30T07:33:57Z (GMT). No. of bitstreams: 1 ijimai20153_3_10_pdf_19818.pdf: 571480 bytes, checksum: 06479916f91cccccf6a804269633ecb4 (MD5)

IJIMAI Editor's Note - Vol. 3 Issue 3

IJIMAI Editor's Note - Vol. 3 Issue 3 Montenegro-Marin, Carlos Enrique The research works presented in this issue are based on various topics of interest, among which are included: 3D Interface, Information Extraction, Artificial immune system, Security in Cloud Computing, Genetic Algorithm, Activity recognition, 3D Render Distribution, Software visualization, Event Perception, New Programming Language, Distributed computing, MOOC environments, etc. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-04-30T07:03:58Z No. of bitstreams: 1 ijimai20153_3_0_pdf_91265.pdf: 149997 bytes, checksum: 632499a44f9b6aa991aeb8edf13a1a9c (MD5); Made available in DSpace on 2020-04-30T07:03:58Z (GMT). No. of bitstreams: 1 ijimai20153_3_0_pdf_91265.pdf: 149997 bytes, checksum: 632499a44f9b6aa991aeb8edf13a1a9c (MD5)

GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

GPGPU Implementation of a Genetic Algorithm for Stereo Refinement Arranz, Álvaro; Alvar, Manuel During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-04-23T07:40:09Z No. of bitstreams: 1 ijimai20143_2_9_pdf_91775.pdf: 1166781 bytes, checksum: f80fa87cd7d8537f083c9ad6a8f0ad4a (MD5); Made available in DSpace on 2020-04-23T07:40:09Z (GMT). No. of bitstreams: 1 ijimai20143_2_9_pdf_91775.pdf: 1166781 bytes, checksum: f80fa87cd7d8537f083c9ad6a8f0ad4a (MD5)

Empirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem

Empirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem Chiroque, Luis F.; Cordobés, Héctor; Fernández Anta, Antonio; García Leiva, Rafael A; Morere, Philippe; Ornella, Lorenzo; Pérez, Fernando; Santos, Agustín Recommendation engines (RE) are becoming highly popular, e.g., in the area of e-commerce. A RE offers new items (products or content) to users based on their profile and historical data. The most popular algorithms used in RE are based on collaborative filtering. This technique makes recommendations based on the past behavior of other users and the similarity between users and items. In this paper we have evaluated the performance of several RE based on the properties of the networks formed by users and items. The RE use in a novel way graph theoretic concepts like edges weights or network flow. The evaluation has been conducted in a real environment (ecosystem) for recommending apps to smartphone users. The analysis of the results allows concluding that the effectiveness of a RE can be improved if the age of the data, and if a global view of the data is considered. It also shows that graph-based RE are effective, but more experiments are required for a more accurate characterization of their properties. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-04-23T06:26:41Z No. of bitstreams: 1 ijimai20143_2_4_pdf_92465.pdf: 667785 bytes, checksum: 87d5e9c0890e250a8433d1c1197c9939 (MD5); Made available in DSpace on 2020-04-23T06:26:41Z (GMT). No. of bitstreams: 1 ijimai20143_2_4_pdf_92465.pdf: 667785 bytes, checksum: 87d5e9c0890e250a8433d1c1197c9939 (MD5)

Editor's Note

Editor's Note Mochón, F.; Gonzalvez-Cabañas, J.C. The term 'Digital Economy' was coined for the first time by Don Tapscott in 1995 in his best-seller The Digital Economy: Promise and Peril in the Age of Networked Intelligence. When he wrote the book 20 years ago, he announced how he thought the Internet would fully transform the nature of business and government. We have now extended the concept, illustrating how digital technologies are rapidly transforming business practices, the economy and societies. Technology, and its impact on business strategy and society, continues to rise in importance. The Digital Economy, sometimes also called “Digital Business” has become a philosophy for many top executive teams as they seek competitive advantages in a world of fast moving technological change. When we talk about digital technologies, we are not only talking about the internet, nor only ICT (Information and Communications Technology), but other concepts such as mobile, telecommunications or content. The digital economy is by no means an exclusively economic concept. Therefore, it might be more appropriate to speak of digital society or digital technology. What matters is that digital is a transverse concept that affects individuals, businesses and public administrations. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-04-22T09:21:44Z No. of bitstreams: 1 ijimai20143_2_0_pdf_24753.pdf: 266741 bytes, checksum: 8aa1a1d9d285168d11cc1f1f5061a085 (MD5); Made available in DSpace on 2020-04-22T09:21:44Z (GMT). No. of bitstreams: 1 ijimai20143_2_0_pdf_24753.pdf: 266741 bytes, checksum: 8aa1a1d9d285168d11cc1f1f5061a085 (MD5)

Procedural Content Generation for Real-Time Strategy Games

Procedural Content Generation for Real-Time Strategy Games Lara-Cabrera, Raúl; Nogueira-Collazo, Mariela; Cotta, Carlos; Fernández-Leiva, Antonio J. Videogames are one of the most important and profitable sectors in the industry of entertainment. Nowadays, the creation of a videogame is often a large-scale endeavor and bears many similarities with, e.g., movie production. On the central tasks in the development of a videogame is content generation, namely the definition of maps, terrains, non-player characters (NPCs) and other graphical, musical and AI-related components of the game. Such generation is costly due to its complexity, the great amount of work required and the need of specialized manpower. Hence the relevance of optimizing the process and alleviating costs. In this sense, procedural content generation (PCG) comes in handy as a means of reducing costs by using algorithmic techniques to automatically generate some game contents. PCG also provides advantages in terms of player experience since the contents generated are typically not fixed but can vary in different playing sessions, and can even adapt to the player herself. For this purpose, the underlying algorithmic technique used for PCG must be also flexible and adaptable. This is the case of computational intelligence in general and evolutionary algorithms in particular. In this work we shall provide an overview of the use of evolutionary intelligence for PCG, with special emphasis on its use within the context of real-time strategy games. We shall show how these techniques can address both playability and aesthetics, as well as improving the game AI. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-04-21T11:57:36Z No. of bitstreams: 1 ijimai20143_2_5_pdf_73775.pdf: 676674 bytes, checksum: 8614fb75083d47f23e59f7ce7d52edc9 (MD5); Made available in DSpace on 2020-04-21T11:57:36Z (GMT). No. of bitstreams: 1 ijimai20143_2_5_pdf_73775.pdf: 676674 bytes, checksum: 8614fb75083d47f23e59f7ce7d52edc9 (MD5)

Mining Web-based Educational Systems to Predict Student Learning Achievements

Mining Web-based Educational Systems to Predict Student Learning Achievements del Campo-Ávila, José; Conejo, Ricardo; Triguero, Francisco; Morales-Bueno, Rafael Educational Data Mining (EDM) is getting great importance as a new interdisciplinary research field related to some other areas. It is directly connected with Web-based Educational Systems (WBES) and Data Mining (DM, a fundamental part of Knowledge Discovery in Databases). The former defines the context: WBES store and manage huge amounts of data. Such data are increasingly growing and they contain hidden knowledge that could be very useful to the users (both teachers and students). It is desirable to identify such knowledge in the form of models, patterns or any other representation schema that allows a better exploitation of the system. The latter reveals itself as the tool to achieve such discovering. Data mining must afford very complex and different situations to reach quality solutions. Therefore, data mining is a research field where many advances are being done to accommodate and solve emerging problems. For this purpose, many techniques are usually considered. In this paper we study how data mining can be used to induce student models from the data acquired by a specific Web-based tool for adaptive testing, called SIETTE. Concretely we have used top down induction decision trees algorithms to extract the patterns because these models, decision trees, are easily understandable. In addition, the conducted validation processes have assured high quality models. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-12T13:07:17Z No. of bitstreams: 1 ijimai20143_2_6_pdf_24850.pdf: 492250 bytes, checksum: 6ac16a37199c49d8c63c7d5a89129b2b (MD5); Made available in DSpace on 2020-02-12T13:07:17Z (GMT). No. of bitstreams: 1 ijimai20143_2_6_pdf_24850.pdf: 492250 bytes, checksum: 6ac16a37199c49d8c63c7d5a89129b2b (MD5)

Differences in Measuring Market Risk in Four Subsectors of the Digital Economy

Differences in Measuring Market Risk in Four Subsectors of the Digital Economy Benito, S.; de Juan, R.; Gómez, R.; Mochón, F. This paper defends the wisdom of not considering the Digital Economy to be one homogeneous sector. Our hypothesis is that it is best to consider it the result of adding four different subsectors. We test whether indeed the economic and financial performance of a portfolio of listed companies in each of the four subsectors presents relevant differences. We use the value at risk measure to estimate market risk of the four subsectors of the digital economy. The riskiest subsector is Mobile/Internet Contents & Services followed by SW&IT Services and Application Software. On the contrary, the Telecom sector is by far the safest one. These results support the hypothesis that the Digital Economy is not a homogeneous sector. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-12T10:13:35Z No. of bitstreams: 1 ijimai20143_2_1_pdf_76355.pdf: 874869 bytes, checksum: e36ba588d66b61ee5e204a78aac8d106 (MD5); Made available in DSpace on 2020-02-12T10:13:35Z (GMT). No. of bitstreams: 1 ijimai20143_2_1_pdf_76355.pdf: 874869 bytes, checksum: e36ba588d66b61ee5e204a78aac8d106 (MD5)

A Repository of Semantic Open EHR Archetypes

A Repository of Semantic Open EHR Archetypes Sánchez, Fernando; Benavides, Samuel; Moreno, Fernando; Garzón, Guillermo; Roldan Garcia, Maria del Mar; Navas-Delgado, Ismael; Aldana-Montes, Jose F. This paper describes a repository of openEHR archetypes that have been translated to OWL. In the work presented here, five different CKMs (Clinical Knowledge Managers) have been downloaded and the archetypes have been translated to OWL. This translation is based on an existing translator that has been improved to solve programming problems with certain structures. As part of the repository a tool has been developed to keep it always up-to-date. So, any change in one of the CKMs (addition, elimination or even change of an archetype) will involve translating the changed archetypes once more. The repository is accessible through a Web interface (http://www.openehr.es/). Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-12T09:59:34Z No. of bitstreams: 1 ijimai20143_2_7_pdf_25353.pdf: 697072 bytes, checksum: f30762d0bb6d068520d04fe86a08954e (MD5); Made available in DSpace on 2020-02-12T09:59:34Z (GMT). No. of bitstreams: 1 ijimai20143_2_7_pdf_25353.pdf: 697072 bytes, checksum: f30762d0bb6d068520d04fe86a08954e (MD5)

The Digital Economy: Social Interaction Technologies – an Overview

The Digital Economy: Social Interaction Technologies – an Overview Redondo, Teófilo Social interaction technologies (SIT) is a very broad field that encompasses a large list of topics: interactive and networked computing, mobile social services and the Social Web, social software and social media, marketing and advertising, various aspects and uses of blogs and podcasting, corporate value and web-based collaboration, e-government and online democracy, virtual volunteering, different aspects and uses of folksonomies, tagging and the social semantic cloud of tags, blog-based knowledge management systems, systems of online learning, with their ePortfolios, blogs and wikis in education and journalism, legal issues and social interaction technology, dataveillance and online fraud, neogeography, social software usability, social software in libraries and nonprofit organizations, and broadband visual communication technology for enhancing social interaction. The fact is that the daily activities of many businesses are being socialized, as is the case with Yammer (https://www.yammer.com/), the social enterprise social network. The leitmotivs of social software are: create, connect, contribute, and collaborate. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-11T12:38:43Z No. of bitstreams: 1 ijimai20143_2_2_pdf_12490.pdf: 471735 bytes, checksum: 5c61e69f5d84955b4ee17174e83e01df (MD5); Made available in DSpace on 2020-02-11T12:38:43Z (GMT). No. of bitstreams: 1 ijimai20143_2_2_pdf_12490.pdf: 471735 bytes, checksum: 5c61e69f5d84955b4ee17174e83e01df (MD5)

Auto-adaptative Robot-aided Therapy based in 3D Virtual Tasks controlled by a Supervised and Dynamic Neuro-Fuzzy System

Auto-adaptative Robot-aided Therapy based in 3D Virtual Tasks controlled by a Supervised and Dynamic Neuro-Fuzzy System LLedo, L. D.; Bertomeu, A.; Díez, J.; Badesa, F. J.; Morales, R.; Sabater, J. M.; Garcia-Aracil, N. This paper presents an application formed by a classification method based on the architecture of ART neural network (Adaptive Resonance Theory) and the Fuzzy Set Theory to classify physiological reactions in order to automatically and dynamically adapt a robot-assisted rehabilitation therapy to the patient needs, using a three-dimensional task in a virtual reality system. Firstly, the mathematical and structural model of the neuro-fuzzy classification method is described together with the signal and training data acquisition. Then, the virtual designed task with physics behavior and its development procedure are explained. Finally, the general architecture of the experimentation for the auto-adaptive therapy is presented using the classification method with the virtual reality exercise. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-11T12:01:13Z No. of bitstreams: 1 ijimai20143_2_8_pdf_17229.pdf: 837804 bytes, checksum: a780230c5aad37f464b38d9e2f45c8af (MD5); Made available in DSpace on 2020-02-11T12:01:13Z (GMT). No. of bitstreams: 1 ijimai20143_2_8_pdf_17229.pdf: 837804 bytes, checksum: a780230c5aad37f464b38d9e2f45c8af (MD5)

New Challenges on Crossplatform Digital Contents

New Challenges on Crossplatform Digital Contents Iglesias Feijoo, Jesús; Amat Gomariz, Guillermo When we speak about devices and platforms, generally we think about those of general use which are currently available (mainly smartphones and tablets). Surely, we would forget all those which are on the way (watches, glasses, cars) and those which are coming. The Internet of Things will transform the technological world in which we are into an amalgamation of devices and interfaces. This paper analyses the challenge for the coming years of getting all these new devices to communicate between them, regardless of their technology and the platforms they use, and it is based on the works done under the Visio Project, funded by the Spanish Ministry of Industry, Energy and Tourism. Finally, a truly universal platform to avoid market fragmentation and provide access to information and services is proposed. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-11T11:21:25Z No. of bitstreams: 1 ijimai20143_2_3_pdf_32365.pdf: 505089 bytes, checksum: 282dbd01abaced45a4e98659f73a2f61 (MD5); Made available in DSpace on 2020-02-11T11:21:25Z (GMT). No. of bitstreams: 1 ijimai20143_2_3_pdf_32365.pdf: 505089 bytes, checksum: 282dbd01abaced45a4e98659f73a2f61 (MD5)

Sphericall: A Human/Artificial Intelligence interaction experience

Sphericall: A Human/Artificial Intelligence interaction experience Gechter, Frack; Ronzani, Bruno; Rioli, Fabien Multi-agent systems are now wide spread in scientific works and in industrial applications. Few applications deal with the Human/Multi-agent system interaction. Multi-agent systems are characterized by individual entities, called agents, in interaction with each other and with their environment. Multi-agent systems are generally classified into complex systems categories since the global emerging phenomenon cannot be predicted even if every component is well known. The systems developed in this paper are named reactive because they behave using simple interaction models. In the reactive approach, the issue of Human/system interaction is hard to cope with and is scarcely exposed in literature. This paper presents Sphericall, an application aimed at studying Human/Complex System interactions and based on two physics inspired multi-agent systems interacting together. The Sphericall device is composed of a tactile screen and a spherical world where agents evolve. This paper presents both the technical background of Sphericall project and a feedback taken from the demonstration performed during OFFF Festival in La Villette (Paris). Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-11T10:31:50Z No. of bitstreams: 1 ijimai20143_1_7_pdf_12621.pdf: 1101589 bytes, checksum: 92fa527c4d19a1f153d14a1534107387 (MD5); Made available in DSpace on 2020-02-11T10:31:50Z (GMT). No. of bitstreams: 1 ijimai20143_1_7_pdf_12621.pdf: 1101589 bytes, checksum: 92fa527c4d19a1f153d14a1534107387 (MD5)

Scene integration for online VR advertising clouds

Scene integration for online VR advertising clouds Kalochristianakis, Michael; Zampoglou, Markos; Kontakis, Kostas; Kapetanakis, Kostas; Malamos, Athanasios This paper presents a scene composition approach that allows the combinational use of standard three dimensional objects, called models, in order to create X3D scenes. The module is an integral part of a broader design aiming to construct large scale online advertising infrastructures that rely on virtual reality technologies. The architecture addresses a number of problems regarding remote rendering for low end devices and last but not least, the provision of scene composition and integration. Since viewers do not keep information regarding individual input models or scenes, composition requires the consideration of mechanisms that add state to viewing technologies. In terms of this work we extended a well-known, open source X3D authoring tool. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-11T09:32:51Z No. of bitstreams: 1 ijimai20143_1_2_pdf_19361.pdf: 582719 bytes, checksum: 93f45ddd198d3c3bd444b26e598dfef9 (MD5); Made available in DSpace on 2020-02-11T09:32:51Z (GMT). No. of bitstreams: 1 ijimai20143_1_2_pdf_19361.pdf: 582719 bytes, checksum: 93f45ddd198d3c3bd444b26e598dfef9 (MD5)

Editor’s Note

Editor’s Note González-Crespo, Rubén The research works presented in this issue are based on various topics of interest, among which are included: bayesian networks, evolutionary algorithms, virtual reality, web advertising, 3D technologies, traffic expression, Smart Cities, computational sustainability, computer vision, image recognition, deep neural networks, graphical models, mobile devices, human/complex system interactions, multi-agent systems, Physics inspired behaviours, etc. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-10T12:57:03Z No. of bitstreams: 1 ijimai20143_1_0_pdf_15248.pdf: 142046 bytes, checksum: f56d1323e5e2b96c458f882377007a74 (MD5); Made available in DSpace on 2020-02-10T12:57:03Z (GMT). No. of bitstreams: 1 ijimai20143_1_0_pdf_15248.pdf: 142046 bytes, checksum: f56d1323e5e2b96c458f882377007a74 (MD5)

Assessing Road Traffic Expression

Assessing Road Traffic Expression Silva, Fábio; Analide, Cesar; Novais, Paulo Road traffic is a problem which is increasing in cities with large population. Unrelated to this fact the number of portable and wearable devices has also been increasing throughout the population of most countries. With this advent, the capacity to monitor and register data about people habits and locations as well as more complex data such as intensity and strength of movements has created an opportunity to contribute to the general wealth and comfort within these environments. Ambient Intelligence and Intelligent Decision Making processes can benefit from the knowledge gathered by these devices to improve decisions on everyday tasks such as deciding navigation routes by car, bicycle or other means of transportation and avoiding route perils. The concept of computational sustainability may also be applied to this problem. Current applications in this area demonstrate the usefulness of real time system that inform the user of certain conditions in the surrounding area. On the other hand, the approach presented in this work aims to describe models and approaches to automatically identify current states of traffic inside cities and use methods from computer science to improve overall comfort and the sustainability of road traffic both with the user and the environment in mind. Such objective is delivered by analyzing real time contributions from those mobile ubiquitous devices to identifying problematic situations and areas under a defined criteria that have significant influence towards a sustainable use of the road transport infrastructure. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-10T12:27:48Z No. of bitstreams: 1 ijimai20143_1_3_pdf_13985.pdf: 935758 bytes, checksum: 631e3601c0468343167469f602ece2d7 (MD5); Made available in DSpace on 2020-02-10T12:27:48Z (GMT). No. of bitstreams: 1 ijimai20143_1_3_pdf_13985.pdf: 935758 bytes, checksum: 631e3601c0468343167469f602ece2d7 (MD5)

Neural Networks through Shared Maps in Mobile Devices

Neural Networks through Shared Maps in Mobile Devices Raveane, William; González Arrieta, María Angélica We introduce a hybrid system composed of a convolutional neural network and a discrete graphical model for image recognition. This system improves upon traditional sliding window techniques for analysis of an image larger than the training data by effectively processing the full input scene through the neural network in less time. The final result is then inferred from the neural network output through energy minimization to reach a more precize localization than what traditional maximum value class comparisons yield. These results are apt for applying this process in a mobile device for real time image recognition. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-10T12:09:00Z No. of bitstreams: 1 ijimai20143_1_4_pdf_46255.pdf: 829893 bytes, checksum: c59549acc727c50adb802cb749a29a40 (MD5); Made available in DSpace on 2020-02-10T12:09:00Z (GMT). No. of bitstreams: 1 ijimai20143_1_4_pdf_46255.pdf: 829893 bytes, checksum: c59549acc727c50adb802cb749a29a40 (MD5)

An Effective Approach for Mobile ad hoc Network via I-Watchdog Protocol

An Effective Approach for Mobile ad hoc Network via I-Watchdog Protocol Lal, Nidhi Mobile ad hoc network (MANET) is now days become very famous due to their fixed infrastructure-less quality and dynamic nature. They contain a large number of nodes which are connected and communicated to each other in wireless nature. Mobile ad hoc network is a wireless technology that contains high mobility of nodes and does not depend on the background administrator for central authority, because they do not contain any infrastructure. Nodes of the MANET use radio wave for communication and having limited resources and limited computational power. The Topology of this network is changing very frequently because they are distributed in nature and self-configurable. Due to its wireless nature and lack of any central authority in the background, Mobile ad hoc networks are always vulnerable to some security issues and performance issues. The security imposes a huge impact on the performance of any network. Some of the security issues are black hole attack, flooding, wormhole attack etc. In this paper, we will discuss issues regarding low performance of Watchdog protocol used in the MANET and proposed an improved Watchdog mechanism, which is called by I-Watchdog protocol that overcomes the limitations of Watchdog protocol and gives high performance in terms of throughput, delay. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-10T11:58:00Z No. of bitstreams: 1 ijimai20143_1_5_pdf_14247.pdf: 615986 bytes, checksum: 1e1ca7c435b0eb48310becfff8014874 (MD5); Made available in DSpace on 2020-02-10T11:58:00Z (GMT). No. of bitstreams: 1 ijimai20143_1_5_pdf_14247.pdf: 615986 bytes, checksum: 1e1ca7c435b0eb48310becfff8014874 (MD5)

Design of a Mutual Exclusion and Deadlock Algorithm in PCBSD – FreeBSD

Design of a Mutual Exclusion and Deadlock Algorithm in PCBSD – FreeBSD Caicedo Acosta, Libertad; Ospina Acosta, Camilo Andrés; Gelvez García, Nancy Yaneth; Romero Villalobos, Oswaldo Alberto This paper shows the implementation of mutual exclusion in PCBSD-FreeBSD operating systems on SMPng environments, providing solutions to problems like investment priority, priority propagation, interlock, CPU downtime, deadlocks, between other. Mutex Control concept is introduced as a solution to these problems through the integration of the scheduling algorithm of multiple queues fed back and mutexes. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-10T10:12:44Z No. of bitstreams: 1 ijimai20143_1_6_pdf_26139.pdf: 826640 bytes, checksum: 898f1afba944443c2468446773ad8717 (MD5); Made available in DSpace on 2020-02-10T10:12:44Z (GMT). No. of bitstreams: 1 ijimai20143_1_6_pdf_26139.pdf: 826640 bytes, checksum: 898f1afba944443c2468446773ad8717 (MD5)

A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks Fukuda, Sho; Yamanaka, Yuuma; Yoshihiro, Takuya Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning), and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-10T08:42:25Z No. of bitstreams: 1 ijimai20143_1_1_pdf_24199.pdf: 826297 bytes, checksum: 0ed207a2fc545bbf338e63d7f5705bf2 (MD5); Made available in DSpace on 2020-02-10T08:42:25Z (GMT). No. of bitstreams: 1 ijimai20143_1_1_pdf_24199.pdf: 826297 bytes, checksum: 0ed207a2fc545bbf338e63d7f5705bf2 (MD5)

Big Data and Learning Analytics in Blended Learning Environments: Benefits and Concerns

Big Data and Learning Analytics in Blended Learning Environments: Benefits and Concerns Picciano, Anthony G. The purpose of this article is to examine big data and learning analytics in blended learning environments. It will examine the nature of these concepts, provide basic definitions, and identify the benefits and concerns that apply to their development and implementation. This article draws on concepts associated with data-driven decision making, which evolved in the 1980s and 1990s, and takes a sober look at big data and analytics. It does not present them as panaceas for all of the issues and decisions faced by higher education administrators, but sees them as part of solutions, although not without significant investments of time and money to achieve worthwhile benefits. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-07T13:19:21Z No. of bitstreams: 1 ijimai20142_7_5_pdf_15146.pdf: 494487 bytes, checksum: 3164ce650970287711026731323f1586 (MD5); Made available in DSpace on 2020-02-07T13:19:21Z (GMT). No. of bitstreams: 1 ijimai20142_7_5_pdf_15146.pdf: 494487 bytes, checksum: 3164ce650970287711026731323f1586 (MD5)

Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study

Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study Corbi, Alberto; Burgos, Daniel Recommender systems require input information in order to properly operate and deliver content or behaviour suggestions to end users. eLearning scenarios are no exception. Users are current students and recommendations can be built upon paths (both formal and informal), relationships, behaviours, friends, followers, actions, grades, tutor interaction, etc. A recommender system must somehow retrieve, categorize and work with all these details. There are several ways to do so: from raw and inelegant database access to more curated web APIs or even via HTML scrapping. New server-centric user-action logging and monitoring standard technologies have been presented in past years by several groups, organizations and standard bodies. The Experience API (xAPI), detailed in this article, is one of these. In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems. We next review standardization efforts in this area; finally, we focus on xAPI and the potential interaction with the LIME model, which will be also summarized below. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-07T13:10:52Z No. of bitstreams: 1 ijimai20142_7_6_pdf_27449.pdf: 927662 bytes, checksum: 424250d2b9c8eb81d03124ebe87cba84 (MD5); Made available in DSpace on 2020-02-07T13:10:52Z (GMT). No. of bitstreams: 1 ijimai20142_7_6_pdf_27449.pdf: 927662 bytes, checksum: 424250d2b9c8eb81d03124ebe87cba84 (MD5)

Analysis of Gait Pattern to Recognize the Human Activities

Analysis of Gait Pattern to Recognize the Human Activities Prakash Gupta, Jay; Dixit, Pushkar; Singh, Nishant; Bhaskar Aemwal, Vijay Human activity recognition based on the computer vision is the process of labelling image sequences with action labels. Accurate systems for this problem are applied in areas such as visual surveillance, human computer interaction and video retrieval. The challenges are due to variations in motion, recording settings and gait differences. Here we propose an approach to recognize the human activities through gait. Activity recognition through Gait is the process of identifying an activity by the manner in which they walk. The identification of human activities in a video, such as a person is walking, running, jumping, jogging etc are important activities in video surveillance. We contribute the use of Model based approach for activity recognition with the help of movement of legs only. Experimental results suggest that our method are able to recognize the human activities with a good accuracy rate and robust to shadows present in the videos Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-07T12:28:26Z No. of bitstreams: 1 ijimai20142_7_1_pdf_23675.pdf: 691721 bytes, checksum: 7cee259713f458b360f84b87d8ac65c2 (MD5); Made available in DSpace on 2020-02-07T12:28:26Z (GMT). No. of bitstreams: 1 ijimai20142_7_1_pdf_23675.pdf: 691721 bytes, checksum: 7cee259713f458b360f84b87d8ac65c2 (MD5)

Integration of Multiple Data Sources for predicting the Engagement of Students in Practical Activities

Integration of Multiple Data Sources for predicting the Engagement of Students in Practical Activities Tobarra, Llanos; Ros, Salvador; Hernández, Roberto; Robles-Gómez, Antonio; Caminero, Agustín C.; Pastor, Rafael This work presents the integration of an automatic assessment system for virtual/remote laboratories and the institutional Learning Management System (LMS), in order to analyze the students’ progress and their collaborative learning in virtual/remote laboratories. As a result of this integration, it is feasible to extract useful information for the characterization of the students’ learning process and detecting the students’ engagement with the practical activities of our subjects. From this integration, a dashboard has been created to graphically present to lecturers the analyzed results. Thanks to this, faculty can use the analyzed information in order to guide the learning/teaching process of each student. As an example, a subject focused on the configuration of network services has been chosen to implement our proposal. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-07T11:48:04Z No. of bitstreams: 1 ijimai20142_7_7_pdf_21332.pdf: 778074 bytes, checksum: 7ff59829d8dca9b02c6e19e85b47fa6f (MD5); Made available in DSpace on 2020-02-07T11:48:04Z (GMT). No. of bitstreams: 1 ijimai20142_7_7_pdf_21332.pdf: 778074 bytes, checksum: 7ff59829d8dca9b02c6e19e85b47fa6f (MD5)

Recognition of Emotions using Energy Based Bimodal Information Fusion and Correlation

Recognition of Emotions using Energy Based Bimodal Information Fusion and Correlation Asawa, Krishna; Manchanda, Priyanka Multi-sensor information fusion is a rapidly developing research area which forms the backbone of numerous essential technologies such as intelligent robotic control, sensor networks, video and image processing and many more. In this paper, we have developed a novel technique to analyze and correlate human emotions expressed in voice tone & facial expression. Audio and video streams captured to populate audio and video bimodal data sets to sense the expressed emotions in voice tone and facial expression respectively. An energy based mapping is being done to overcome the inherent heterogeneity of the recorded bi-modal signal. The fusion process uses sampled and mapped energy signal of both modalities’s data stream and further recognize the overall emotional component using Support Vector Machine (SVM) classifier with the accuracy 93.06%. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T13:43:01Z No. of bitstreams: 1 ijimai20142_7_2_pdf_26941.pdf: 747112 bytes, checksum: 4c4f2f247d8fcfb61e12ec6636a4889a (MD5); Made available in DSpace on 2020-02-05T13:43:01Z (GMT). No. of bitstreams: 1 ijimai20142_7_2_pdf_26941.pdf: 747112 bytes, checksum: 4c4f2f247d8fcfb61e12ec6636a4889a (MD5)

Social Networks as Learning Environments for Higher Education

Social Networks as Learning Environments for Higher Education Cortés, J.A.; Lozano, J.O. Learning is considered as a social activity, a student does not learn only of the teacher and the textbook or only in the classroom, learn also from many other agents related to the media, peers and society in general. And since the explosion of the Internet, the information is within the reach of everyone, is there where the main area of opportunity in new technologies applied to education, as well as taking advantage of recent socialization trends that can be leveraged to improve not only informing of their daily practices, but rather as a tool that explore different branches of education research. One can foresee the future of higher education as a social learning environment, open and collaborative, where people construct knowledge in interaction with others, in a comprehensive manner. The mobility and ubiquity that provide mobile devices enable the connection from anywhere and at any time. In modern educational environments can be expected to facilitate mobile devices in the classroom expansion in digital environments, so that students and teachers can build the teaching-learning process collectively, this partial derivative results in the development of draft research approved by the CONADI in “Universidad Cooperativa de Colombia”, "Social Networks: A teaching strategy in learning environments in higher education." Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T13:10:53Z No. of bitstreams: 1 ijimai20142_7_8_pdf_87965.pdf: 424646 bytes, checksum: 4941c27a64595243f261333c1b30c183 (MD5); Made available in DSpace on 2020-02-05T13:10:53Z (GMT). No. of bitstreams: 1 ijimai20142_7_8_pdf_87965.pdf: 424646 bytes, checksum: 4941c27a64595243f261333c1b30c183 (MD5)

Dissemination Matters: Influences of Dissemination Activities on User Types in an Online Educational Community

Dissemination Matters: Influences of Dissemination Activities on User Types in an Online Educational Community Yuan, Min; Recker, Mimi Emerging online educational communities provide spaces for teachers to find resources, create instructional activities, and share these activities with others. Within these online communities, individual users’ activities may vary widely, and thus different user types can be identified. In addition, users’ patterns of activities in online communities are dynamic, and further can be affected by dissemination activities. Through analyzing usage analytics in an online teacher community called the Instructional Architect, this study explores the influences of dissemination activities on the usage patterns of different user types. Results show that dissemination activities can play an important role in encouraging users’ active participation, while the absence of dissemination activities can further increase participation inequality. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T12:56:30Z No. of bitstreams: 1 ijimai20142_7_3_pdf_32458.pdf: 558405 bytes, checksum: ae2ff68a41dd9683c506ae2cfd822d14 (MD5); Made available in DSpace on 2020-02-05T12:56:30Z (GMT). No. of bitstreams: 1 ijimai20142_7_3_pdf_32458.pdf: 558405 bytes, checksum: ae2ff68a41dd9683c506ae2cfd822d14 (MD5)

Editor’s Note

Editor’s Note de-la-Fuente-Valentín, Luis; Burgos, Daniel; Mazza, Riccardo This special issue, Special Issue on Multisensor user tracking and analytics to improve education and other application fields, concentrates on the practical and experimental use of data mining and analytics techniques, specially focusing on the educational area. The selected papers deal with the most relevant issues in the field, such as the integration of data from different sources, the identification of data suitable for the problem analysis, and the validation of the analytics techniques as support in the decision making process. The application fields of the analytics techniques presented in this paper have a clear focus on the educational area (where Learning Analytics has emerged as a buzzword in the recent years) but not restricted to it. The result is a collection of use cases, experimental validations and analytics systems with a clear contribution to the state of the art. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T12:42:45Z No. of bitstreams: 1 ijimai20142_7_0_pdf_30073.pdf: 282935 bytes, checksum: 93c3f6fb2de1861c76b164e0c42a9155 (MD5); Made available in DSpace on 2020-02-05T12:42:45Z (GMT). No. of bitstreams: 1 ijimai20142_7_0_pdf_30073.pdf: 282935 bytes, checksum: 93c3f6fb2de1861c76b164e0c42a9155 (MD5)

Personality and Education Mining based Job Advisory System

Personality and Education Mining based Job Advisory System Choudhary, Rajendra S.; Kukreja, Rajul; Jain, Nitika; Jain, Shikha Every job demands an employee with some specific qualities in addition to the basic educational qualification. For example, an introvert person cannot be a good leader despite of a very good academic qualification. Thinking and logical ability is required for a person to be a successful software engineer. So, the aim of this paper is to present a novel approach for advising an ideal job to the job seeker while considering his personality trait and educational qualification both. Very well-known theories of personality like MBTI indicator and OCEAN theory, are used for personality mining. For education mining, score based system is used. The score based system captures the information from attributes like most scoring subject, dream job etc. After personality mining, the resultant values are coalesced with the information extracted from education mining. And finally, the most suited jobs, in terms of personality and educational qualification are recommended to the job seekers. The experiment is conducted on the students who have earned an engineering degree in the field of computer science, information technology and electronics. Nevertheless, the same architecture can easily be extended to other educational degrees also. To the best of the author’s knowledge, this is a first e-job advisory system that recommends the job best suited as per one’s personality using MBTI and OCEAN theory both. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T12:16:42Z No. of bitstreams: 1 ijimai20142_7_4_pdf_92105.pdf: 431640 bytes, checksum: b6bc49b7833bad0cd1be763ed0dbf0e2 (MD5); Made available in DSpace on 2020-02-05T12:16:42Z (GMT). No. of bitstreams: 1 ijimai20142_7_4_pdf_92105.pdf: 431640 bytes, checksum: b6bc49b7833bad0cd1be763ed0dbf0e2 (MD5)

A Fault-Tolerant Mobile Computing Model Based On Scalable Replica

A Fault-Tolerant Mobile Computing Model Based On Scalable Replica Sati, Meenakshi; Vikash, Vivek; Bijalwan, Vishwanath; Kumari, Pinki; Raj, Manish; Balodhi, Meenu; Gairola, Priya; Bhaskar Semwal, Vijay The most frequent challenge faced by mobile user is stay connected with online data, while disconnected or poorly connected store the replica of critical data. Nomadic users require replication to store copies of critical data on their mobile machines. Existing replication services do not provide all classes of mobile users with the capabilities they require, which include: the ability for direct synchronization between any two replicas, support for large numbers of replicas, and detailed control over what files reside on their local (mobile) replica. Existing peer-to-peer solutions would enable direct communication, but suffers from dramatic scaling problems in the number of replicas, limiting the number of overall users and impacting performance. Roam is a replication system designed to satisfy the requirements of the mobile user. Roam is based on the Ward Model, replication architecture for mobile environments. Using the Ward Model and new distributed algorithms, Roam provides a scalable replication solution for the mobile user. We describe the motivation, design, and implementation of Roam and report its performance. Replication is extremely important in mobile environments because nomadic users require local copies of important data. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T11:37:47Z No. of bitstreams: 1 ijimai20142_6_7_pdf_73533.pdf: 541713 bytes, checksum: 8606a0d86a6cf45e70d2a48239d8445a (MD5); Made available in DSpace on 2020-02-05T11:37:47Z (GMT). No. of bitstreams: 1 ijimai20142_6_7_pdf_73533.pdf: 541713 bytes, checksum: 8606a0d86a6cf45e70d2a48239d8445a (MD5)

Robust Lossless Semi Fragile Information Protection in Images

Robust Lossless Semi Fragile Information Protection in Images Dixit, Pushkar; Singh, Nishant; Prakash Gupta, Jay Internet security finds it difficult to keep the information secure and to maintain the integrity of the data. Sending messages over the internet secretly is one of the major tasks as it is widely used for passing the message. In order to achieve security there must be some mechanism to protect the data against unauthorized access. A lossless data hiding scheme is proposed in this paper which has a higher embedding capacity than other schemes. Unlike other schemes that are used for embedding fixed amount of data, the proposed data hiding method is block based approach and it uses a variable data embedding in different blocks which reduces the chances of distortion and increases the hiding capacity of the image. When the data is recovered the original image can be restored without any distortion. Our experimental results indicate that the proposed solution can significantly support the data hiding problem. We achieved good Peak signal-to-noise ratio (PSNR) while hiding large amount of data into smoother regions. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T10:54:09Z No. of bitstreams: 1 ijimai20142_6_9_pdf_92745.pdf: 736624 bytes, checksum: 9c3175c6a69ca8d74491395c5f1e0084 (MD5); Made available in DSpace on 2020-02-05T10:54:09Z (GMT). No. of bitstreams: 1 ijimai20142_6_9_pdf_92745.pdf: 736624 bytes, checksum: 9c3175c6a69ca8d74491395c5f1e0084 (MD5)

Overlap Algorithms in Flexible Job-shop Scheduling

Overlap Algorithms in Flexible Job-shop Scheduling Gutiérrez, Celia The flexible Job-shop Scheduling Problem (fJSP) considers the execution of jobs by a set of candidate resources while satisfying time and technological constraints. This work, that follows the hierarchical architecture, is based on an algorithm where each objective (resource allocation, start-time assignment) is solved by a genetic algorithm (GA) that optimizes a particular fitness function, and enhances the results by the execution of a set of heuristics that evaluate and repair each scheduling constraint on each operation. The aim of this work is to analyze the impact of some algorithmic features of the overlap constraint heuristics, in order to achieve the objectives at a highest degree. To demonstrate the efficiency of this approach, experimentation has been performed and compared with similar cases, tuning the GA parameters correctly. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T09:53:40Z No. of bitstreams: 1 ijimai20142_6_5_pdf_59045.pdf: 1261543 bytes, checksum: 99c0b53045c01dc320643c3355e90a5b (MD5); Made available in DSpace on 2020-02-05T09:53:40Z (GMT). No. of bitstreams: 1 ijimai20142_6_5_pdf_59045.pdf: 1261543 bytes, checksum: 99c0b53045c01dc320643c3355e90a5b (MD5)

Our System IDCBR-MAS: from the Modelisation by AUML to the Implementation under JADE Platform

Our System IDCBR-MAS: from the Modelisation by AUML to the Implementation under JADE Platform Zouhair, Abdelhamid; En-Naimi, El Mokhtar; Amami, Benaissa; Boukachour, Hadhoum; Person, Patrick; Bertelle, Cyrille This paper presents our work in the field of Intelligent Tutoring System (ITS), in fact there is still the problem of knowing how to ensure an individualized and continuous learners follow-up during learning process, indeed among the numerous methods proposed, very few systems concentrate on a real time learners follow-up. Our work in this field develops the design and implementation of a Multi-Agents System Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. This approach involves 1) the use of Dynamic Case Based Reasoning to retrieve the past experiences that are similar to the learner’s traces (traces in progress), and 2) the use of Multi-Agents System. Our Work focuses on the use of the learner traces. When interacting with the platform, every learner leaves his/her traces on the machine. The traces are stored in database, this operation enriches collective past experiences. The traces left by the learner during the learning session evolve dynamically over time; the case-based reasoning must take into account this evolution in an incremental way. In other words, we do not consider each evolution of the traces as a new target, so the use of classical cycle Case Based reasoning in this case is insufficient and inadequate. In order to solve this problem, we propose a dynamic retrieving method based on a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). Through monitoring, comparing and analyzing these traces, the system keeps a constant intelligent watch on the platform, and therefore it detects the difficulties hindering progress, and it avoids possible dropping out. The system can support any learning subject. To help and guide the learner, the system is equipped with combined virtual and human tutors. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T09:30:49Z No. of bitstreams: 1 ijimai20142_6_6_pdf_27403.pdf: 1240400 bytes, checksum: 1fdcc12125021c869b3906c35623e42a (MD5); Made available in DSpace on 2020-02-05T09:30:49Z (GMT). No. of bitstreams: 1 ijimai20142_6_6_pdf_27403.pdf: 1240400 bytes, checksum: 1fdcc12125021c869b3906c35623e42a (MD5)

Reconstruction of High Resolution 3D Objects from Incomplete Images and 3D Information

Reconstruction of High Resolution 3D Objects from Incomplete Images and 3D Information Pacheco, Alexander; Bolívar, Holman; Pascual Espada, Jordán; González-Crespo, Rubén To this day, digital object reconstruction is a quite complex area that requires many techniques and novel approaches, in which high-resolution 3D objects present one of the biggest challenges. There are mainly two different methods that can be used to reconstruct high resolution objects and images: passive methods and active methods. This methods depend on the type of information available as input for modeling 3D objects. The passive methods use information contained in the images and the active methods make use of controlled light sources, such as lasers. The reconstruction of 3D objects is quite complex and there is no unique solution- The use of specific methodologies for the reconstruction of certain objects it’s also very common, such as human faces, molecular structures, etc. This paper proposes a novel hybrid methodology, composed by 10 phases that combine active and passive methods, using images and a laser in order to supplement the missing information and obtain better results in the 3D object reconstruction. Finally, the proposed methodology proved its efficiency in two complex topological complex objects. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-02-05T09:03:07Z No. of bitstreams: 1 ijimai20142_6_1_pdf_16808.pdf: 1646312 bytes, checksum: 8ec7375e728da483da7df9dd33774470 (MD5); Made available in DSpace on 2020-02-05T09:03:07Z (GMT). No. of bitstreams: 1 ijimai20142_6_1_pdf_16808.pdf: 1646312 bytes, checksum: 8ec7375e728da483da7df9dd33774470 (MD5)

A Hybrid Algorithm for Recognizing the Position of Ezafe Constructions in Persian Texts

A Hybrid Algorithm for Recognizing the Position of Ezafe Constructions in Persian Texts Noferesti, Samira; Shamsfard, Mehrnoush In the Persian language, an Ezafe construction is a linking element which joins the head of a phrase to its modifiers. The Ezafe in its simplest form is pronounced as –e, but generally not indicated in writing. Determining the position of an Ezafe is advantageous for disambiguating the boundary of the syntactic phrases which is a fundamental task in most natural language processing applications. This paper introduces a framework for combining genetic algorithms with rule-based models that brings the advantages of both approaches and overcomes their problems. This framework was used for recognizing the position of Ezafe constructions in Persian written texts. At the first stage, the rule-based model was applied to tag some tokens of an input sentence. Then, in the second stage, the search capabilities of the genetic algorithm were used to assign the Ezafe tag to untagged tokens using the previously captured training information. The proposed framework was evaluated on Peykareh corpus and it achieved 95.26 percent accuracy. Test results show that this proposed approach outperformed other approaches for recognizing the position of Ezafe constructions. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-29T12:38:18Z No. of bitstreams: 1 ijimai20142_6_2_pdf_23890.pdf: 1022351 bytes, checksum: adce857ffe59b0d727675e7c6aef3966 (MD5); Made available in DSpace on 2020-01-29T12:38:18Z (GMT). No. of bitstreams: 1 ijimai20142_6_2_pdf_23890.pdf: 1022351 bytes, checksum: adce857ffe59b0d727675e7c6aef3966 (MD5)

Business and Social Behaviour Intelligence Analysis Using PSO

Business and Social Behaviour Intelligence Analysis Using PSO Bhaskar, Vinay S; Kumar Singh, Abhishek; Dhruw, Jyoti; Parashar, Anubha; Sharma, Mradula The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. The paper introduces the decision making model which is based on the application of Artiﬁcial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) algorithm. Essentially the business spatial data illustrate the group behaviors. The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data: enterprises maintain customer address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and self-descriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behaviour. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-29T11:31:37Z No. of bitstreams: 1 ijimai20142_6_8_pdf_20898.pdf: 500686 bytes, checksum: dd75df878763aab9007877f9adc1fe92 (MD5); Made available in DSpace on 2020-01-29T11:31:37Z (GMT). No. of bitstreams: 1 ijimai20142_6_8_pdf_20898.pdf: 500686 bytes, checksum: dd75df878763aab9007877f9adc1fe92 (MD5)

Editor’s Note

Editor’s Note Herrera Viedma, Enrique The International Journal of Interactive Multimedia and Artificial Intelligence provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence and Interactive Multimedia techniques. The research works presented in this issue are based on various topics of interest, among which are included: 3D image reconstruction, Persian texts, usability evaluation methods, user experience, oriented matroids, flexible job-shop scheduling, business and social behavior, mobile computing and mobile devices, intelligent tutoring systems and geography optimization. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-29T11:17:00Z No. of bitstreams: 1 ijimai20142_6_0_pdf_17943.pdf: 142578 bytes, checksum: a845a9cb401e7a8c74764c9a489fbb94 (MD5); Made available in DSpace on 2020-01-29T11:17:00Z (GMT). No. of bitstreams: 1 ijimai20142_6_0_pdf_17943.pdf: 142578 bytes, checksum: a845a9cb401e7a8c74764c9a489fbb94 (MD5)

A REST Service for Triangulation of Point Sets Using Oriented Matroids

A REST Service for Triangulation of Point Sets Using Oriented Matroids Valero-Medina, José Antonio; Lizarazo-Salcedo, Ivan This paper describes the implementation of a prototype REST service for triangulation of point sets collected by mobile GPS receivers. The first objective of this paper is to test functionalities of an application, which exploits mobile devices’ capabilities to get data associated with their spatial location. A triangulation of a set of points provides a mechanism through which it is possible to produce an accurate representation of spatial data. Such triangulation may be used for representing surfaces by Triangulated Irregular Networks (TINs), and for decomposing complex two-dimensional spatial objects into simpler geometries. The second objective of this paper is to promote the use of oriented matroids for finding alternative solutions to spatial data processing and analysis tasks. This study focused on the particular case of the calculation of triangulations based on oriented matroids. The prototype described in this paper used a wrapper to integrate and expose several tools previously implemented in C++. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-29T10:58:04Z No. of bitstreams: 1 ijimai20142_6_4_pdf_14209.pdf: 1037055 bytes, checksum: 1db110570191aced8d8e7a7d12571831 (MD5); Made available in DSpace on 2020-01-29T10:58:04Z (GMT). No. of bitstreams: 1 ijimai20142_6_4_pdf_14209.pdf: 1037055 bytes, checksum: 1db110570191aced8d8e7a7d12571831 (MD5)

Usability Evaluation Methods for Special Interest Internet Information Services

Usability Evaluation Methods for Special Interest Internet Information Services Schön, Eva-Maria; Hellmers, Jens; Thomaschewski, Jörg The internet provides a wide range of scientific information for different areas of research, used by the related scientific communities. Often the design or architecture of these web pages does not correspond to the mental model of their users. As a result the wanted information is difficult to find. Methods established by Usability Engineering and User Experience can help to increase the appeal of scientific internet information services by analyzing the users’ requirements. This paper describes a procedure to analyze and optimize scientific internet information services that can be accomplished with relatively low effort. It consists of a combination of methods that already have been successfully applied to practice: Personas, usability inspections, Online Questionnaire, Kano model and Web Analytics. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-28T13:18:47Z No. of bitstreams: 1 ijimai20142_6_3_pdf_14574.pdf: 471429 bytes, checksum: fe1b2e9b8823bf8aac9525ca52949402 (MD5); Made available in DSpace on 2020-01-28T13:18:47Z (GMT). No. of bitstreams: 1 ijimai20142_6_3_pdf_14574.pdf: 471429 bytes, checksum: fe1b2e9b8823bf8aac9525ca52949402 (MD5)

An Entrepreneurial Well-being Model based on GEM Data for Spain

An Entrepreneurial Well-being Model based on GEM Data for Spain Saiz-Alvarez, José Manuel; Coduras Martínez, Alicia; Cuervo-Arango Martínez, Carlos The Economics of Happiness is one of the research areas of greatest growth in recent years. Throughout this work, a venture based model in which satisfaction of Spanish entrepreneurs with their professional life is performed. We analyze the responses of 9,989 entrepreneurs using data from the Global Entrepreneurship Monitor (GEM), and six hypothesis are discussed. The results show that, for the Spanish case, there is a strong consistency in the results the opportunity entrepreneurs present greater satisfaction than necessity entrepreneurs. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-28T12:24:38Z No. of bitstreams: 1 ijimai20132_5_5_pdf_26415.pdf: 734577 bytes, checksum: cad303065c5a58d7d0013607dfdb11d3 (MD5); Made available in DSpace on 2020-01-28T12:24:38Z (GMT). No. of bitstreams: 1 ijimai20132_5_5_pdf_26415.pdf: 734577 bytes, checksum: cad303065c5a58d7d0013607dfdb11d3 (MD5)

Editor’s Note

Editor’s Note Mochón, Francisco; Rojas, Mariano The science of happiness is trans-disciplinary. Happiness is an experience human beings have and, in consequence, its understanding calls for knowledge from all disciplines which, in one way or the other, deal with all facets of human lives. Various disciplines have contributed to the development of the science of happiness; among them: Psychology, Sociology, Economics, Psychiatry and Neuroscience. Because happiness research deals with human being of flesh and blood, it requires high-level techniques to dealing with large information sets in order to extract that information which is relevant. In the study of happiness there are many observations –as many as persons in the world-, there are many variables, and there are many interrelations and synergies to take account of. In consequence, happiness research benefits from sophisticated models that allow for a better understanding of people’s happiness; without losing contact with what real human beings experience, it is important to use techniques that allow researchers to process all the information reaching for valuable conclusions. It is with this purpose that Computer Science has joined the other disciplines providing its calculation powerful tools to advance the study of happiness. It is therefore appropriate that The International Journal of Interactive Multimedia and Artificial Intelligence has decided to launch a special issue on happiness showing some of the potential contributions the discipline can make to happiness research. The research works presented in this issue cover various topics of interest, all related to potential contributions from Computer Science to the understanding of happiness and subjective well-being. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-28T11:27:14Z No. of bitstreams: 1 ijimai20132_5_0_pdf_20298.pdf: 143992 bytes, checksum: 68be0e0461e0b2aeffb7439210d69008 (MD5); Made available in DSpace on 2020-01-28T11:27:14Z (GMT). No. of bitstreams: 1 ijimai20132_5_0_pdf_20298.pdf: 143992 bytes, checksum: 68be0e0461e0b2aeffb7439210d69008 (MD5)

Intelligent Position Aware Mobile Services for Seamless and Non-Intrusive Clocking-in

Intelligent Position Aware Mobile Services for Seamless and Non-Intrusive Clocking-in Rios-Aguilar, Sergio This paper analyzes the viability of the use of employees smartphones as a valid tool for companies in order to conduct presence control. A Mobile Location Aware Information System is also proposed for a non intrusive Presence Control using exclusively terminal-based reactive location technologies, meeting cost minimization, and universal access criteria. The focus is providing trust to the employees, so that they feel safe and in control of when the location data is gathered while satisfying the control needs of the employer. LAMS platform is a state-of-the-art framework for synchronous mobile location-aware content personalization, using A-GPS terminal-based/network assisted mobile positioning techniques and UAProf data processing at the origin server. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-28T11:07:00Z No. of bitstreams: 1 ijimai20132_5_6_pdf_10811.pdf: 521182 bytes, checksum: a0c934acf542b0227769e7af305ac6eb (MD5); Made available in DSpace on 2020-01-28T11:07:00Z (GMT). No. of bitstreams: 1 ijimai20132_5_6_pdf_10811.pdf: 521182 bytes, checksum: a0c934acf542b0227769e7af305ac6eb (MD5)

A System for Personality and Happiness Detection

A System for Personality and Happiness Detection Saez, Yago; Navarro, Carlos; Mochon, Asuncion; Isasi, Pedro This work proposes a platform for estimating personality and happiness. Starting from Eysenck's theory about human's personality, authors seek to provide a platform for collecting text messages from social media (Whatsapp), and classifying them into different personality categories. Although there is not a clear link between personality features and happiness, some correlations between them could be found in the future. In this work, we describe the platform developed, and as a proof of concept, we have used different sources of messages to see if common machine learning algorithms can be used for classifying different personality features and happiness. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-28T10:19:18Z No. of bitstreams: 1 ijimai20132_5_1_pdf_10736.pdf: 903987 bytes, checksum: c2fae33d288e83086fe9aff294791d19 (MD5); Made available in DSpace on 2020-01-28T10:19:18Z (GMT). No. of bitstreams: 1 ijimai20132_5_1_pdf_10736.pdf: 903987 bytes, checksum: c2fae33d288e83086fe9aff294791d19 (MD5)

A First Approach to the Implicit Measurement of Happiness in Latin America Through the Use of Social Networks

A First Approach to the Implicit Measurement of Happiness in Latin America Through the Use of Social Networks Mochón, Francisco; Sanjuán Martínez, Óscar This research paper can be classified as pertaining to the group of empirical studies that try to measure subjective well-being. The article presents as its greatest contributions the use of a subjective measurement of well-being based on social networks for the Latin American setting, as well as its comparative analysis with another traditional method. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-28T10:03:13Z No. of bitstreams: 1 ijimai20132_5_2_pdf_14275.pdf: 523892 bytes, checksum: 9d82f03d9c264d5e2c3813631f27a9ea (MD5); Made available in DSpace on 2020-01-28T10:03:13Z (GMT). No. of bitstreams: 1 ijimai20132_5_2_pdf_14275.pdf: 523892 bytes, checksum: 9d82f03d9c264d5e2c3813631f27a9ea (MD5)

Happiness and Human Relations: The Role of Materialistic Values. An ABM Illustration

Happiness and Human Relations: The Role of Materialistic Values. An ABM Illustration Rojas, Mariano; Ibarra-López, Ignacio This paper argues that a person’s happiness must be understood as a phenomenon that emerges not only from her individual condition but also from her place in society. Understanding that a person is socially immersed implies giving a greater role to social interactions and social structure. The paper presents a simple model to take into consideration the role of human relations. An agent-based model (ABM) is used to illustrate the implementation of the model in understanding people’s happiness. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-27T13:13:51Z No. of bitstreams: 1 ijimai20132_5_3_pdf_38475.pdf: 460799 bytes, checksum: 5ad705b4692720d51f4a6ebd59ada3b9 (MD5); Made available in DSpace on 2020-01-27T13:13:51Z (GMT). No. of bitstreams: 1 ijimai20132_5_3_pdf_38475.pdf: 460799 bytes, checksum: 5ad705b4692720d51f4a6ebd59ada3b9 (MD5)

Graph-based Techniques for Topic Classification of Tweets in Spanish

Graph-based Techniques for Topic Classification of Tweets in Spanish Cordobés, Héctor; Fernández Anta, Antonio; Chiroque, Luis F.; Pérez, Fernando; Redondo, Teófilo; Santos, Agustín Topic classification of texts is one of the most interesting challenges in Natural Language Processing (NLP). Topic classifiers commonly use a bag-of-words approach, in which the classifier uses (and is trained with) selected terms from the input texts. In this work we present techniques based on graph similarity to classify short texts by topic. In our classifier we build graphs from the input texts, and then use properties of these graphs to classify them. We have tested the resulting algorithm by classifying Twitter messages in Spanish among a predefined set of topics, achieving more than 70% accuracy. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-27T12:59:10Z No. of bitstreams: 1 ijimai20132_5_4_pdf_17528.pdf: 604080 bytes, checksum: 8cad73f207270bccbcad22c5635250c8 (MD5); Made available in DSpace on 2020-01-27T12:59:10Z (GMT). No. of bitstreams: 1 ijimai20132_5_4_pdf_17528.pdf: 604080 bytes, checksum: 8cad73f207270bccbcad22c5635250c8 (MD5)

Gamification, Social Networks and Sustainable Environments

Gamification, Social Networks and Sustainable Environments Silva, Fábio; Analide, Cesar; Rosa, Luís; Felgueiras, Gilberto; Pimenta, Cedric Intelligent environments and ambient intelligence enabled systems provide means to gather rich information from both environments and its users. With the help of such systems, it is possible to foster communities of ambient intelligence systems with community driven knowledge, which is created by individual actions and setups in each of the environments. Such arrangements provides the potential to build systems that promote better practices and more efficient and sustainable environments by promoting the community best examples and engaging users to adopt and develop proactive behaviors to improve their standings in the community. This work aims to use knowledge from communities of intelligent environments to their own benefit. The approach presented in this work uses information from different environments, ranking them according to their sustainability assessment. Recommendations are then computed using similarity and clustering functions ranking users and environments, updating their previous records and launching new recommendations in the process. Gamification concepts are used in order to keep users motivation and engage them actively to produce better results in terms of sustainability Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-27T09:54:50Z No. of bitstreams: 1 ijimai20132_4_6_pdf_29055.pdf: 763270 bytes, checksum: 133c26d72738347b5e1d5bce804689f4 (MD5); Made available in DSpace on 2020-01-27T09:54:50Z (GMT). No. of bitstreams: 1 ijimai20132_4_6_pdf_29055.pdf: 763270 bytes, checksum: 133c26d72738347b5e1d5bce804689f4 (MD5)

CAIN-21: Automatic adaptation decisions and extensibility in an MPEG-21 adaptation engine

CAIN-21: Automatic adaptation decisions and extensibility in an MPEG-21 adaptation engine López, Fernando; Martínez, José M.; García, Narciso This paper presents the progress and final state of CAIN-21, an extensible and metadata driven multimedia adaptation in the MPEG-21 framework. CAIN-21 facilitates the integration of pluggable multimedia adaptation tools, automatically chooses the chain of adaptations to perform and manages its execution. To drive the adaptation, it uses the description tools and implied ontology established by MPEG-21. The paper not only describes the evolution and latest version of CAIN-21, but also identifies limitations and ambiguities in the description capabilities of MPEG-21. Therefore, it proposes some extensions to the MPEG-21 description schema for removing these problems. Finally, the pros and cons of CAIN-21 with respect to other multimedia adaptation engines are discussed. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-27T09:42:53Z No. of bitstreams: 1 ijimai20132_4_1_pdf_14413.pdf: 803250 bytes, checksum: 038f125af5dd81f47566f34a30b22c78 (MD5); Made available in DSpace on 2020-01-27T09:42:53Z (GMT). No. of bitstreams: 1 ijimai20132_4_1_pdf_14413.pdf: 803250 bytes, checksum: 038f125af5dd81f47566f34a30b22c78 (MD5)

Application of Hybrid Agents to Smart Energy Management of a Prosumer Node

Application of Hybrid Agents to Smart Energy Management of a Prosumer Node Caianiello, Pasquale; Costantini, Stefania; De Gasperis, Giovanni; Gimenez De Lorenzo, Mario We outline a solution to the problem of intelligent control of energy consumption of a smart building system by a prosumer planning agent that acts on the base of the knowledge of the system state and of a prediction of future states. Predictions are obtained by using a synthetic model of the system as obtained with a machine learning approach. We present case studies simulations implementing different instantiations of agents that control an air conditioner according to temperature set points dynamically chosen by the user. The agents are able of energy saving while trying to keep indoor temperature within a given comfort interval. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-27T09:08:37Z No. of bitstreams: 1 ijimai20132_4_7_pdf_88975.pdf: 772462 bytes, checksum: 3e137d9c073947633e749e80174f767b (MD5); Made available in DSpace on 2020-01-27T09:08:37Z (GMT). No. of bitstreams: 1 ijimai20132_4_7_pdf_88975.pdf: 772462 bytes, checksum: 3e137d9c073947633e749e80174f767b (MD5)

An Agent-Based Approach for Evaluating Basic Design Options of Management Accounting Systems

An Agent-Based Approach for Evaluating Basic Design Options of Management Accounting Systems Wall, Friederike This paper investigates the effectiveness of reducing errors in management accounting systems with respect to organizational performance. In particular, different basic design options of management accounting systems of how to improve the information base by measurements of actual values are analyzed in different organizational contexts. The paper applies an agent-based simulation based on the idea of NK fitness landscapes. The results provide broad, but no universal support for conventional wisdom that lower inaccuracies of accounting information lead to more effective adaptation processes. Furthermore, results indicate that the effectiveness of improving the management accounting system subtly interferes with the complexity of the interactions within the organization and the coordination mode applied Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-27T08:44:26Z No. of bitstreams: 1 ijimai20132_4_2_pdf_27067.pdf: 639169 bytes, checksum: 75fecb441bb583b99a8d44258dddd061 (MD5); Made available in DSpace on 2020-01-27T08:44:26Z (GMT). No. of bitstreams: 1 ijimai20132_4_2_pdf_27067.pdf: 639169 bytes, checksum: 75fecb441bb583b99a8d44258dddd061 (MD5)

A Constraint-Based Model for Fast Post-Disaster Emergency Vehicle Routing

A Constraint-Based Model for Fast Post-Disaster Emergency Vehicle Routing Amadini, Roberto; Sefrioui, Imane; Mauro, Jacopo; Gabbrielli, Maurizio Disasters like terrorist attacks, earthquakes, hurricanes, and volcano eruptions are usually unpredictable events that affect a high number of people. We propose an approach that could be used as a decision support tool for a post-disaster response that allows the assignment of victims to hospitals and organizes their transportation via emergency vehicles. By exploiting the synergy between Mixed Integer Programming and Constraint Programming techniques, we are able to compute the routing of the vehicles so as to rescue much more victims than both heuristic based and complete approaches in a very reasonable time. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-24T12:32:28Z No. of bitstreams: 1 ijimai20132_4_8_pdf_21464.pdf: 659507 bytes, checksum: 953d29d8f5cb0fc7212c0e5631833ffa (MD5); Made available in DSpace on 2020-01-24T12:32:28Z (GMT). No. of bitstreams: 1 ijimai20132_4_8_pdf_21464.pdf: 659507 bytes, checksum: 953d29d8f5cb0fc7212c0e5631833ffa (MD5)

Editor’s Note

Editor’s Note González-Crespo, Rubén The research works presented in this issue are based on various topics of interest, among which are included: Mobile services, mpeg, agent-based Simulation, complexity, management accounting systems, animal-drawn vehicles, traffic and transport, possibility theory, precautionary saving, MAS, ambient intelligence, gamification, sustainable environments, disaster recovery, DSS, constraint programming and ICT. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-24T11:22:44Z No. of bitstreams: 1 ijimai20132_4_0_pdf_77052.pdf: 209295 bytes, checksum: 7f6a8f747ee1b86ee7b77bdc5a191041 (MD5); Made available in DSpace on 2020-01-24T11:22:44Z (GMT). No. of bitstreams: 1 ijimai20132_4_0_pdf_77052.pdf: 209295 bytes, checksum: 7f6a8f747ee1b86ee7b77bdc5a191041 (MD5)

Reality and perspectives of a model for the population that obtains its income with the use of an animal-drawn vehicle in the city of Bogota

Reality and perspectives of a model for the population that obtains its income with the use of an animal-drawn vehicle in the city of Bogota Sánchez Aparicio, Ismael Fernando; Romero Villalobos, Oswaldo Alberto This paper analyzes the structure of the data collected in the population dependent or receives its revenues in the use of animal-drawn vehicle, to extract an economic model for the development of this activity (which is currently done with these vehicles and is unbusinesslike) introducing formal parameters, as well as replacement of the vehicle analyzes the development of this activity in this population. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-22T13:27:13Z No. of bitstreams: 1 ijimai20132_4_3_pdf_70525.pdf: 439909 bytes, checksum: abd6cae4bd54f8e1fe1e70a218966088 (MD5); Made available in DSpace on 2020-01-22T13:27:13Z (GMT). No. of bitstreams: 1 ijimai20132_4_3_pdf_70525.pdf: 439909 bytes, checksum: abd6cae4bd54f8e1fe1e70a218966088 (MD5)

Global Collective Intelligence in Technological Societies: as a result of Collaborative Knowledge in combination with Artificial Intelligence

Global Collective Intelligence in Technological Societies: as a result of Collaborative Knowledge in combination with Artificial Intelligence Piedra Calderón, Juan Carlos; Rainer, J. Javier The big influence of Information and Communication Technologies (ICT), especially in area of construction of Technological Societies has generated big social changes. That is visible in the way of relating to people in different environments. These changes have the possibility to expand the frontiers of knowledge through sharing and cooperation. That has meaning the inherently creation of a new form of Collaborative Knowledge. The potential of this Collaborative Knowledge has been given through ICT in combination with Artificial Intelligence processes, from where is obtained a Collective Knowledge. When this kind of knowledge is shared, it gives the place to the Global Collective Intelligence. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-22T13:13:29Z No. of bitstreams: 1 ijimai20132_4_9_pdf_23958.pdf: 396670 bytes, checksum: 43e4a5a34bc1cd687287449fa2aa0716 (MD5); Made available in DSpace on 2020-01-22T13:13:29Z (GMT). No. of bitstreams: 1 ijimai20132_4_9_pdf_23958.pdf: 396670 bytes, checksum: 43e4a5a34bc1cd687287449fa2aa0716 (MD5)

Connecting possibilistic prudence and optimal saving

Connecting possibilistic prudence and optimal saving Lucia Casademunt, Ana María; Georgescu, Irina In this paper we study the optimal saving problem in the framework of possibility theory. The notion of possibilistic precautionary saving is introduced as a measure of the way the presence of possibilistic risk (represented by a fuzzy number) influences a consumer in establishing the level of optimal saving. The notion of prudence of an agent in the face of possibilistic risk is defined and the equivalence between the prudence condition and a positive possibilistic precautionary saving is proved. Some relations between possibilistic risk aversion, prudence and possibilistic precautionary saving were established. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-22T12:24:44Z No. of bitstreams: 1 ijimai20132_4_4_pdf_10239.pdf: 483479 bytes, checksum: 5f42e1eaaaf4ba6f8d7cb74371aba750 (MD5); Made available in DSpace on 2020-01-22T12:24:44Z (GMT). No. of bitstreams: 1 ijimai20132_4_4_pdf_10239.pdf: 483479 bytes, checksum: 5f42e1eaaaf4ba6f8d7cb74371aba750 (MD5)

PLANE: A Platform for Negotiation of Multiattribute Multimedia Objects

PLANE: A Platform for Negotiation of Multiattribute Multimedia Objects Guedes, Rharon M. This work proposes the definition of a system to negotiate products in an e-commerce scenario. This negotiation system is defined as PLANE – Platform to Assist Negotiation – and it is carried in a semi-automatic way, using multi-attributes functions, based on attributes of the negotiated content. It also presents an architecture to interconnect the participant through an inter-network in the television broadcasters context. Each participant of the inter-network applies policies for its own contents, and all of them must comply these policies. If a participant needs a content not covered by the policies, it is possible to start a negotiation process for this specific content. Experiments present a simulation scenario where PLANE assists the negotiation between three sellers and one buyer with predefined negotiation profiles. Results demonstrated the success of the system in approximate the negotiator after some few interactions, reducing time and cost. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-22T11:03:51Z No. of bitstreams: 1 ijimai20132_4_10_pdf_13842.pdf: 564845 bytes, checksum: 9dd3b52b8cb2d80dec6b3e08d1ef7f35 (MD5); Made available in DSpace on 2020-01-22T11:03:51Z (GMT). No. of bitstreams: 1 ijimai20132_4_10_pdf_13842.pdf: 564845 bytes, checksum: 9dd3b52b8cb2d80dec6b3e08d1ef7f35 (MD5)

Analysis of Bullying in Cooperative Multi-agent Systems’ Communications

Analysis of Bullying in Cooperative Multi-agent Systems’ Communications Gutiérrez, Celia Cooperative Multi-agent Systems frameworks do not include modules to test communications yet. The proposed framework incorporates robust analysis tools using IDKAnalysis2.0 to evaluate bullying effect in communications. The present work is based on ICARO-T. This platform follows the Adaptive Multi-agent Systems paradigm. Experimentation with ICARO-T includes two deployments: the equitative and the authoritative. Results confirm the usefulness of the analysis tools when exporting to Cooperative Multi-agent Systems that use different configurations. Besides, ICARO-T is provided with new functionality by a set of tools for communication analysis. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-22T10:36:40Z No. of bitstreams: 1 ijimai20132_4_5_pdf_10514.pdf: 497306 bytes, checksum: b6d2860a8d8bf1acf62522f1d74e21a1 (MD5); Made available in DSpace on 2020-01-22T10:36:40Z (GMT). No. of bitstreams: 1 ijimai20132_4_5_pdf_10514.pdf: 497306 bytes, checksum: b6d2860a8d8bf1acf62522f1d74e21a1 (MD5)

An Agent-Based Approach for Data Fusion in Homeland Security

An Agent-Based Approach for Data Fusion in Homeland Security Castillo Chamorro, José Miguel; de Solís Montes, D. Rafael This article presents an agent-based solution for data fusion in Homeland Security. Communication technology has been developed very fast in the last decades. We can get lots of data in milliseconds. Our current problem is to process such amounts of data in order to provide useful information. We have to focus our effort on developing intelligent information systems able to handle big amounts of data extracting or revealing relations among data and able to produce information easily understandable for the human user. That is the case of data fusion in tactical operations, especially in the field of defense and Homeland security. Our research is focused on obtaining a Multi-agent system able to inference future enemy’s actions or behaviors from data received from heterogeneous sensors. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-22T09:47:15Z No. of bitstreams: 1 ijimai2013_3_6_pdf_21581 (1).pdf: 400153 bytes, checksum: 9ab17b229ac651105187c523c47afc9c (MD5); Made available in DSpace on 2020-01-22T09:47:15Z (GMT). No. of bitstreams: 1 ijimai2013_3_6_pdf_21581 (1).pdf: 400153 bytes, checksum: 9ab17b229ac651105187c523c47afc9c (MD5)

An Example in Remote Computing Over the Internet applied to Geometry

An Example in Remote Computing Over the Internet applied to Geometry Ferreira, M.; Casquilho, M. Scientific computing over the Internet can suit many activities that have not, in the authors’ opinion, been explored enough in general. Resources such as executables, languages, packages, can be used from a remote computing system. In this study, largely based on academic practice, a simple illustrative example in Geometry is implemented on a distributed system that outsources the computing-intensive tasks to remote servers that may be located in other universities or companies, linked to grids and clusters and so on. The software stack and software developed to support the communication is explained in detail. The architecture developed stresses the interoperability of the software, and a suitable high degree of decoupling between components hosted in various locations. The results of this study motivate further work and serve a practical purpose that may be useful to everyone doing scientific computing. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-22T09:04:36Z No. of bitstreams: 1 ijimai20132_3_5_pdf_30080.pdf: 593504 bytes, checksum: 3095e0fcdce4f792a946d87dc7e55a6c (MD5); Made available in DSpace on 2020-01-22T09:04:36Z (GMT). No. of bitstreams: 1 ijimai20132_3_5_pdf_30080.pdf: 593504 bytes, checksum: 3095e0fcdce4f792a946d87dc7e55a6c (MD5)

Improvements in the native development environment for Sony AIBO

Improvements in the native development environment for Sony AIBO Kertész, Csaba — The entertainment robotics have been on a peak with AIBO, but this robot brand has been discontinued by the Sony in 2006 to help its financial position. Among other reasons, the robot failed to enter into both the mainstream and the robotics research labs besides the RoboCup competitions, however, there were some attempts to use the robot for rehabilitation and emotional medical treatments. A native software development environment (Open-R SDK) was provided to program AIBO, nevertheless, the operating system (Aperios) induced difficulties for the students and the researchers in the software development. The author of this paper made efforts to update the Open-R and overcome the problems. More enhancements have been implemented in the core components, some software methodologies were applied to solve a number of restrictions and the achievements are summarized here. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-21T13:21:02Z No. of bitstreams: 1 ijimai20132_3_7_pdf_45805.pdf: 490831 bytes, checksum: cf59694446f04121f8e0da13d8190524 (MD5); Made available in DSpace on 2020-01-21T13:21:02Z (GMT). No. of bitstreams: 1 ijimai20132_3_7_pdf_45805.pdf: 490831 bytes, checksum: cf59694446f04121f8e0da13d8190524 (MD5)

Analysis of Log File Data to Understand Mobile Service Context and Usage Patterns

Analysis of Log File Data to Understand Mobile Service Context and Usage Patterns Klein, Bernhard; Pretel, Ivan; Vanhecke, Sacha; Lago, Ana B.; Lopez-de-Ipiña, Diego Several mobile acceptance models exist today that focus on user interface handling and usage frequency evaluation. Since mobile applications reach much deeper into everyday life, it is however important to better consider user behaviour for the service evaluation. In this paper we introduce the Behaviour Assessment Model (BAM), which is designed to gaining insights about how well services enable, enhance and replace human activities. More specifically, the basic columns of the evaluation framework concentrate on (1) service actuation in relation to the current user context, (2) the balance between service usage effort and benefit, and (3) the degree to which community knowledge can be exploited. The evaluation is guided by a process model that specifies individual steps of data capturing, aggregation, and final assessment. The BAM helps to gain stronger insights regarding characteristic usage hotspots, frequent usage patterns, and leveraging of networking effects showing more realistically the strengths and weaknesses of mobile services. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-21T12:41:30Z No. of bitstreams: 1 ijimai20132_3_2_pdf_26595.pdf: 951402 bytes, checksum: 653524b7c6b5411ec401b30e147c9fe3 (MD5); Made available in DSpace on 2020-01-21T12:41:30Z (GMT). No. of bitstreams: 1 ijimai20132_3_2_pdf_26595.pdf: 951402 bytes, checksum: 653524b7c6b5411ec401b30e147c9fe3 (MD5)

Editor’s Note

Editor’s Note González-Crespo, Rubén The research works presented in this issue are based on various topics of interest, among wich are included: Mobile services, gesture recognition, physics simulation, management decision support, business intelligence, Internet, remote executables, scientific computing, university-industry links, Sony AIBO, Aperios, toolchain, MAS, data fusion, tracks, merge, inference, Homeland Security, european projects, research trends, emerging technologies and desk research. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-21T11:59:03Z No. of bitstreams: 1 ijimai20132_3_0_pdf_35445 (1).pdf: 201216 bytes, checksum: 33f3617d15c560d854a082ee96bd7bdf (MD5); Made available in DSpace on 2020-01-21T11:59:03Z (GMT). No. of bitstreams: 1 ijimai20132_3_0_pdf_35445 (1).pdf: 201216 bytes, checksum: 33f3617d15c560d854a082ee96bd7bdf (MD5)

A Fuzzy Group Prioritization Method for Deriving Weights and its Software Implementation

A Fuzzy Group Prioritization Method for Deriving Weights and its Software Implementation Almulhim, Tarifa; Mikhailov, Ludmil; Xu, Dong-Ling Several Multi-Criteria Decision Making (MCDM) methods involve pairwise comparisons to obtain the preferences of decision makers (DMs). This paper proposes a fuzzy group prioritization method for deriving group priorities/weights from fuzzy pairwise comparison matrices. The proposed method extends the Fuzzy Preferences Programming Method (FPP) by considering the different importance weights of multiple DMs . The elements of the group pairwise comparison matrices are presented as fuzzy numbers rather than exact numerical values, in order to model the uncertainty and imprecision in the DMs’ judgments. Unlike the known fuzzy prioritization techniques, the proposed method is able to derive crisp weights from incomplete and fuzzy set of comparison judgments and does not require additional aggregation procedures. A prototype of a decision tool is developed to assist DMs to implement the proposed method for solving fuzzy group prioritization problems in MATLAB. Detailed numerical examples are used to illustrate the proposed approach. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-20T12:18:46Z No. of bitstreams: 1 ijimai20132_3_1_pdf_27045.pdf: 619707 bytes, checksum: 8da6b79e2e524f5a761b8db51585f8c8 (MD5); Made available in DSpace on 2020-01-20T12:18:46Z (GMT). No. of bitstreams: 1 ijimai20132_3_1_pdf_27045.pdf: 619707 bytes, checksum: 8da6b79e2e524f5a761b8db51585f8c8 (MD5)

SketchyDynamics: A Library for the Development of Physics Simulation Applications with Sketch-Based Interfaces

SketchyDynamics: A Library for the Development of Physics Simulation Applications with Sketch-Based Interfaces Costa, Abílio; Pereira, João P. Sketch-based interfaces provide a powerful, natural and intuitive way for users to interact with an application. By combining a sketch-based interface with a physically simulated environment, an application offers the means for users to rapidly sketch a set of objects, like if they are doing it on piece of paper, and see how these objects behave in a simulation. In this paper we present SketchyDynamics, a library that intends to facilitate the creation of applications by rapidly providing them a sketch-based interface and physics simulation capabilities. SketchyDynamics was designed to be versatile and customizable but also simple. In fact, a simple application where the user draws objects and they are immediately simulated, colliding with each other and reacting to the specified physical forces, can be created with only 3 lines of code. In order to validate SketchyDynamics design choices, we also present some details of the usability evaluation that was conducted with a proof-of-concept prototype. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-17T13:29:36Z No. of bitstreams: 1 ijimai20132_3_3_pdf_15923.pdf: 729546 bytes, checksum: 47405c6f448728a54c3248802d3d74a5 (MD5); Made available in DSpace on 2020-01-17T13:29:36Z (GMT). No. of bitstreams: 1 ijimai20132_3_3_pdf_15923.pdf: 729546 bytes, checksum: 47405c6f448728a54c3248802d3d74a5 (MD5)

Emerging Technologies Landscape on Education. A review

Emerging Technologies Landscape on Education. A review de-la-Fuente-Valentín, Luis; Carrasco, Aurora; Konya, Kinga; Burgos, Daniel This paper presents a desk research that analysed available recent studies in the field of Technology Enhanced Learning. The desk research is focused on work produced in the frame of FP6 and FP7 European programs, in the area of Information and Communication Technologies. It concentrates in technologies that support existing forms of learning, and also in technologies that enhance new learning paradigms. This approach includes already adopted and successfully piloted technologies. The elaboration of the desk research had three main parts: firstly, the collection of documents from CORDIS and other institutions related to TEL research; secondly, the identification of relevant terms appearing in those documents and the elaboration of a thesaurus; and thirdly, a quantitative analysis of each term occurrences. Many of the identified technologies belong to the fields of interactive multimedia, Human-computer Interaction and-or related to recommendation and learning analytics. This study becomes a thorough review of the current state of these fields through the actual development of R&D European projects. This research, will be used as a basis to better understand the evolution of the sector, and to focus future research efforts on these sectors and their application to education. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-17T13:19:43Z No. of bitstreams: 1 ijimai20132_3_8_pdf_20133.pdf: 778597 bytes, checksum: 13f2ec7d9bd9bcd178977d9c0fd8d80e (MD5); Made available in DSpace on 2020-01-17T13:19:43Z (GMT). No. of bitstreams: 1 ijimai20132_3_8_pdf_20133.pdf: 778597 bytes, checksum: 13f2ec7d9bd9bcd178977d9c0fd8d80e (MD5)

From Management Information Systems to Business Intelligence: The Development of Management Information Needs

From Management Information Systems to Business Intelligence: The Development of Management Information Needs Skyrius, Rimvydas; Kazakevičienė, Gėlytė; Bujauskas, Vytautas Despite the advances in IT, information systems intended for management informing did not uniformly fulfil the increased expectations of users; this can be said mostly about complex information needs. Although some of the technologies for supporting complicated insights, like management decision support systems and technologies, experienced reduction in interest both from researchers and practitioners, this did not reduce the importance of well-supported business informing and decision making. Being attributed to the group of intelligent systems and technologies, decision support (DS) technologies have been largely supplemented by business intelligence (BI) technologies. Both types of technologies are supported by respective information technologies, which often appear to be quite closely related. The objective of this paper is to define relations between simple and complex informing intended to satisfy different sets of needs and provided by different sets of support tools. The paper attempts to put together decision support and business intelligence technologies, based on common goals of sense-making and use of advanced analytical tools. A model of two interconnected cycles has been developed to relate the activities of decision support and business intelligence. Empirical data from earlier research is used to direct possible further insights into this area. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-17T13:11:17Z No. of bitstreams: 1 ijimai20132_3_4_pdf_28905.pdf: 649090 bytes, checksum: 4b6eb1581a086af0e0dbb0b966b4399d (MD5); Made available in DSpace on 2020-01-17T13:11:17Z (GMT). No. of bitstreams: 1 ijimai20132_3_4_pdf_28905.pdf: 649090 bytes, checksum: 4b6eb1581a086af0e0dbb0b966b4399d (MD5)

Infected Fruit Part Detection using K-Means Clustering Segmentation Technique

Infected Fruit Part Detection using K-Means Clustering Segmentation Technique Dubey, Shiv Ram; Dixit, Pushkar; Singh, Nishant; Gupta, Jay Prakash Nowadays, overseas commerce has increased drastically in many countries. Plenty fruits are imported from the other nations such as oranges, apples etc. Manual identification of defected fruit is very time consuming. This work presents a novel defect segmentation of fruits based on color features with K-means clustering unsupervised algorithm. We used color images of fruits for defect segmentation. Defect segmentation is carried out into two stages. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished. Then the clustered blocks are merged to a specific number of regions. Using this two step procedure, it is possible to increase the computational efficiency avoiding feature extraction for every pixel in the image of fruits. Although the color is not commonly used for defect segmentation, it produces a high discriminative power for different regions of image. This approach thus provides a feasible robust solution for defect segmentation of fruits. We have taken apple as a case study and evaluated the proposed approach using defected apples. The experimental results clarify the effectiveness of proposed approach to improve the defect segmentation quality in aspects of precision and computational time. The simulation results reveal that the proposed approach is promising. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-17T11:21:04Z No. of bitstreams: 1 ijimai20132_2_9_pdf_10021.pdf: 1256571 bytes, checksum: b4f5729f8f84575efd4bc81ced79646c (MD5); Made available in DSpace on 2020-01-17T11:21:04Z (GMT). No. of bitstreams: 1 ijimai20132_2_9_pdf_10021.pdf: 1256571 bytes, checksum: b4f5729f8f84575efd4bc81ced79646c (MD5)

Gade4all: Developing Multi-platform Videogames based on Domain Specific Languages and Model Driven Engineering

Gade4all: Developing Multi-platform Videogames based on Domain Specific Languages and Model Driven Engineering Nuñez-Valdez, Edward Rolando; Sanjuan, Oscar; Pelayo García-Bustelo, B. Cristina; Cueva-Lovelle, Juan Manuel; Infante Hernandez, Guillermo The development of applications for mobile devices is a constantly growing market which and more and more enterprises support the development of applications for this kind of devices. In that sense, videogames for mobile devices have become very popular worldwide and are now part of highly profitable and competitive industry. Due to the diversity of platforms and mobile devices and the complexity of this kind of applications, the development time and the number of errors within that development process have increased. The productivity of the developers has also decreased due to the necessity of using many programming languages in the development process. One of the most popular strategies is to employ specialized people to perform the development tasks more efficiently, but this involves an increase of the costs, which makes some applications economically unviable. In this article we present the Gade4all Project, consisting in a new platform that aims to facilitate the development of videogames and entertainment software through the use of Domain Specific Languages and Model Driven Engineering. This tool makes possible for users without previous knowledge in the field of software development to create 2D videogames for multiplatform mobile devices in a simple and innovative way. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-17T10:12:32Z No. of bitstreams: 1 ijimai20132_2_4_pdf_13724.pdf: 1124804 bytes, checksum: 42c1a17d5d3dd1007f116266ffe68f3b (MD5); Made available in DSpace on 2020-01-17T10:12:32Z (GMT). No. of bitstreams: 1 ijimai20132_2_4_pdf_13724.pdf: 1124804 bytes, checksum: 42c1a17d5d3dd1007f116266ffe68f3b (MD5)

L.I.M.E. A recommendation model for informal and formal learning, engaged

L.I.M.E. A recommendation model for informal and formal learning, engaged Burgos, Daniel In current eLearning models and implementations (e.g. Learning Management Systems-LMS) there is a lack of engagement between formal and informal activities. Furthermore, the online methodology focuses on a standard set of units of learning and learning objects, along with pre-defined tests, and collateral resources like, i.e. discussion fora and message wall. They miss the huge potential of learning via the interlacement of social networks, LMS and external sources. Thanks to user behaviour, user interaction, and personalised counselling by a tutor, learning performance can be improved. We design and develop an adaptation eLearning model for restricted social networks, which supports this approach. In addition, we build an eLearning module that implements this conceptual model in a real application case, and present the preliminary analysis and positive results. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-17T09:59:14Z No. of bitstreams: 1 ijimai20132_2_11_pdf_25682.pdf: 515885 bytes, checksum: eda40e33ca8bc589def6d6d702f3e885 (MD5); Made available in DSpace on 2020-01-17T09:59:14Z (GMT). No. of bitstreams: 1 ijimai20132_2_11_pdf_25682.pdf: 515885 bytes, checksum: eda40e33ca8bc589def6d6d702f3e885 (MD5)

Smart Indoor Positioning/Location and Navigation: A Lightweight Approach

Smart Indoor Positioning/Location and Navigation: A Lightweight Approach Puértolas Montañés, José Antonio; Mendoza Rodríguez, Adriana; Sanz Prieto, Iván In this paper a new location indoor system is presented, which shows the position and orientation of the user in closed environments, as well as the optimal route to his destination through location tags. This system is called Labelee, and it makes easier the interaction between users and devices through QR code scanning or by NFC tag reading, because this technology is increasingly common in the latest smartphones. With this system, users could locate themselves into an enclosure with less interaction. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-14T13:39:09Z No. of bitstreams: 1 ijimai20132_2_5_pdf_27975.pdf: 594010 bytes, checksum: 5e485c4551c769c593af581be94a247c (MD5); Made available in DSpace on 2020-01-14T13:39:09Z (GMT). No. of bitstreams: 1 ijimai20132_2_5_pdf_27975.pdf: 594010 bytes, checksum: 5e485c4551c769c593af581be94a247c (MD5)

Legal Issues Concerning P2P Exchange of Educational Materials and Their Impact on E-Learning Multi-Agent Systems

Legal Issues Concerning P2P Exchange of Educational Materials and Their Impact on E-Learning Multi-Agent Systems Gil, Eugenio; Castillo Sanz, Andrés G The last years have known an impressive change in the use of technologies for the sharing and dissemination of knowledge, thus affecting deeply all the traditional means used by education in all its shapes and levels. This transformation has not been fully understood by the society at large for its immense impacts and its short life. This paper describes in the question emerging from the clash of the rights to education in a wide sense and the rights derived from authorship and how that issue is affecting the design of e-learning multi-agent tools. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-14T13:22:01Z No. of bitstreams: 1 ijimai20132_2_10_pdf_42425.pdf: 275910 bytes, checksum: 09c662d513e3e7692830c48911628245 (MD5); Made available in DSpace on 2020-01-14T13:22:01Z (GMT). No. of bitstreams: 1 ijimai20132_2_10_pdf_42425.pdf: 275910 bytes, checksum: 09c662d513e3e7692830c48911628245 (MD5)

Open Data as a key factor for developing expert systems: a perspective from Spain

Open Data as a key factor for developing expert systems: a perspective from Spain Rodríguez Rojas, Luz Andrea; Cueva Lovelle, Juan Manuel; Tarazona Bermúdez, Giovanny Mauricio; Montenegro, Carlos Enrique The open data movement is relatively new but very significant, and potentially powerful. The overall intention is to make local, regional and national data available in a form that allows for direct manipulation. This paper is based on analyzing the current context of the Open Data initiative in Spain, from its origins and concepts, the legal framework, current initiatives and challenges that must be addressed for effective reuse of public sector information. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-14T13:07:17Z No. of bitstreams: 1 ijimai20132_2_6_pdf_21720.pdf: 586542 bytes, checksum: ae1eb7fec884b3aa4ff3d9b635501493 (MD5); Made available in DSpace on 2020-01-14T13:07:17Z (GMT). No. of bitstreams: 1 ijimai20132_2_6_pdf_21720.pdf: 586542 bytes, checksum: ae1eb7fec884b3aa4ff3d9b635501493 (MD5)

Annotation and Visualization in Android: An Application for Education and Real Time Information

Annotation and Visualization in Android: An Application for Education and Real Time Information Barahona Neri, Renato; Millán García, Gustavo; Bolibar Barón, Holman D. By using Augmented Reality applications, users can get more information while interacting with real objects. The popularity of the Smartphones and the ubiquity of an Internet connection within modern devices, offer the best combination for these kind of applications, which can pull content from heterogeneous sources. The goal with this work is to show the architecture and a basic implementation of a prototype for an AR application that displays information (opinions) about physical places as comments overlaid to the place left there by other users, but that also encourage in-situ content creation for collaboration. These applications can also be used in order to improve the interaction between students and physical places, getting facts, or associating quizzes to a specific location; tourism guides, promotions of products, just to mention a few. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-14T12:13:57Z No. of bitstreams: 1 ijimai20132_2_1_pdf_10239.pdf: 623066 bytes, checksum: 0c57cc136e318e18b428d9ce1d534e26 (MD5); Made available in DSpace on 2020-01-14T12:13:57Z (GMT). No. of bitstreams: 1 ijimai20132_2_1_pdf_10239.pdf: 623066 bytes, checksum: 0c57cc136e318e18b428d9ce1d534e26 (MD5)

Editor’s Note

Editor’s Note Pascual Espada, Jordán; González-Crespo, Rubén The International Journal of Interactive Multimedia and Artificial Intelligence provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence and Interactive Multimedia techniques. The research works presented in this issue are based on various topics of interest, among wich are included: AI for Software Engineering, Education, Computer Vision, Augmented Reality, Natural Language Understanding, Data Mining, Knowledge-Based/Expert Systems, Image Processing, Location systems, Internet of Things, Multiobjective Evolutionary Algorithms and Software architectures. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-14T11:48:28Z No. of bitstreams: 1 ijimai20132_2_0_pdf_50315.pdf: 177394 bytes, checksum: a52e397a86e8d1b0af5ba244f1cafd3c (MD5); Made available in DSpace on 2020-01-14T11:48:28Z (GMT). No. of bitstreams: 1 ijimai20132_2_0_pdf_50315.pdf: 177394 bytes, checksum: a52e397a86e8d1b0af5ba244f1cafd3c (MD5)

Using Rules to Adapt Applications for Business Models with High Evolutionary Rates

Using Rules to Adapt Applications for Business Models with High Evolutionary Rates Juan Fuente, A. A; López Pérez, B.; Infante Hernandez, Guillermo; Cases Fernández, L. J. Nowadays, business models are in permanent evolution since the requirements belongs to a rapidly evolving world. In a context where communications all around the world travel so fast the business models need to be adapted permanently to the information the managers receive. In such world, traditional software development, needed for adapting software to changes, do not work properly since business changes need to be in exploitation in shorter times. In that situation, it is needed to go quicker from the business idea to the exploitation environment. This issue can be solved accelerating the development speed: from the expert to the customer, with no –or few, technical intervention. This paper proposes an approach to empower domain experts in developing adaptability solutions by using automated sets of production rules in a friendly way. Furthermore, a use case that implements this kind of development was used in a real problem prototype. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-14T11:18:26Z No. of bitstreams: 1 ijimai20132_2_7_pdf_31819.pdf: 715234 bytes, checksum: 2aeec9fee275c7dcb5463e39e0be9d2b (MD5); Made available in DSpace on 2020-01-14T11:18:26Z (GMT). No. of bitstreams: 1 ijimai20132_2_7_pdf_31819.pdf: 715234 bytes, checksum: 2aeec9fee275c7dcb5463e39e0be9d2b (MD5)

BPLOM: BPM Level-Oriented Methodology for Incremental Business Process Modeling and Code Generation on Mobile Platforms

BPLOM: BPM Level-Oriented Methodology for Incremental Business Process Modeling and Code Generation on Mobile Platforms Solís-Martínez, Jaime; García-Menéndez, Natalia; Pelayo García-Bustelo, B. Cristina; Cueva Lovelle, Juan Manuel The requirements engineering phase is the departure point for the development process of any kind of computer application, it determines the functionality needed in the working scenario of the program. Although this is a crucial point in application development, as incorrect requirement definition leads to costly error appearance in later stages of the development process, application domain experts’ implication remains minor. In order to correct this scenario, business process modeling notations were introduced to favor business expert implication in this phase, but notation complexity prevents this participation to reach its ideal state. Hence, we promote the definition of a level oriented business process methodology, which encourages the adaptation of the modeling notation to the modeling and technical knowledge shown by the expert. This approach reduces the complexity found by domain experts and enables them to model their processes completely with a level of technical detail directly proportional to their knowledge. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-14T10:51:32Z No. of bitstreams: 1 ijimai20132_2_2_pdf_13432.pdf: 1148438 bytes, checksum: 894a5235edc033f8979b0d4dc89e4644 (MD5); Made available in DSpace on 2020-01-14T10:51:32Z (GMT). No. of bitstreams: 1 ijimai20132_2_2_pdf_13432.pdf: 1148438 bytes, checksum: 894a5235edc033f8979b0d4dc89e4644 (MD5)

Application of Multiobjective Evolutionary Techniques for Robust Portfolio Optimization

Application of Multiobjective Evolutionary Techniques for Robust Portfolio Optimization Garcia Rodriguez, Sandra On December 20 of 2012 Sandra García Rodríguez defended his PhD at Carlos III of Madrid (Spain), called: “Application of Multiobjective Techniques for Robust Portfolio Optimization”. This thesis was supervised by Dr. David Quintana Montero and Dr. Inés M. Galván León. The defense was done in a publicly open presentation held at Carlos III University of Madrid. The PhD was approved, with the highest rating Cum Laude, by the examining committee: Dr. José Manuel Molina López, Dr. Antonio Gaspar Lopes da Cunha and Dr. David Camacho Fernández. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-14T10:09:02Z No. of bitstreams: 1 ijimai20132_2_8_pdf_12330.pdf: 185101 bytes, checksum: b9cdd62f4178e94fbd30c7d38e33e484 (MD5); Made available in DSpace on 2020-01-14T10:09:02Z (GMT). No. of bitstreams: 1 ijimai20132_2_8_pdf_12330.pdf: 185101 bytes, checksum: b9cdd62f4178e94fbd30c7d38e33e484 (MD5)

Kuruma: The Vehicle Automatic Data Capture for Urban Computing Collaborative Systems

Kuruma: The Vehicle Automatic Data Capture for Urban Computing Collaborative Systems Cueva-Fernandez, Guillermo; Pascual Espada, Jordán; García-Díaz, Vicente; GonzalezRodriguez, Martin Smartphones can provide coverage in large areas all around the world and with the availability of powerful operating systems they can become solid sensing infrastructures. In fact, static sensors are hard to deploy and maintain while modern mobile devices include many sensors that can be used to sense and benefit from collaborative communities. This project tries to improve urban computing by developing a framework able to create monitoring applications for mobile devices, focusing on obtaining the highest degree of interoperability between sensors. A prototype application has been developed to demonstrate the feasibility of creating multidisciplinary applications with several different approaches. The application developed consists of a Road Roughness Information System that measures smoothness and detects irregularities on the roads. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-14T09:56:46Z No. of bitstreams: 1 ijimai20132_2_3_pdf_31219.pdf: 522063 bytes, checksum: 19b681746248e16b4a6e2b0519f5bd91 (MD5); Made available in DSpace on 2020-01-14T09:56:46Z (GMT). No. of bitstreams: 1 ijimai20132_2_3_pdf_31219.pdf: 522063 bytes, checksum: 19b681746248e16b4a6e2b0519f5bd91 (MD5)

Improving the family orientation process in Cuban Special Schools trough Nearest Prototype classification

Improving the family orientation process in Cuban Special Schools trough Nearest Prototype classification Villuendas-Rey, Y.; Rey-Benguría, C.; Caballero-Mota, Y.; García-Lorenzo, M. M. Cuban Schools for children with Affective – Behavioral Maladies (SABM) have as goal to accomplish a major change in children behavior, to insert them effectively into society. One of the key elements in this objective is to give an adequate orientation to the children’s families; due to the family is one of the most important educational contexts in which the children will develop their personality. The family orientation process in SABM involves clustering and classification of mixed type data with non-symmetric similarity functions. To improve this process, this paper includes some novel characteristics in clustering and prototype selection. The proposed approach uses a hierarchical clustering based on compact sets, making it suitable for dealing with non-symmetric similarity functions, as well as with mixed and incomplete data. The proposal obtains very good results on the SABM data, and over repository databases. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-14T09:39:14Z No. of bitstreams: 1 ijimai20132_12_pdf_27060.pdf: 684085 bytes, checksum: 4f11365e352782f254af5c7910e4adf1 (MD5); Made available in DSpace on 2020-01-14T09:39:14Z (GMT). No. of bitstreams: 1 ijimai20132_12_pdf_27060.pdf: 684085 bytes, checksum: 4f11365e352782f254af5c7910e4adf1 (MD5)

A multi-agent system model to integrate Virtual Learning Environments and Intelligent Tutoring Systems

A multi-agent system model to integrate Virtual Learning Environments and Intelligent Tutoring Systems Giuffra, P.; Cecilia, E.; Silveira, Ricardo A. Virtual learning environments (VLEs) are used in distance learning and classroom teaching as teachers and students support tools in the teaching–learning process, where teachers can provide material, activities and assessments for students. However, this process is done in the same way for all the students, regardless of their differences in performance and behavior in the environment. The purpose of this work is to develop an agent-based intelligent learning environment model inspired by intelligent tutoring to provide adaptability to distributed VLEs, using Moodle as a case study and taking into account student's performance on tasks and activities proposed by the teacher, as well as monitoring his/her study material access. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-14T09:22:42Z No. of bitstreams: 1 ijimai20132_17_pdf_59225.pdf: 456824 bytes, checksum: 2bd2d95a159948c04d52b80739eeed70 (MD5); Made available in DSpace on 2020-01-14T09:22:42Z (GMT). No. of bitstreams: 1 ijimai20132_17_pdf_59225.pdf: 456824 bytes, checksum: 2bd2d95a159948c04d52b80739eeed70 (MD5)

Engineering Education through eLearning technology in Spain

Engineering Education through eLearning technology in Spain Fernández Rodríguez, Juan Carlos; Rainer Granados, José Javier; Miralles Muñoz, Fernando eLearning kind of education is stirring up all the disciplines in the academic circles, especially since it provides an access to educational areas that are uneasy and traditionally in-person, such as Engineering. Even though it had an outbreak in some of the most prestigious American universities, eLearning has being a reality in Spain for some years now, changing educational and teaching habits. To ensure a proper education is not an easy task with it comes to engineering fields, therefore this article shows an update on the works developed on this issue and the technologies they used. In this report it is given a perspective of the intimate relationship between the eLearning method of learning and the studies of Engineering in Spain, through the TIC development and the current educational legislation. In this regard, teaching examples are given on several subjects of different engineering studies, emphasizing the good results obtained in the abovementioned experiences. Below here is a evaluation on the results obtained in the analyzed studies. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-14T08:38:35Z No. of bitstreams: 1 ijimai20132_16_pdf_25489.pdf: 398839 bytes, checksum: 4af5ec01ddbd3dba3711e5f606162db3 (MD5); Made available in DSpace on 2020-01-14T08:38:35Z (GMT). No. of bitstreams: 1 ijimai20132_16_pdf_25489.pdf: 398839 bytes, checksum: 4af5ec01ddbd3dba3711e5f606162db3 (MD5)

Confidentiality of 2D Code using Infrared with Cell-level Error Correction

Confidentiality of 2D Code using Infrared with Cell-level Error Correction Teraura, Nobuyuki; Sakurai, Kouichi Optical information media printed on paper use printing materials to absorb visible light. There is a 2D code, which may be encrypted but also can possibly be copied. Hence, we envisage an information medium that cannot possibly be copied and thereby offers high security. At the surface, the normal 2D code is printed. The inner layers consist of 2D codes printed using a variety of materials, which absorb certain distinct wavelengths, to form a multilayered 2D code. Information can be distributed among the 2D codes forming the inner layers of the multiplex. Additionally, error correction at cell level can be introduced Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-13T13:49:08Z No. of bitstreams: 1 ijimai20132_13_pdf_21937.pdf: 652063 bytes, checksum: 613e715c7cced021a5ee1b2b275ed559 (MD5); Made available in DSpace on 2020-01-13T13:49:08Z (GMT). No. of bitstreams: 1 ijimai20132_13_pdf_21937.pdf: 652063 bytes, checksum: 613e715c7cced021a5ee1b2b275ed559 (MD5)

GLOA: A New Job Scheduling Algorithm for Grid Computing

GLOA: A New Job Scheduling Algorithm for Grid Computing Pooranian, Zahra; Shojafar, Mohammad; Abawajy, Jemal H.; Singhal, Mukesh The purpose of grid computing is to produce a virtual supercomputer by using free resources available through widespread networks such as the Internet. This resource distribution, changes in resource availability, and an unreliable communication infrastructure pose a major challenge for efficient resource allocation. Because of the geographical spread of resources and their distributed management, grid scheduling is considered to be a NP-complete problem. It has been shown that evolutionary algorithms offer good performance for grid scheduling. This article uses a new evaluation (distributed) algorithm inspired by the effect of leaders in social groups, the group leaders' optimization algorithm (GLOA), to solve the problem of scheduling independent tasks in a grid computing system. Simulation results comparing GLOA with several other evaluation algorithms show that GLOA produces shorter makespans. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-13T13:41:07Z No. of bitstreams: 1 ijimai20132_18_pdf_62825.pdf: 458448 bytes, checksum: 4bc123b76419e205118dc3fb9f68587f (MD5); Made available in DSpace on 2020-01-13T13:41:07Z (GMT). No. of bitstreams: 1 ijimai20132_18_pdf_62825.pdf: 458448 bytes, checksum: 4bc123b76419e205118dc3fb9f68587f (MD5)

A Case-based Reasoning Approach to Validate Grammatical Gender and Number Agreement in Spanish language

A Case-based Reasoning Approach to Validate Grammatical Gender and Number Agreement in Spanish language Bacca, Jorge; Baldiris, Silvia; Fabregat, Ramón; Avila, Cecilia Across Latin America 420 indigenous languages are spoken. Spanish is considered a second language in indigenous communities and is progressively introduced in education. However, most of the tools to support teaching processes of a second language have been developed for the most common languages such as English, French, German, Italian, etc. As a result, only a small amount of learning objects and authoring tools have been developed for indigenous people considering the specific needs of their population. This paper introduces Multilingual–Tiny as a web authoring tool to support the virtual experience of indigenous students and teachers when they are creating learning objects in indigenous languages or in Spanish language, in particular, when they have to deal with the grammatical structures of Spanish. Multilingual–Tiny has a module based on the Case-based Reasoning technique to provide recommendations in real time when teachers and students write texts in Spanish. An experiment was performed in order to compare some local similarity functions to retrieve cases from the case library taking into account the grammatical structures. As a result we found the similarity function with the best performance. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-08T13:23:18Z No. of bitstreams: 1 ijimai20132_110_pdf_86555.pdf: 732536 bytes, checksum: 72ecf61f80bb2e0cf7464821b10dd3da (MD5); Made available in DSpace on 2020-01-08T13:23:18Z (GMT). No. of bitstreams: 1 ijimai20132_110_pdf_86555.pdf: 732536 bytes, checksum: 72ecf61f80bb2e0cf7464821b10dd3da (MD5)

Efficient Measurement of the User Experience of Interactive Products. How to use the User Experience Questionnaire (UEQ).Example: Spanish Language Version

Efficient Measurement of the User Experience of Interactive Products. How to use the User Experience Questionnaire (UEQ).Example: Spanish Language Version Rauschenberger, Maria; Schrepp, Martin; Pérez Cota, Manuel; Olschner, Siegfried; Thomaschewski, Jörg Developer, manager and user feedback is needed to optimize products. Besides the basic Software qualities – usability and user experience are important properties for improving your product. Usability is well known and can be tested with e.g. a usability test or an expert review. In contrast user experience describes the whole impact a product has on the end-user. The timeline goes from before, while and after the use of a product. We present a tool that allows you to evaluate the user experience of a product with little effort. Furthermore the tool is available in different languages and we are using the new Spanish Version. We show how this tool can be used for a continuous user experience assessment. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-08T12:58:52Z No. of bitstreams: 1 ijimai20132_15_pdf_35685.pdf: 549269 bytes, checksum: bd038e076a6e7892d59b56a5d6bdd383 (MD5); Made available in DSpace on 2020-01-08T12:58:52Z (GMT). No. of bitstreams: 1 ijimai20132_15_pdf_35685.pdf: 549269 bytes, checksum: bd038e076a6e7892d59b56a5d6bdd383 (MD5)

BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile

BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile Rodríguez, Paula A.; Tabares, Valentina; Duque, Néstor D.; Ovalle, Demetrio A.; Vicari, Rosa M. Learning Objects (LOs) are distinguished from traditional educational resources for their easy and quickly availability through Web-based repositories, from which they are accessed through their metadata. In addition, having a user profile allows an educational recommender system to help the learner to find the most relevant LOs based on their needs and preferences. The aim of this paper is to propose an agent-based model so-called BROA to recommend relevant LOs recovered from Repository Federations as well as LOs adapted to learner profile. The model proposed uses both role and service models of GAIA methodology, and the analysis models of the MAS-CommonKADS methodology. A prototype was built based on this model and validated to obtain some assessing results that are finally presented. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2020-01-08T12:37:26Z No. of bitstreams: 1 ijimai20132_11_pdf_51705.pdf: 715963 bytes, checksum: a6b224b37d9cffb3bf5f42ed648b5a42 (MD5); Made available in DSpace on 2020-01-08T12:37:26Z (GMT). No. of bitstreams: 1 ijimai20132_11_pdf_51705.pdf: 715963 bytes, checksum: a6b224b37d9cffb3bf5f42ed648b5a42 (MD5)

Editor’s Note

Editor’s Note Boff, Elisa; Pavón, Juan This special issue “Artificial Intelligence and Social Application” includes extended versions of selected papers from Artificial Intelligence and Education area of the 13th edition of the Ibero-American Conference on Artificial Intelligence, held in Cartagena de Indias - Colombia, November, 2012. The issue includes, thus, five selected papers, describing innovative research work, on Artificial Intelligence in Education area including, among others: Recommender Systems, Learning Objects, Intelligent Tutoring Systems, Multi-Agent Systems, Virtual Learning Environments, Case-based reasoning and Classifiers Algorithms. This issue also includes six papers in the Interactive Multimedia and Artificial Intelligence areas, dealing with subjects such as User Experience, E-Learning, Communication Tools, Multi-Agent Systems, Grid Computing. IBERAMIA 2012 was the 13th edition of the Ibero-American Conference on Artificial Intelligence, a leading symposium where the Ibero-American AI community comes together to share research results and experiences with researchers in Artificial Intelligence from all over the world. The papers were organized in topical sections on knowledge representation and reasoning, information and knowledge processing, knowledge discovery and data mining, machine learning, bio-inspired computing, fuzzy systems, modelling and simulation, ambient intelligence, multi-agent systems, human-computer interaction, natural language processing, computer vision and robotics, planning and scheduling, AI in education, and knowledge engineering and applications. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-20T11:12:56Z No. of bitstreams: 1 ijimai20132_10_pdf_34715.pdf: 91641 bytes, checksum: f2fe50d868723ec36002320acee0b861 (MD5); Made available in DSpace on 2019-12-20T11:12:56Z (GMT). No. of bitstreams: 1 ijimai20132_10_pdf_34715.pdf: 91641 bytes, checksum: f2fe50d868723ec36002320acee0b861 (MD5)

An Analysis Architecture for Communications in Multi-agent Systems

An Analysis Architecture for Communications in Multi-agent Systems Gutiérrez, Celia Evaluation tools are significant from the Agent Oriented Software Engineering (AOSE) point of view. Defective designs of communications in Multi-agent Systems (MAS) may overload one or several agents, causing a bullying effect on them. Bullying communications have avoidable consequences, as high response times and low quality of service (QoS). Architectures that perform evaluation functionality must include features to measure the bullying activity and QoS, but it is also recommendable that they have reusability and scalability features. Evaluation tools with these features can be applied to a wide range of MAS, while minimizing designer’s effort. This work describes the design of an architecture for communication analysis, and its evolution to a modular version, that can be applied to different types of MAS. Experimentation of both versions shows differences between its executions. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-20T09:59:54Z No. of bitstreams: 1 ijimai20132_19_pdf_25820.pdf: 612127 bytes, checksum: a565b560123a134419e8ce56798b60a7 (MD5); Made available in DSpace on 2019-12-20T09:59:54Z (GMT). No. of bitstreams: 1 ijimai20132_19_pdf_25820.pdf: 612127 bytes, checksum: a565b560123a134419e8ce56798b60a7 (MD5)

Mining Social and Affective Data for Recommendation of Student Tutors

Mining Social and Affective Data for Recommendation of Student Tutors Boff, Elisa; Reategui, Eliseo Berni This paper presents a learning environment where a mining algorithm is used to learn patterns of interaction with the user and to represent these patterns in a scheme called item descriptors. The learning environment keeps theoretical information about subjects, as well as tools and exercises where the student can put into practice the knowledge gained. One of the main purposes of the project is to stimulate collaborative learning through the interaction of students with different levels of knowledge. The students' actions, as well as their interactions, are monitored by the system and used to find patterns that can guide the search for students that may play the role of a tutor. Such patterns are found with a particular learning algorithm and represented in item descriptors. The paper presents the educational environment, the representation mechanism and learning algorithm used to mine social-affective data in order to create a recommendation model of tutors. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-20T09:32:53Z No. of bitstreams: 1 ijimai20132_14_pdf_15364.pdf: 477069 bytes, checksum: 8d241549cfffc61b2a8a462edc885d1a (MD5); Made available in DSpace on 2019-12-20T09:32:53Z (GMT). No. of bitstreams: 1 ijimai20132_14_pdf_15364.pdf: 477069 bytes, checksum: 8d241549cfffc61b2a8a462edc885d1a (MD5)

Model Innovation of Process Based on the Standard e-commerce International GS1

Model Innovation of Process Based on the Standard e-commerce International GS1 Tarazona Bermúdez, Giovanny Mauricio; Rodríguez Rojas, Luz Andrea; B Pelayo, Cristina; Sanjuán Martínez, Óscar This article focuses on the design and characterization of management model for MSMEs, based on e-commerce and the GS1 international e-com standard. The first part contextualizes electronic commerce and its impact on domestic industry, and briefly describes the B2B e-commerce model used in Colombia. Subsequently the first step to apply the model is presented, which corresponds to the design of a diagnostic methodology that evaluates the technological, technical, commercial and administrative aspects of the organization; after that are exposed the results of the pilot experiment performed on a MSME from Bogota, and finally will be explained the procedures for the implementation of the model. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-12T13:31:07Z No. of bitstreams: 1 ijimai20121_7_8_pdf_11649.pdf: 381484 bytes, checksum: c0416111cf85f368ff3a6b5fa7ba6494 (MD5); Made available in DSpace on 2019-12-12T13:31:07Z (GMT). No. of bitstreams: 1 ijimai20121_7_8_pdf_11649.pdf: 381484 bytes, checksum: c0416111cf85f368ff3a6b5fa7ba6494 (MD5)

Dynamic, ecological, accessible and 3D Virtual Worlds-based Libraries using OpenSim and Sloodle along with mobile location and NFC for checking in

Dynamic, ecological, accessible and 3D Virtual Worlds-based Libraries using OpenSim and Sloodle along with mobile location and NFC for checking in González-Crespo, Rubén; Ríos Aguilar, Sergio; Ferro Escobar, Roberto; Torres, Nicolás This paper proposes the implementation of a 3D virtual library, using open platforms such as OpenSimulator and Sloodle, applied to the integration of virtual learning environments. It also proposes their application to the creation of open libraries to share and disseminate the new dynamic nature of knowledge, in the understanding that 3D virtual worlds may contribute to the future of libraries as part of green initiatives to achieve an ecologic and sustainable planet. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-12T13:17:38Z No. of bitstreams: 1 ijimai20121_7_7_pdf_23525.pdf: 760714 bytes, checksum: b957846a538dac535ecbd5167244d79f (MD5); Made available in DSpace on 2019-12-12T13:17:38Z (GMT). No. of bitstreams: 1 ijimai20121_7_7_pdf_23525.pdf: 760714 bytes, checksum: b957846a538dac535ecbd5167244d79f (MD5)

Semantic Brokering of Multimedia Contents for Smart Delivery of Ubiquitous Services in Pervasive Environments

Semantic Brokering of Multimedia Contents for Smart Delivery of Ubiquitous Services in Pervasive Environments Amato, Alba; Di Martino, Beniamino; Venticinque, Salvatore With the proliferation of modern mobile devices having the capability to interact each other and with the environment in a transparent manner, there is an increase in the development of those applications that are specifically designed for pervasive and ubiquitous environments. Those applications are able to provide a service of interest for the user that depends on context information, such as the user's position, his preferences, the capability of the device and its available resources. Services have to respond in a rational way in many different situations choosing the actions with the best expected result by the user, so making environment not only more connected and efficient, but smarter. Here we present a semantic framework that provides the technology for the development of intelligent, context aware services and their delivery in pervasive and ubiquitous environments. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-12T12:48:22Z No. of bitstreams: 1 ijimai20121_7_2_pdf_22967.pdf: 860053 bytes, checksum: 7170114d11070d1de75b7c00165e22d9 (MD5); Made available in DSpace on 2019-12-12T12:48:22Z (GMT). No. of bitstreams: 1 ijimai20121_7_2_pdf_22967.pdf: 860053 bytes, checksum: 7170114d11070d1de75b7c00165e22d9 (MD5)

A Client-Server System for Ubiquitous Video Service

A Client-Server System for Ubiquitous Video Service Nossenson, Ronit; Yudilevich, Orit; Marlowitz, Omer In this work we introduce a simple client-server system architecture and algorithms for ubiquitous live video and VOD service support. The main features of the system are: efficient usage of network resources, emphasis on user personalization, and ease of implementation. The system supports many continuous service requirements such as QoS provision, user mobility between networks and between different communication devices, and simultaneous usage of a device by a number of users. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-11T11:14:30Z No. of bitstreams: 1 ijimai20121_7_4_pdf_22432.pdf: 962763 bytes, checksum: ec74e3c44d9329085d85a87adbc55bc3 (MD5); Made available in DSpace on 2019-12-11T11:14:30Z (GMT). No. of bitstreams: 1 ijimai20121_7_4_pdf_22432.pdf: 962763 bytes, checksum: ec74e3c44d9329085d85a87adbc55bc3 (MD5)

Service Orchestration on the Internet of Things

Service Orchestration on the Internet of Things Pascual Espada, Jordán On July 27, 2010, Jordán Pascual Espada defended his Master’s thesis at Oviedo University (Spain), titled: “Service Orchestration on the internet of things”. This Master’s thesis is the final part of the Web Engineering Official Research Master belonging to the European Higher Education Area. Jordán Pascual Espada defended his dissertation in a publicly open presentation held in the School of Computer Engineering at Oviedo University, and was able to comment on every question raised by his committee and the audience. The master’s thesis was supervised by his advisors, Juan Manuel Cueva Lovelle and Oscar Sanjuán Martínez. The thesis has been read and approved by his thesis committee, receiving the highest rating. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-11T11:01:35Z No. of bitstreams: 1 ijimai20121_7_9_pdf_13854.pdf: 217432 bytes, checksum: 3cd959db29c7a60d940c6fec97c9bf90 (MD5); Made available in DSpace on 2019-12-11T11:01:35Z (GMT). No. of bitstreams: 1 ijimai20121_7_9_pdf_13854.pdf: 217432 bytes, checksum: 3cd959db29c7a60d940c6fec97c9bf90 (MD5)

Automatic Multimedia Creation Enriched with Dynamic Conceptual Data

Automatic Multimedia Creation Enriched with Dynamic Conceptual Data Martín, Angel; Iribas, Haritz; Alberdi, Ion; Aginako, Naiara There is a growing gap between the multimedia production and the context centric multimedia services. The main problem is the under-exploitation of the content creation design. The idea is to support dynamic content generation adapted to the user or display profile. Our work is an implementation of a web platform for automatic generation of multimedia presentations based on SMIL (Synchronized Multimedia Integration Language) standard. The system is able to produce rich media with dynamic multimedia content retrieved automatically from different content databases matching the semantic context. For this purpose, we extend the standard interpretation of SMIL tags in order to accomplish a semantic translation of multimedia objects in database queries. This permits services to take benefit of production process to create customized content enhanced with real time information fed from databases. The described system has been successfully deployed to create advanced context centric weather forecasts. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-11T10:55:35Z No. of bitstreams: 1 ijimai20121_7_5_pdf_13895.pdf: 604126 bytes, checksum: 92746e2b80edd25adfdd923f5f172361 (MD5); Made available in DSpace on 2019-12-11T10:55:35Z (GMT). No. of bitstreams: 1 ijimai20121_7_5_pdf_13895.pdf: 604126 bytes, checksum: 92746e2b80edd25adfdd923f5f172361 (MD5)

Artificial intelligence methodologies applied to quality control of the positioning services offered by the Red Andaluza de Posicionamiento (RAP) network

Artificial intelligence methodologies applied to quality control of the positioning services offered by the Red Andaluza de Posicionamiento (RAP) network Giménez de Ory, Elena; Gil, Antonio J. On April 26, 2012, Elena Giménez de Ory defend-ed her Ph.D. thesis at University of Jaén, entitled: “Robust methodologies applied to quality control of the positioning services offered by the Red Andaluza de Posicionamiento (RAP) network”. Elena Giménez de Ory defended her dissertation in a publicly open presentation held in the Higher Polytechnic School at the University of Jaén, and was able to comment on every question raised by her thesis committee and the audience. The thesis was supervised by her advisor, Prof. Antonio J. Gil Cruz, and the rest of his thesis committee, Prof. Manuel Sánchez de la Orden, Dr. Antonio Miguel Ruiz Armenteros and Dr. Gracia Rodríguez Caderot. The thesis has been read and approved by his thesis committee, receiving the highest rating. All of them were present at the presentation. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-11T10:38:18Z No. of bitstreams: 1 ijimai20121_7_10_pdf_41850.pdf: 285488 bytes, checksum: 75b11ef1be9a35e29bd9a867573cd7ed (MD5); Made available in DSpace on 2019-12-11T10:38:18Z (GMT). No. of bitstreams: 1 ijimai20121_7_10_pdf_41850.pdf: 285488 bytes, checksum: 75b11ef1be9a35e29bd9a867573cd7ed (MD5)

Accessing Wireless Sensor Networks Via Dynamically Reconfigurable Interaction Models

Accessing Wireless Sensor Networks Via Dynamically Reconfigurable Interaction Models Gomes, Maria Cecília; Paulino, Hervé; Baptista, Adérito; Araújo, Filipe The Wireless Sensor Networks (WSNs) technology is already perceived as fundamental for science across many domains, since it provides a low cost solution for environment monitoring. WSNs representation via the service concept and its inclusion in Web environments, e.g. through Web services, supports particularly their open/standard access and integration. Although such Web enabled WSNs simplify data access, network parameterization and aggregation, the existing interaction models and run-time adaptation mechanisms available to clients are still scarce. Nevertheless, applications increasingly demand richer and more flexible accesses besides the traditional client/server. For instance, applications may require a streaming model in order to avoid sequential data requests, or the asynchronous notification of subscribed data through the publish/subscriber. Moreover, the possibility to automatically switch between such models at runtime allows applications to define flexible context-based data acquisition. To this extent, this paper discusses the relevance of the session and pattern abstractions on the design of a middleware prototype providing richer and dynamically reconfigurable interaction models to Web enabled WSNs. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-05T13:17:41Z No. of bitstreams: 1 ijimai20121_7_6_pdf_26241.pdf: 1013143 bytes, checksum: 8d8582d0358b2e1a2090c9c4c4d370e2 (MD5); Made available in DSpace on 2019-12-05T13:17:41Z (GMT). No. of bitstreams: 1 ijimai20121_7_6_pdf_26241.pdf: 1013143 bytes, checksum: 8d8582d0358b2e1a2090c9c4c4d370e2 (MD5)

A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm

A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm Khan, Koffka; Sahai, Ashok Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO) is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-05T13:03:25Z No. of bitstreams: 1 ijimai20121_7_3_pdf_25064.pdf: 495834 bytes, checksum: 1373b185bd1090d1b3aa35fc50b0ef39 (MD5); Made available in DSpace on 2019-12-05T13:03:25Z (GMT). No. of bitstreams: 1 ijimai20121_7_3_pdf_25064.pdf: 495834 bytes, checksum: 1373b185bd1090d1b3aa35fc50b0ef39 (MD5)

Framework for Computation Offloading in Mobile Cloud Computing

Framework for Computation Offloading in Mobile Cloud Computing Kovachev, Dejan; Klamma, Ralf The inherently limited processing power and battery lifetime of mobile phones hinder the possible execution of computationally intensive applications like content-based video analysis or 3D modeling. Offloading of computationally intensive application parts from the mobile platform into a remote cloud infrastructure or nearby idle computers addresses this problem. This paper presents our Mobile Augmentation Cloud Services (MACS) middleware which enables adaptive extension of Android application execution from a mobile client into the cloud. Applications are developed by using the standard Android development pattern. The middleware does the heavy lifting of adaptive application partitioning, resource monitoring and computation offloading. These elastic mobile applications can run as usual mobile application, but they can also use remote computing resources transparently. Two prototype applications using the MACS middleware demonstrate the benefits of the approach. The evaluation shows that applications, which involve costly computations, can benefit from offloading with around 95% energy savings and significant performance gains compared to local execution only. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-05T12:33:30Z No. of bitstreams: 1 ijimai20121_7_1_pdf_62175.pdf: 755340 bytes, checksum: cc15ae458bdf5674b6afba0f7799e448 (MD5); Made available in DSpace on 2019-12-05T12:33:30Z (GMT). No. of bitstreams: 1 ijimai20121_7_1_pdf_62175.pdf: 755340 bytes, checksum: cc15ae458bdf5674b6afba0f7799e448 (MD5)

Linked Data Methodologies for Managing Information about Television Content

Linked Data Methodologies for Managing Information about Television Content Redondo-García, José Luis; Botón-Fernández, Vicente; Lozano-Tello, Adolfo OntoTV is a television information management system designed for improving the quality and quantity of the information available in the current television platforms. In order to achieve this objective, OntoTV (1) collects the information offered by the broadcasters, (2) integrates it into a ontology-based data structure, (3) extracts extra data from alternative television sources, and (4) makes possible for the user to perform queries over the stored information. This document shows the way Linked Data methodologies have been applied in OntoTV system, and the improvements in the data consumption and publication processes that have been obtained as result. On the one hand, the possibility of accessing to information available in the Web of Data has made possible to offer more complete descriptions about the programs, as well as more detailed guides than those obtained by using classic collection methods. On the other hand, as the information of the television programs and channels is published according to the Linked Data philosophy, it becomes available not only for OntoTV clients, but also for other agents able to access Linked Data resources, who could offer the viewer more fresh and innovative features. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T10:49:48Z No. of bitstreams: 1 IJIMAI20121_6_5.pdf: 1129975 bytes, checksum: 476f7c52062460a2528613c34f055765 (MD5); Made available in DSpace on 2019-12-03T10:49:48Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_5.pdf: 1129975 bytes, checksum: 476f7c52062460a2528613c34f055765 (MD5)

Comparative Study on Feature Selection and Fusion Schemes for Emotion Recognition from Speech

Comparative Study on Feature Selection and Fusion Schemes for Emotion Recognition from Speech Planet, Santiago; Iriondo, Ignasi The automatic analysis of speech to detect affective states may improve the way users interact with electronic devices. However, the analysis only at the acoustic level could be not enough to determine the emotion of a user in a realistic scenario. In this paper we analyzed the spontaneous speech recordings of the FAU Aibo Corpus at the acoustic and linguistic levels to extract two sets of features. The acoustic set was reduced by a greedy procedure selecting the most relevant features to optimize the learning stage. We compared two versions of this greedy selection algorithm by performing the search of the relevant features forwards and backwards. We experimented with three classification approaches: Nave-Bayes, a support vector machine and a logistic model tree, and two fusion schemes: decision-level fusion, merging the hard-decisions of the acoustic and linguistic classifiers by means of a decision tree; and feature-level fusion, concatenating both sets of features before the learning stage. Despite the low performance achieved by the linguistic data, a dramatic improvement was achieved after its combination with the acoustic information, improving the results achieved by this second modality on its own. The results achieved by the classifiers using the parameters merged at feature level outperformed the classification results of the decision-level fusion scheme, despite the simplicity of the scheme. Moreover, the extremely reduced set of acoustic features obtained by the greedy forward search selection algorithm improved the results provided by the full set. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T10:36:45Z No. of bitstreams: 1 IJIMAI20121_6_6.pdf: 382450 bytes, checksum: 1a3a08599d1b3bc8d031977afa6e70ca (MD5); Made available in DSpace on 2019-12-03T10:36:45Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_6.pdf: 382450 bytes, checksum: 1a3a08599d1b3bc8d031977afa6e70ca (MD5)

A Useful Metaheuristic for Dynamic Channel Assignment in Mobile Cellular Systems

A Useful Metaheuristic for Dynamic Channel Assignment in Mobile Cellular Systems Kumar Singh, Deepak; Srinivas, K.; Bhagwan Das, D. The prime objective of a Channel Assignment Problem (CAP) is to assign appropriate number of required channels to each cell in a way to achieve both efficient frequency spectrum utilization and minimization of interference effects (by satisfying a number of channel reuse constraints). Dynamic Channel Assignment (DCA) assigns the channels to the cells dynamically according to traffic demand, and hence, can provide higher capacity (or lower call blocking probability), fidelity and quality of service than the fixed assignment schemes. Channel assignment algorithms are formulated as combinatorial optimization problems and are NP-hard. Devising a DCA, that is practical, efficient, and which can generate high quality assignments, is challenging. Though Metaheuristic Search techniques like Evolutionary Algorithms, Differential Evolution, Particle Swarm Optimization prove effective in the solution of Fixed Channel Assignment (FCA) problems but they still require high computational time and therefore may be inefficient for DCA. A number of approaches have been proposed for the solution of DCA problem but the high complexity of these proposed approaches makes them unsuitable/less efficient for practical use. Therefore, this paper presents an effective and efficient Hybrid Discrete Binary Differential Evolution Algorithm (HDB-DE) for the solution of DCA Problem Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T10:16:53Z No. of bitstreams: 1 IJIMAI20121_6_1.pdf: 419918 bytes, checksum: 9ed933dd981ea6a03123333d2ea35da4 (MD5); Made available in DSpace on 2019-12-03T10:16:53Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_1.pdf: 419918 bytes, checksum: 9ed933dd981ea6a03123333d2ea35da4 (MD5)

DEVELOP-FPS: a First Person Shooter Development Tool for Rule-based Scripts

DEVELOP-FPS: a First Person Shooter Development Tool for Rule-based Scripts Correia, Bruno; Urbano, Paulo; Moniz, Luís We present DEVELOP-FPS, a software tool specially designed for the development of First Person Shooter (FPS) players controlled by Rule Based Scripts. DEVELOP-FPS may be used by FPS developers to create, debug, maintain and compare rule base player behaviours, providing a set of useful functionalities: i) for an easy preparation of the right scenarios for game debugging and testing; ii) for controlling the game execution: users can stop and resume the game execution at any instant, monitoring and controlling every player in the game, monitoring the state of each player, their rule base activation, being able to issue commands to control their behaviour; and iii) to automatically run a certain number of game executions and collect data in order to evaluate and compare the players performance along a sufficient number of similar experiments. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T10:06:57Z No. of bitstreams: 1 IJIMAI20121_6_7.pdf: 728274 bytes, checksum: 0182356e8f8c0e92428053ee088d9f56 (MD5); Made available in DSpace on 2019-12-03T10:06:57Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_7.pdf: 728274 bytes, checksum: 0182356e8f8c0e92428053ee088d9f56 (MD5)

Improving Web Learning through model Optimization using Bootstrap for a Tour-Guide Robot

Improving Web Learning through model Optimization using Bootstrap for a Tour-Guide Robot León, Rafael; Rainer, J. Javier; Rojo, José Manuel; Galán, Ramón We perform a review of Web Mining techniques and we describe a Bootstrap Statistics methodology applied to pattern model classifier optimization and verification for Supervised Learning for Tour-Guide Robot knowledge repository management. It is virtually impossible to test thoroughly Web Page Classifiers and many other Internet Applications with pure empirical data, due to the need for human intervention to generate training sets and test sets. We propose using the computer-based Bootstrap paradigm to design a test environment where they are checked with better reliability. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T09:57:24Z No. of bitstreams: 1 IJIMAI20121_6_2.pdf: 620104 bytes, checksum: 45501369bacf46d3e346b60e77c414de (MD5); Made available in DSpace on 2019-12-03T09:57:24Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_2.pdf: 620104 bytes, checksum: 45501369bacf46d3e346b60e77c414de (MD5)

Conceptualizing the e-Learning Assessment Domain using an Ontology Network

Conceptualizing the e-Learning Assessment Domain using an Ontology Network Romero, Lucía; Gutiérrez, Milagros; Caliusco, María Laura During the last year, approaches that use ontologies, the backbone of the Semantic Web technologies, for different purposes in the assessment domain of e-Learning have emerged. One of these purposes is the use of ontologies as a mean of providing a structure to guide the automated design of assessments. The most of the approaches that deal with this problem have proposed individual ontologies that model only a part of the assessment domain. The main contribution of this paper is an ontology network, called AONet, that conceptualizes the e-assessment domain with the aim of supporting the semi-automatic generation of it. The main advantage of this network is that it is enriched with rules for considering not only technical aspects of an assessment but also pedagogic. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T09:44:11Z No. of bitstreams: 1 IJIMAI20121_6_3.pdf: 759584 bytes, checksum: f2e6244d28e91f90c7505bf579dc25ad (MD5); Made available in DSpace on 2019-12-03T09:44:11Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_3.pdf: 759584 bytes, checksum: f2e6244d28e91f90c7505bf579dc25ad (MD5)

O-ODM Framework for Object-Relational Databases

O-ODM Framework for Object-Relational Databases Rombaldo Jr, Carlos Alberto; Alves Souza, Solange N; de Souza, Luiz Sergio Object-Relational Databases introduce new features which allow manipulating objects in databases.At present, many DBMS offer resources to manipulate objects in database, but most application developers just map class to relations tables, failing to exploit the O-R model strength. The lack of tools that aid the database project contributes to this situation. This work presents O-ODM (Object-Object Database Mapping), a persistent framework that maps objects from OO applications to database objects.Persistent Frameworks have been used to aid developers, managing all access to DBMS. This kind of tool allows developers to persist objects without solid knowledge about DBMSs and specific languages, improving the developers' productivity, mainly when a different DBMS is used. The results of some experiments using O-ODM are shown. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T09:29:26Z No. of bitstreams: 1 IJIMAI20121_6_4.pdf: 490822 bytes, checksum: 44f43d5d74961dbc4c1ac0049089c818 (MD5); Made available in DSpace on 2019-12-03T09:29:26Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_4.pdf: 490822 bytes, checksum: 44f43d5d74961dbc4c1ac0049089c818 (MD5)

Performance comparison of hierarchical checkpoint protocols grid computing

Performance comparison of hierarchical checkpoint protocols grid computing Massata NDIAYE, Ndeye; SENS, Pierre; THIARE, Ousmane Grid infrastructure is a large set of nodes geographically distributed and connected by a communication. In this context, fault tolerance is a necessity imposed by the distribution that poses a number of problems related to the heterogeneity of hardware, operating systems, networks, middleware, applications, the dynamic resource, the scalability, the lack of common memory, the lack of a common clock, the asynchronous communication between processes. To improve the robustness of supercomputing applications in the presence of failures, many techniques have been developed to provide resistance to these faults of the system. Fault tolerance is intended to allow the system to provide service as specified in spite of occurrences of faults. It appears as an indispensable element in distributed systems. To meet this need, several techniques have been proposed in the literature. We will study the protocols based on rollback recovery. These protocols are classified into two categories: coordinated checkpointing and rollback protocols and log-based independent checkpointing protocols or message logging protocols. However, the performance of a protocol depends on the characteristics of the system, network and applications running. Faced with the constraints of large-scale environments, many of algorithms of the literature showed inadequate. Given an application environment and a system, it is not easy to identify the recovery protocol that is most appropriate for a cluster or hierarchical environment, like grid computing. While some protocols have been used successfully in small scale, they are not suitable for use in large scale. Hence there is a need to implement these protocols in a hierarchical fashion to compare their performance in grid computing. In this paper, we propose hierarchical version of four well-known protocols. We have implemented and compare the performance of these protocols in clusters and grid computing using the Omnet++ simulator. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T08:35:21Z No. of bitstreams: 1 IJIMAI20121_5_6_0.pdf: 501573 bytes, checksum: 50f397ad789c8fa82cf3afd94c7cb7e2 (MD5); Made available in DSpace on 2019-12-03T08:35:21Z (GMT). No. of bitstreams: 1 IJIMAI20121_5_6_0.pdf: 501573 bytes, checksum: 50f397ad789c8fa82cf3afd94c7cb7e2 (MD5)

Approach for solving multimodal problems using Genetic Algorithms with Grouped into Species optimized with Predator-Prey

Approach for solving multimodal problems using Genetic Algorithms with Grouped into Species optimized with Predator-Prey Seoane, Pablo; Gestal, Marcos; Dorado, Julián Over recent years, Genetic Algorithms have proven to be an appropriate tool for solving certain problems. However, it does not matter if the search space has several valid solutions, as their classic approach is insufficient. To this end, the idea of dividing the individuals into species has been successfully raised. However, this solution is not free of drawbacks, such as the emergence of redundant species, overlapping or performance degradation by significantly increasing the number of individuals to be evaluated. This paper presents the implementation of a method based on the predator-prey technique, with the aim of providing a solution to the problem, as well as a number of examples to prove its effectiveness. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T08:22:09Z No. of bitstreams: 1 ijimai20121_5_1_pdf_24294.pdf: 440636 bytes, checksum: 63a237a6b427c31f75ea72be91e7d96a (MD5); Made available in DSpace on 2019-12-03T08:22:09Z (GMT). No. of bitstreams: 1 ijimai20121_5_1_pdf_24294.pdf: 440636 bytes, checksum: 63a237a6b427c31f75ea72be91e7d96a (MD5)

Recognizing Human Activities Userindependently on Smartphones Based on Accelerometer Data

Recognizing Human Activities Userindependently on Smartphones Based on Accelerometer Data Siirtola, Pekka; Röning, Juha Real-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemented to the phone and the whole classification process (preprocessing, feature extraction and classification) was done on the device. The system is trained using phone orientation independent features to recognize five everyday activities: walking, running, cycling, driving a car and sitting/standing while the phone is in the pocket of the subject's trousers. Two classifiers were compared, knn (k nearest neighbors) and QDA (quadratic discriminant analysis). The models for real-time activity recognition were trained offline using a data set collected from eight subjects and these offline results were compared to real-time recognition rates, which are obtained by implementing models to mobile activity recognition application which currently supports two operating systems: Symbian^3 and Android. The results show that the presented method is light and, therefore, suitable for be used in real-time recognition. In addition, the recognition rates on the smartphones were encouraging, in fact, the recognition accuracies obtained are approximately as high as offline recognition rates. Also, the results show that the method presented is not an operating system dependent. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-02T13:43:47Z No. of bitstreams: 1 IJIMAI20121_5_5.pdf: 384789 bytes, checksum: 3fc2de3c84520a4808e6218a437f750f (MD5); Made available in DSpace on 2019-12-02T13:43:47Z (GMT). No. of bitstreams: 1 IJIMAI20121_5_5.pdf: 384789 bytes, checksum: 3fc2de3c84520a4808e6218a437f750f (MD5)

Wireless Sensor Networks and Real-Time Locating Systems to Fight against Maritime Piracy

Wireless Sensor Networks and Real-Time Locating Systems to Fight against Maritime Piracy García, Óscar; Alonso, Ricardo S.; Tapia, Dante I.; Guevara, Fabio There is a wide range of military and civil applications where Wireless Sensor Networks (WSNs) and Multi-Agent Systems (MASs) can be used for providing context-awareness for troops and special corps. On the one hand, WSNs comprise an ideal technology to develop Real-Time Locating Systems (RTLSs) aimed at indoor environments, where existing global navigation satellite systems do not work properly. On the other hand, agent-based architectures allow building autonomous and robust systems that are capable of working on highly dynamic scenarios. This paper presents two piracy scenarios where the n-Core platform can be applied. n-Core is a hardware and software platform intended for developing and deploying easily and quickly a wide variety of WSNs applications based on the ZigBee standard. In the first scenario a RTLS is deployed to support boarding and rescue operations. In the second scenario a multi-agent system is proposed to detect the unloading of illegal traffic of merchandise at ports. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-25T13:39:30Z No. of bitstreams: 1 IJIMAI20121_5_2.pdf: 512417 bytes, checksum: c12a838275bc55d6e9a20090acb3cc36 (MD5); Made available in DSpace on 2019-11-25T13:39:30Z (GMT). No. of bitstreams: 1 IJIMAI20121_5_2.pdf: 512417 bytes, checksum: c12a838275bc55d6e9a20090acb3cc36 (MD5)

Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models

Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models Goyal, Sumit; Kumar Goyal, Gyanendra For predicting the shelf life of processed cheese stored at 7-8 C, Elman single and multilayer models were developed and compared. The input variables used for developing the models were soluble nitrogen, pH; standard plate count, Yeast & mould count, and spore count, while output variable was sensory score. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were applied in order to compare the prediction ability of the developed models. The Elman models got simulated very well and showed excellent agreement between the experimental data and the predicted values, suggesting that the Elman models can be used for predicting the shelf life of processed cheese. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-25T13:30:01Z No. of bitstreams: 1 IJIMAI20121_5_8.pdf: 354637 bytes, checksum: a0f05c6bfb57dea450dca1a5de6797de (MD5); Made available in DSpace on 2019-11-25T13:30:01Z (GMT). No. of bitstreams: 1 IJIMAI20121_5_8.pdf: 354637 bytes, checksum: a0f05c6bfb57dea450dca1a5de6797de (MD5)

Improved Differential Evolution Algorithm for Parameter Estimation to Improve the Production of Biochemical Pathway

Improved Differential Evolution Algorithm for Parameter Estimation to Improve the Production of Biochemical Pathway Khim Chong, Chuii; Saberi Mohamad, Mohd; Deris, Safaai; Shahir Shamsir, Mohd; Wen Choon, Yee; En Chai, Lian This paper introduces an improved Differential Evolution algorithm (IDE) which aims at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Many computation algorithms face obstacles due to the noisy data and difficulty of the system in estimating myriad of parameters, and require longer computational time to estimate the relevant parameters. The proposed algorithm (IDE) in this paper is a hybrid of a Differential Evolution algorithm (DE) and a Kalman Filter (KF). The outcome of IDE is proven to be superior than Genetic Algorithm (GA) and DE. The results of IDE from experiments show estimated optimal kinetic parameters values, shorter computation time and increased accuracy for simulated results compared with other estimation algorithms. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-25T13:22:09Z No. of bitstreams: 1 IJIMAI20121_5_3_0.pdf: 630396 bytes, checksum: 74a0895721a42251f43ea1dc5068352c (MD5); Made available in DSpace on 2019-11-25T13:22:09Z (GMT). No. of bitstreams: 1 IJIMAI20121_5_3_0.pdf: 630396 bytes, checksum: 74a0895721a42251f43ea1dc5068352c (MD5)

A Grammatical Approach to the Modeling of an Autonomous Robot

A Grammatical Approach to the Modeling of an Autonomous Robot López-García, Gabriel; Gallego-Sánchez, A. Javier; Dalmau-Espert, J. Luis; Molina-Carmona, Rafael; Compañ-Rosique, Patricia Virtual Worlds Generator is a grammatical model that is proposed to define virtual worlds. It integrates the diversity of sensors and interaction devices, multimodality and a virtual simulation system. Its grammar allows the definition and abstraction in symbols strings of the scenes of the virtual world, independently of the hardware that is used to represent the world or to interact with it. A case study is presented to explain how to use the proposed model to formalize a robot navigation system with multimodal perception and a hybrid control scheme of the robot. The result is an instance of the model grammar that implements the robotic system and is independent of the sensing devices used for perception and interaction. As a conclusion the Virtual Worlds Generator adds value in the simulation of virtual worlds since the definition can be done formally and independently of the peculiarities of the supporting devices. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-25T13:11:11Z No. of bitstreams: 1 IJIMAI20121_5_4_0.pdf: 640641 bytes, checksum: d05974c0da1f4a6c5c014e86b74d0aa9 (MD5); Made available in DSpace on 2019-11-25T13:11:11Z (GMT). No. of bitstreams: 1 IJIMAI20121_5_4_0.pdf: 640641 bytes, checksum: d05974c0da1f4a6c5c014e86b74d0aa9 (MD5)

Fusing Facial Features for Face Recognition

Fusing Facial Features for Face Recognition Ahmad Dargham, Jamal; Chekima, Ali; Gubin Moung, Ervin Face recognition is an important biometric method because of its potential applications in many fields, such as access control, surveillance, and human-computer interaction. In this paper, a face recognition system that fuses the outputs of three face recognition systems based on Gabor jets is presented. The first system uses the magnitude, the second uses the phase, and the third uses the phase-weighted magnitude of the jets. The jets are generated from facial landmarks selected using three selection methods. It was found out that fusing the facial features gives better recognition rate than either facial feature used individually regardless of the landmark selection method. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-19T13:44:47Z No. of bitstreams: 1 IJIMAI20121_5_7.pdf: 719614 bytes, checksum: cb284fe86989d96931d9916718c166d3 (MD5); Made available in DSpace on 2019-11-19T13:44:47Z (GMT). No. of bitstreams: 1 IJIMAI20121_5_7.pdf: 719614 bytes, checksum: cb284fe86989d96931d9916718c166d3 (MD5)

Design of a System for Monitoring Technology Multiple Application in Order to Measure the Gap in Technology Companies "MiPymes"

Design of a System for Monitoring Technology Multiple Application in Order to Measure the Gap in Technology Companies "MiPymes" Espinel Ortega, Álvaro; Martín García, Víctor; Vega Escobar, Adriana Marcela This is one of several articles that aims to disseminate the research results in developing advanced doctoral paper entitled: Model of System for Technological Surveillance for multiple application to measure the technological gap in Colombian companies. This paper is related to the design of Technological Surveillance, following the Rational Unified Process for software development, commonly known as RUP, UML, and implemented with the programming language C#, database engine SQL Server, set to an architectural model of three layers (3-Tier). This document takes into account especially related to requirements, architecture and modeling and subsequent articles are related to testing, evaluation and results of the prototype was implemented and results in the field of technologically surveillance exercises, all in accordance with the objectives of the Doctoral Paper. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-13T13:33:08Z No. of bitstreams: 1 IJIMAI20111_4_9.pdf: 1012682 bytes, checksum: 368a11d4756a21329a3448ce8fabbf5a (MD5); Made available in DSpace on 2019-11-13T13:33:08Z (GMT). No. of bitstreams: 1 IJIMAI20111_4_9.pdf: 1012682 bytes, checksum: 368a11d4756a21329a3448ce8fabbf5a (MD5)

Virtual Objects on the Internet of Things

Virtual Objects on the Internet of Things Pascual Espada, Jordán; Sanjuán Martínez, Óscar; Pelayo García-Bustelo, B. Cristina; Cueva Lovelle, Juan Manuel As technology advances more and more things began to appear in digital format, such as: tickets, agendas, books, electronic purses, etc. Internet of things encourages communication and integration of physical objects with each other and people to automate tasks and improve efficiency. Digital objects like physicists should be part of Internet of Things but the different structures of these digital objects causes in most cases these digital objects can interact only with specific applications that know the specific format. Based on the problems in this paper proposes a structure that supports the generic construction of virtual objects irrespective of their business logic and their integration with other applications and things. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-13T12:54:59Z No. of bitstreams: 1 IJIMAI20111_4_4.pdf: 850892 bytes, checksum: ed6eecb8dd32eb3ffe279b721fa73e9a (MD5); Made available in DSpace on 2019-11-13T12:54:59Z (GMT). No. of bitstreams: 1 IJIMAI20111_4_4.pdf: 850892 bytes, checksum: ed6eecb8dd32eb3ffe279b721fa73e9a (MD5)

Clasification Of Arrhythmic ECG Data Using Machine Learning Techniques

Clasification Of Arrhythmic ECG Data Using Machine Learning Techniques Vishwa, Abhinav; Lal, Mohit K.; Dixit, Sharad; Vardwaj, Dr. Pritish In this paper we proposed a automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia using multi-channel ECG recordings. In this study, we are mainly interested in producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. Neural network model with back propagation algorithm is used to classify arrhythmia cases into normal and abnormal classes. Networks models are trained and tested for MIT-BIH arrhythmia. The differen structures of ANN have been trained by mixture of arrhythmic and non arrhythmic data patient. The classification performance is evaluated using measures; sensitivity, specificity, classification accuracy, mean squared error (MSE), receiver operating characteristics (ROC) and area under curve (AUC). Our experimental results gives 96.77% accuracy on MIT-BIH database and 96.21% on database prepared by including NSR database also. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-13T12:45:43Z No. of bitstreams: 1 IJIMAI20111_4_11.pdf: 410624 bytes, checksum: 516cf7aa3b2951a50cab32a4aed44913 (MD5); Made available in DSpace on 2019-11-13T12:45:43Z (GMT). No. of bitstreams: 1 IJIMAI20111_4_11.pdf: 410624 bytes, checksum: 516cf7aa3b2951a50cab32a4aed44913 (MD5)

The Cambrian Explosion of Popular 3D Printing

The Cambrian Explosion of Popular 3D Printing Chulilla Cano, Juan Luis The unexpected appearance of 3D printing has caught many of technology analyst by surprise. In this paper we aim to provide a social context to the feedback loops that have generated this rapid evolution of technologies and skills involved in 3D printing, as well as and online communities related with 3D printing and the impact of this evolution on media an popular imaginary… and our near future. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-13T12:27:12Z No. of bitstreams: 1 IJIMAI20111_4_5.pdf: 349823 bytes, checksum: a27adbf2cb7262fa847176018bb50055 (MD5); Made available in DSpace on 2019-11-13T12:27:12Z (GMT). No. of bitstreams: 1 IJIMAI20111_4_5.pdf: 349823 bytes, checksum: a27adbf2cb7262fa847176018bb50055 (MD5)

Accurate location estimation of moving object In Wireless Sensor network

Accurate location estimation of moving object In Wireless Sensor network Bhaskar Semwal, Vijay; Bhaskar Semwal, Vinay; Sati, Meenakshi; Verma, Dr.Shirshu One of the central issues in wirless sensor networks is track the location, of moving object which have overhead of saving data, an accurate estimation of the target location of object with energy constraint .We do not have any mechanism which control and maintain data .The wireless communication bandwidth is also very limited. Some field which is using this technique are flood and typhoon detection, forest fire detection, temperature and humidity and ones we have these information use these information back to a central air conditioning and ventilation. In this research paper, we propose protocol based on the prediction and adaptive based algorithm which is using less sensor node reduced by an accurate estimation of the target location. We had shown that our tracking method performs well in terms of energy saving regardless of mobility pattern of the mobile target. We extends the life time of network with less sensor node. Once a new object is detected, a mobile agent will be initiated to track the roaming path of the object. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-13T12:18:43Z No. of bitstreams: 1 IJIMAI20111_4_12.pdf: 417391 bytes, checksum: dc1c8fbe1c0946ef40782444df5b238d (MD5); Made available in DSpace on 2019-11-13T12:18:43Z (GMT). No. of bitstreams: 1 IJIMAI20111_4_12.pdf: 417391 bytes, checksum: dc1c8fbe1c0946ef40782444df5b238d (MD5)

Patterns of Software Development Process

Patterns of Software Development Process Bolaños Castro, Sandro Javier; González-Crespo, Rubén; Medina García, Víctor Hugo This article presents a set of patterns that can be found to perform best practices in software processes that are directly related to the problem of implementing the activities of the process, the roles involved, the knowledge generated and the inputs and outputs belonging to the process. In this work, a definition of the architecture is encouraged by using different recurrent configurations that strengthen the process and yield efficient results for the development of a software project. The patterns presented constitute a catalog, which serves as a vocabulary for communication among project participants [1], [2], and also can be implemented through software tools, thus facilitating patterns implementation [3]. Additionally, a tool that can be obtained under GPL (General Public license) is provided for this purpose. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-13T11:13:09Z No. of bitstreams: 1 IJIMAI20111_4_6.pdf: 618663 bytes, checksum: ae7b253f1ced530434ecd224959e56d6 (MD5); Made available in DSpace on 2019-11-13T11:13:09Z (GMT). No. of bitstreams: 1 IJIMAI20111_4_6.pdf: 618663 bytes, checksum: ae7b253f1ced530434ecd224959e56d6 (MD5)

Towards an Ontology to Describe the Taxonomy of Common Modules in Learning Management Systems

Towards an Ontology to Describe the Taxonomy of Common Modules in Learning Management Systems Montenegro-Marin, Carlos Enrique; Cueva-Lovelle, Juan Manuel; Sanjuan, Oscar; Nuñez-Valdez, Edward Rolando This article have the objective a create ontology for "common modules in a Learning Management Systems", the steps for the build Ontology were: Determine the domain and scope of the ontology, Consider reusing existing ontology, Enumerate important terms in the ontology, Define the classes and the class hierarch, Define the properties of classes—slot and Define the facets of the slot, finally be explained how the ontology is composed. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-13T10:47:01Z No. of bitstreams: 1 IJIMAI20111_4_8.pdf: 1716637 bytes, checksum: 47879eda3025f1281b97823819060bc4 (MD5); Made available in DSpace on 2019-11-13T10:47:01Z (GMT). No. of bitstreams: 1 IJIMAI20111_4_8.pdf: 1716637 bytes, checksum: 47879eda3025f1281b97823819060bc4 (MD5)

Introduction to Devices Orchestration in Internet of Things Using SBPMN

Introduction to Devices Orchestration in Internet of Things Using SBPMN González García, Alejandro; Álvarez Álvarez, Manuel; Pascual Espada, Jordán; Sanjuán Martínez, Óscar; Cueva Lovelle, Juan Manuel; Pelayo García-Bustelo, B. Cristina In this research we try to provide an architecture that allows the orchestration of objects that are part of the Internet of things creating business processes. Internet of Things is still in full development; this implies that there is a lack of standards for its proper implementation. Among these gaps is for example the technology used to allow objects to connect to the network, since there are several options but none seems to end imposed that is why this work try to provide architecture that imposes an alternative solution to this problem. However, it is difficult to provide a common solution to all the objects used in everyday life because of its great diversity, it requires us to classify them and thus create an appropriate architecture for each of the types These architectures are designed to facilitate the devices orchestration in a similar way as is currently done with web services enabling business process modeling. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-13T10:29:46Z No. of bitstreams: 1 IJIMAI20111_4_3.pdf: 393575 bytes, checksum: 4f9c98040a82bada08c9d1e6a5f75dc1 (MD5); Made available in DSpace on 2019-11-13T10:29:46Z (GMT). No. of bitstreams: 1 IJIMAI20111_4_3.pdf: 393575 bytes, checksum: 4f9c98040a82bada08c9d1e6a5f75dc1 (MD5)

Discover, Reuse and Share Knowledge on Service Oriented Architectures

Discover, Reuse and Share Knowledge on Service Oriented Architectures Soto Carrión, Jesús; Shu, Lei; Garcia Gordo, Elisa Current Semantic Web frameworks provide a complete infrastructure to manage ontologies schemes easing information retrieval with inference support. Ideally, the use of their frameworks should be transparent and decoupled, avoiding direct dependencies either on the application logic or on the ontology language. Besides there are different logic models used by ontology languages (OWL- Description Logic, OpenCyc-FOL, ...) and query languages (RDQL, SPARQL, OWLQL, nRQL, etc..). These facts show integration and interoperability tasks between ontologies and applications are tedious on currently systems. This research provides a general ESB service engine design based on JBI that enables ontology query and reasoning capabilities thought an Enterprise Service Bus. An early prototype that shows how works our research ideas has been developed. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-06T13:27:31Z No. of bitstreams: 1 IJIMAI20111_4_1.pdf: 1137787 bytes, checksum: 58bbff8fbaad807c3f2c4c0de9ab05eb (MD5); Made available in DSpace on 2019-11-06T13:27:31Z (GMT). No. of bitstreams: 1 IJIMAI20111_4_1.pdf: 1137787 bytes, checksum: 58bbff8fbaad807c3f2c4c0de9ab05eb (MD5)

Social Voting Techniques: A Comparison of the Methods Used for Explicit Feedback in Recommendation Systems

Social Voting Techniques: A Comparison of the Methods Used for Explicit Feedback in Recommendation Systems Nuñez-Valdez, Edward Rolando; Cueva-Lovelle, Juan Manuel; Sanjuan, Oscar; Montenegro-Marin, Carlos Enrique; Infante Hernandez, Guillermo Web recommendation systems usually brings a content list to users based on previous ratings made by them to other similar contents through some social voting mean. This paper aims to present a comparison of the main explicit rating methods used by web recommendation systems. The goal of this survey is to determine which of the studied methods fits better to user preferences when they rate a content on the web; based on the obtained results, a recommendation system can be implemented using an explicit feedback method to achieve this goal. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-06T13:21:01Z No. of bitstreams: 1 IJIMAI20111_4_10.pdf: 675391 bytes, checksum: bb6dbdf45da12a8ff7d6299fd4f58062 (MD5); Made available in DSpace on 2019-11-06T13:21:01Z (GMT). No. of bitstreams: 1 IJIMAI20111_4_10.pdf: 675391 bytes, checksum: bb6dbdf45da12a8ff7d6299fd4f58062 (MD5)

Antipatterns: A Compendium of Bad Practices in Software Development Processes

Antipatterns: A Compendium of Bad Practices in Software Development Processes Bolaños-Castro, Sandro Javier; González-Crespo, Rubén; Medina García, Víctor Hugo This Article presents a set of software process antipatterns, which arise as a result of bad practices within application development processes. Process AntiPatterns warn us about the harmful effects that may arise in projects, and also describe the features that identify them. The proposed antipatterns provide a catalog that serves as a vocabulary for communication among project participants. Such Antipatterns can be implemented through software tools in order to keep better record of their implementation. Additionally, a tool that can operate under GPL (General Public license) is provided for this purpose. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-06T13:14:51Z No. of bitstreams: 1 IJIMAI20111_4_7.pdf: 717704 bytes, checksum: 0eb15709eaa2e50f1655042a1db9e76e (MD5); Made available in DSpace on 2019-11-06T13:14:51Z (GMT). No. of bitstreams: 1 IJIMAI20111_4_7.pdf: 717704 bytes, checksum: 0eb15709eaa2e50f1655042a1db9e76e (MD5)

Facial Emotion Recognition Using Context Based Multimodal Approach

Facial Emotion Recognition Using Context Based Multimodal Approach Metri, Priya; Ghorpade, Jayshree; Butalia, Ayesha Emotions play a crucial role in person to person interaction. In recent years, there has been a growing interest in improving all aspects of interaction between humans and computers. The ability to understand human emotions is desirable for the computer in several applications especially by observing facial expressions. This paper explores a ways of humancomputer interaction that enable the computer to be more aware of the user’s emotional expressions we present a approach for the emotion recognition from a facial expression, hand and body posture. Our model uses multimodal emotion recognition system in which we use two different models for facial expression recognition and for hand and body posture recognition and then combining the result of both classifiers using a third classifier which give the resulting emotion . Multimodal system gives more accurate result than a signal or bimodal system. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-11-06T13:09:51Z No. of bitstreams: 1 IJIMAI20111_4_2.pdf: 432022 bytes, checksum: 415f33cd05d425391be8d92beb95cfb7 (MD5); Made available in DSpace on 2019-11-06T13:09:51Z (GMT). No. of bitstreams: 1 IJIMAI20111_4_2.pdf: 432022 bytes, checksum: 415f33cd05d425391be8d92beb95cfb7 (MD5)

Relative Radiometric Normalization of Multitemporal images

Relative Radiometric Normalization of Multitemporal images Broncano Mateos, Carlos Javier; Pinilla Ruiz, Carlos; González-Crespo, Rubén; Castillo Sanz, Andrés G A correct radiometric normalization between both images is fundamental for change detection. MAD method and its IR-MAD extension in an implementation on multisprectral aerial images is described in this paper. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-10-25T10:18:35Z No. of bitstreams: 1 IJIMAI20101_3_9.pdf: 563574 bytes, checksum: 6a5fa30370820a9ea9bfbb38f1659314 (MD5); Made available in DSpace on 2019-10-25T10:18:35Z (GMT). No. of bitstreams: 1 IJIMAI20101_3_9.pdf: 563574 bytes, checksum: 6a5fa30370820a9ea9bfbb38f1659314 (MD5)

Trust Levels Definition on Virtual Learning Platforms Through Semantic Languages

Trust Levels Definition on Virtual Learning Platforms Through Semantic Languages Gaona-García, Paulo Alonso; Soto-Carrión, Jesús; Montenegro-Marin, Carlos Enrique Trust level concept is a topic that has opened a knowledge area about the profile evaluation and the people participation in Social Networks. These have presented a high knowledge profit, but at the same time it is necessary to analyze a group of variables to determine the trust participants' degree. In addition, this is a topic that from some years ago has been presenting a big expectation to settle some alternatives to generate confidence in an activer community on internet. To establish these parameters it is important to define a model to abstract some variables that are involved in this process. For this, it is relevant to take into account the semantic languages as one of the alternatives that allow these kinds of activities. The purpose of this article is to analyze the Trust Levels definition in the contents that are shared on Open Source Virtual learning Platforms through the use of a model of representation of semantic languages. The last ones allow determining the trust in the use of learning objects that are shared in this kind of platforms. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-10-25T09:03:38Z No. of bitstreams: 1 IJIMAI20101_3_4.pdf: 695418 bytes, checksum: e77fe561b15b049feb23b9f48c4449ff (MD5); Made available in DSpace on 2019-10-25T09:03:38Z (GMT). No. of bitstreams: 1 IJIMAI20101_3_4.pdf: 695418 bytes, checksum: e77fe561b15b049feb23b9f48c4449ff (MD5)

A Prototype for linear features generalization

A Prototype for linear features generalization Lorenzo Romero, Wenceslao; González-Crespo, Rubén; Castillo Sanz, Andrés G A computer application designed to generalize linear elements in a vector formatted cartographic set by means of two of the most contrasted line generalization algorithms, Douglas-Peucker simplification and Bézier curves based smoothing, is presented in this paper. Regarding codification, the simultaneous treatment of different lineal geometry entity classes and the conservation of their original topological relationships among them are considered. It is recommended in processes that produce small scale reductions (in a 1:2 relationship or similar). The application allows changing the characteristic parameters of the referred algorithms and proposes a report of the results obtained after every transformation. That way it supplies an additional facility as a trial tool to choose the parameters that give the best results in every process. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-10-25T08:35:32Z No. of bitstreams: 1 IJIMAI20101_3_10.pdf: 777882 bytes, checksum: 23da103bba795c2b196cdb14c9dc51b5 (MD5); Made available in DSpace on 2019-10-25T08:35:32Z (GMT). No. of bitstreams: 1 IJIMAI20101_3_10.pdf: 777882 bytes, checksum: 23da103bba795c2b196cdb14c9dc51b5 (MD5)

Biometry, the safe key

Biometry, the safe key Fraile Hurtado, María; Herrero Langreo, Miguel; Menéndez de Miguel, Pilar; Delgado Villanueva, Valerio Biometry is the next step in authentication, why do not we take this stepforward in our communication security systems? Keys are the main disadvantage in the cryptography, what if we were our own key? Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-10-24T12:38:49Z No. of bitstreams: 1 IJIMAI20101_3_6.pdf: 566892 bytes, checksum: b3326c331b37a0b79b0d7a589625a9c8 (MD5); Made available in DSpace on 2019-10-24T12:38:49Z (GMT). No. of bitstreams: 1 IJIMAI20101_3_6.pdf: 566892 bytes, checksum: b3326c331b37a0b79b0d7a589625a9c8 (MD5)

A New Technique in saving Fingerprint with low volume by using Chaos Game and Fractal Theory

A New Technique in saving Fingerprint with low volume by using Chaos Game and Fractal Theory Jampour, Mahdi; Javidi, Mohammad M.; Nejad, Adel Soleymani; Ashourzadeh, Maryam; Yaghoobi, Mahdi Fingerprint is one of the simplest and most reliable biometric features of human for identification. In this study by using fractal theory and by the assistance of Chaos Game a new fractal is made from fingerprint. While making the new fractal by using Chaos Game mechanism some parameters, which can be used in identification process, can be deciphered. For this purpose, a fractal is made for each fingerprint, we save 10 parameters for every fingerprint, which have necessary information for identity, as said before. So we save 10 decimal parameters with 0.02 accuracy instead of saving the picture of a fingerprint or some parts of it. Now we improve the great volume of fingerprint pictures by using this model which employs fractal for knowing the personality. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-10-24T12:22:15Z No. of bitstreams: 1 IJIMAI20101_3_5.pdf: 1034025 bytes, checksum: ff2123a1d8311dc8c80082bc697f8586 (MD5); Made available in DSpace on 2019-10-24T12:22:15Z (GMT). No. of bitstreams: 1 IJIMAI20101_3_5.pdf: 1034025 bytes, checksum: ff2123a1d8311dc8c80082bc697f8586 (MD5)

Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data You, Haowen;; Rumbe, George Accurate diagnostic detection of the cancerous cells in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Bayesian classifier and other Artificial neural network classifiers (Backpropagation, linear programming, Learning vector quantization, and K nearest neighborhood) on the Wisconsin breast cancer classification problem. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-10-24T11:34:54Z No. of bitstreams: 1 IJIMAI20101_3_1.pdf: 359090 bytes, checksum: a3bd9322980bda310a2cad9b4d6ddde1 (MD5); Made available in DSpace on 2019-10-24T11:34:54Z (GMT). No. of bitstreams: 1 IJIMAI20101_3_1.pdf: 359090 bytes, checksum: a3bd9322980bda310a2cad9b4d6ddde1 (MD5)

A crystal ball made of agents

A crystal ball made of agents Castillo, J. M. This article presents an agent-based solution to model the opinions of an experts group with the aim of predicting possible future scenarios. The need to envision the future is not new; it has existed since the beginning of human-kind. What it is new is the applicable technology that is available in a specific period of time. It is not usual to find a critical social system which evolves according to predictable guidelines or tendencies. Because of that reason, technical prediction based on past and present data is not reliable. This paper includes the process description of eliciting information from a group of experts and a real case study. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-10-24T10:48:33Z No. of bitstreams: 1 IJIMAI20101_3_2.pdf: 405336 bytes, checksum: 2e20c9ec1eeb0fdd00abb65709c65def (MD5); Made available in DSpace on 2019-10-24T10:48:33Z (GMT). No. of bitstreams: 1 IJIMAI20101_3_2.pdf: 405336 bytes, checksum: 2e20c9ec1eeb0fdd00abb65709c65def (MD5)

Semantics of immersive web through its architectural structure and graphic primitives

Semantics of immersive web through its architectural structure and graphic primitives Bolivar, Holman; Pacheco, Alexander; González-Crespo, Rubén Currently, practices and tools for computer-aided three-dimensional design, do not allow the semantic description of objects constructed in some cases specified notations as handling layers, or labeling of each development itself. The lack of a standard for the description of the elements represents a major drawback for using advanced three-dimensional environments such as the automation of search and construction processes that require semantic knowledge of its elements. This project proposes the development the semantic composition from the hierarchy of three-dimensional visualization of graphics primitives used to construct three-dimensional objects, taking into account the geometric composition architecture of standard 19775-1 of the International Electrotechnical Commission of the International Organization for Standardization For the development of semantic composition use the methodology methontology proposed by the Universidad Politécnica de Madrid, because it allows the construction of ontologies about specific domains, limiting the domain by defining classes and subclasses, relationships and the generation of instances a framework for resource description on web ontology language. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-10-24T10:36:20Z No. of bitstreams: 1 IJIMAI20101_3_8.pdf: 526691 bytes, checksum: f9853578c197ca7ae4dbf6e5e7cc72b4 (MD5); Made available in DSpace on 2019-10-24T10:36:20Z (GMT). No. of bitstreams: 1 IJIMAI20101_3_8.pdf: 526691 bytes, checksum: f9853578c197ca7ae4dbf6e5e7cc72b4 (MD5)

Social and Cultural Dimensions of Digital Inclusions

Social and Cultural Dimensions of Digital Inclusions Chulilla-Cano, J. L. The state of Internet Adoption Curve in 2011 for the developed countries reveals an apparently optimistic picture: the majority of European and North American populations have adopted main online tools and resources. However, as access of the majority of these populations doesn’t mean universal access, we review some of the main proposals about Digital Divide and the use of EU Digital inclusion perspective in order to focus on the main obstacles for universal access to Internet. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-10-24T10:04:52Z No. of bitstreams: 1 IJIMAI20101_3_3.pdf: 308069 bytes, checksum: 5e7eaa43dee11d531e5e0974e0858e79 (MD5); Made available in DSpace on 2019-10-24T10:04:52Z (GMT). No. of bitstreams: 1 IJIMAI20101_3_3.pdf: 308069 bytes, checksum: 5e7eaa43dee11d531e5e0974e0858e79 (MD5)

Modeling and Comparison Study of Modules in Open Source LMS Platforms with Cmapstool

Modeling and Comparison Study of Modules in Open Source LMS Platforms with Cmapstool Montenegro-Marin, Carlos Enrique; Cueva-Lovelle, Juan Manuel; Sanjuan, Oscar; Gaona-García, Paulo Alonso This work is part of a first phase of the project “DOMAIN SPECIFIC MODELING FOR THE LEARNING OBJECTS BUILD PLATFORM-INDEPENDENT " and seeks to make a comparison between Open Source LMS to get a first approximation of common modules them, and then start building the ontology compatible with all LMS studied, for that reason this paper is organized as follows: 1.Select Tools to work. 2. Contextualization of LMS tools to work. 3. Leaning Objects. 4. Structure of the LMS. 5. LMS ratings. 6. Creating the map of knowledge for each LMS. 7. Comparison between the LMS modules modeled and 8. Conclusions. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-10-23T12:44:56Z No. of bitstreams: 1 IJIMAI20101_3_7.pdf: 761762 bytes, checksum: 252cd98d562df47bd7fc583fee95e102 (MD5); Made available in DSpace on 2019-10-23T12:44:56Z (GMT). No. of bitstreams: 1 IJIMAI20101_3_7.pdf: 761762 bytes, checksum: 252cd98d562df47bd7fc583fee95e102 (MD5)

Collaborative Geographic Information Systems for Business Intelligence

Collaborative Geographic Information Systems for Business Intelligence Ramirez, Juan José This paper shows a number of sceneries where information (specifically, geographical-related information) is lost because there is no method for storing or sharing it. This research has been done with the aim to solve those scenery problems in a general way, by means of a geographical information system that can store geographical-related information and publish it in order to avoid loss of information and enabling geographical information sharing Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-30T11:12:46Z No. of bitstreams: 1 IJIMAI1-2-16.pdf: 199490 bytes, checksum: c3b7f7f43e6437e74ea5c75a2893058f (MD5); Made available in DSpace on 2019-09-30T11:12:46Z (GMT). No. of bitstreams: 1 IJIMAI1-2-16.pdf: 199490 bytes, checksum: c3b7f7f43e6437e74ea5c75a2893058f (MD5)

Route planning algorithms: Planific@ Project

Route planning algorithms: Planific@ Project Martín García, Carlos; Martín Ortega, Gonzalo Planific@ is a route planning project for the city of Madrid (Spain). Its main aim is to develop an intelligence system capable of routing people from one place in the city to any other using the public transport. In order to do this, it is necessary to take into account such things as: time, traffic, user preferences, etc. Before beginning to design the project is necessary to make a comprehensive study of the variety of main known route planning algorithms suitable to be used in this project Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-30T10:21:37Z No. of bitstreams: 1 IJIMAI1-2-11.pdf: 1087183 bytes, checksum: 35681f3a3d69d15e6c4063daf37424da (MD5); Made available in DSpace on 2019-09-30T10:21:37Z (GMT). No. of bitstreams: 1 IJIMAI1-2-11.pdf: 1087183 bytes, checksum: 35681f3a3d69d15e6c4063daf37424da (MD5)

Business Process Re-engineering in Saudi Arabia: A Survey of Understanding and Attitudes

Business Process Re-engineering in Saudi Arabia: A Survey of Understanding and Attitudes Rahali, Essam; Chaczko, Zenon; Agbinya, Johnson; Chiu, Christopher This survey was conducted in the Kingdom of Saudi Arabia (KSA) to investigate the level of awareness of BPR. Respondents (customers, employees, and Managers) had different educational backgrounds and were from private and public sectors. Findings of the study indicate a general awareness of BPR in KSA Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-30T09:51:11Z No. of bitstreams: 1 IJIMAI1-2-6.pdf: 461452 bytes, checksum: 835058dfff5a05a1e99e0a8e37b57c79 (MD5); Made available in DSpace on 2019-09-30T09:51:11Z (GMT). No. of bitstreams: 1 IJIMAI1-2-6.pdf: 461452 bytes, checksum: 835058dfff5a05a1e99e0a8e37b57c79 (MD5)

Ontology of a scene based on Java 3D architecture

Ontology of a scene based on Java 3D architecture Bolívar Barón, Holman; González-Crespo, Rubén; Sanjuán Martínez, Óscar The present article seeks to make an approach to the class hierarchy of a scene built with the architecture Java 3D, to develop an ontology of a scene as from the semantic essential components for the semantic structuring of the Web3D. Java was selected because the language recommended by the W3C Consortium for the Development of the Web3D oriented applications as from X3D standard is Xj3D which compositionof their Schemas is based the architecture of Java3D In first instance identifies the domain and scope of the ontology, defining classes and subclasses that comprise from Java3D architecture and the essential elements of a scene, as its point of origin, the field of rotation, translation The limitation of the scene and the definition of shaders, then define the slots that are declared in RDF as a framework for describing the properties of the classes established from identifying the domain and range of each class, then develops composition of the OWL ontology on SWOOP Finally, be perform instantiations of the ontology building for a Iconosphere object as from class expressions defined. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-30T09:10:51Z No. of bitstreams: 1 IJIMAI1-2-3.pdf: 324262 bytes, checksum: d697c24d7ab2f2a1360618d9230197f7 (MD5); Made available in DSpace on 2019-09-30T09:10:51Z (GMT). No. of bitstreams: 1 IJIMAI1-2-3.pdf: 324262 bytes, checksum: d697c24d7ab2f2a1360618d9230197f7 (MD5)

WoT model for authenticity contents in virtual learning platforms

WoT model for authenticity contents in virtual learning platforms Gaona-García, Paulo Alonso; Soto-Carrión, Jesús The following research proposal seeks to bring a model of security software on virtual learning platforms LCMS under all SCORM specifications to ensure the authenticity of content created under concepts of digital signature and identification of protocols and mechanisms to ensure such activities. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-27T12:38:53Z No. of bitstreams: 1 IJIMAI1-2-13.pdf: 698948 bytes, checksum: ce01562863f13eaee02a1e66b6c00f9b (MD5); Made available in DSpace on 2019-09-27T12:38:53Z (GMT). No. of bitstreams: 1 IJIMAI1-2-13.pdf: 698948 bytes, checksum: ce01562863f13eaee02a1e66b6c00f9b (MD5)

Prototype of assignment intelligent adaptive of service providers inside of ESB with data mining

Prototype of assignment intelligent adaptive of service providers inside of ESB with data mining Castaño, Andrés Paolo The SOA philosophy can address new business challenges, become more competitive and provide integrated information systems. In addition, technologies such as BPM, BAM and Web Services are essential complements to SOA. This work aims to use several of these technologies integrated into a single application that will allow in a phase of a previously defined business process, to perform the analysis of input suppliers to the company through the generation of a decision tree using embedded code of the free tool Weka for data mining in order to feedback the business process and evaluate these results to improve the process. For the realization of this prototype we worked with the jBPM suite, the API from Weka to get the J48 algorithm, the postgresql database, the format for data exchange JSON and the web service. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-27T12:20:29Z No. of bitstreams: 1 IJIMAI1-2-7.pdf: 545152 bytes, checksum: c71b6d4c9e5740ca270567833ae48682 (MD5); Made available in DSpace on 2019-09-27T12:20:29Z (GMT). No. of bitstreams: 1 IJIMAI1-2-7.pdf: 545152 bytes, checksum: c71b6d4c9e5740ca270567833ae48682 (MD5)

Using Recommendation System for E-learning Environments at degree level

Using Recommendation System for E-learning Environments at degree level Sanjuán Martínez, Óscar; Pelayo García-Bustelo, B. Cristina; González-Crespo, Rubén; Torres Franco, Enrique Nowadays, new technologies and the fast growth of the Internet have made access to information easier for all kind of people, raising new challenges to education when using Internet as a medium. One of the best examples is how to guide students in their learning processes. The need to look for guidance from their teachers or other companions that many Internet users experience when endeavoring to choose their readings, exercises o practices is a very common reality. In order to cater for this need many different information and recommendation strategies have been developed. Recommendation Systems is one of these. Recommendation Systems try to help the user, presenting him those objects he could be more interested in, based on his known preferences or on those of other users with similar characteristics.This document tries to present the current situation with regards to Recommendation Systems and their application on distance education over the Internet. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-27T11:38:21Z No. of bitstreams: 1 IJIMAI1-2-12.pdf: 263181 bytes, checksum: 8a0167e783556b8b2ae4971dfa81a34b (MD5); Made available in DSpace on 2019-09-27T11:38:21Z (GMT). No. of bitstreams: 1 IJIMAI1-2-12.pdf: 263181 bytes, checksum: 8a0167e783556b8b2ae4971dfa81a34b (MD5)

Security Guidelines for the Development of Accessible Web Applications through the implementation of intelligent systems

Security Guidelines for the Development of Accessible Web Applications through the implementation of intelligent systems Núñez Valdez, Edward Rolando; Sanjuán Martínez, Óscar; García Fernández, Gloria; Joyanes Aguilar, Luis; Cuevas Lovelle, Juan Ml. Due to the significant increase in threats, attacks and vulnerabilities that affect the Web in recent years has resulted the development and implementation of tools and methods to ensure security measures in the privacy, confidentiality and data integrity of users and businesses. Under certain circumstances, despite the implementation of these tools do not always get the flow of information which is passed in a secure manner. Many of these security tools and methods cannot be accessed by people who have disabilities or assistive technologies which enable people to access the Web efficiently. Among these security tools that are not accessible are the virtual keyboard, the CAPTCHA and other technologies that help to some extent to ensure safety on the Internet and are used in certain measures to combat malicious code and attacks that have been increased in recent times on the Web. Through the implementation of intelligent systems can detect, recover and receive information on the characteristics and properties of the different tools and hardware devices or software with which the user is accessing a web application and through analysis and interpretation of these intelligent systems can infer and automatically adjust the characteristics necessary to have these tools to be accessible by anyone regardless of disability or navigation context. This paper defines a set of guidelines and specific features that should have the security tools and methods to ensure the Web accessibility through the implementation of intelligent systems. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-27T10:31:35Z No. of bitstreams: 1 IJIMAI1-2-14.pdf: 256695 bytes, checksum: cc702e83ed938fc3d342d4e052ace51b (MD5); Made available in DSpace on 2019-09-27T10:31:35Z (GMT). No. of bitstreams: 1 IJIMAI1-2-14.pdf: 256695 bytes, checksum: cc702e83ed938fc3d342d4e052ace51b (MD5)

SALCER´s Project

SALCER´s Project Espinós, Bosco; Pérez, Carlos SALCER (in Spanish: Sistema de Asesoramiento y Localización de Centrales de EnergíaRenovables) could be translated as Counseling and Location of Renewable Energy Power Station´s System. Its objective is to develop a system capable of finding the most suitable place for the construction of renewable power stations, taking into account such things as: budget, topography, amount of energy needed, among others. The most relevant aims of the project are: study of a certain variety of renewable energy technologies, designing an accurate topology system, restraining decisions to demand forecasts and finally performance of an energy plan for a specific region. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-27T09:47:56Z No. of bitstreams: 1 IJIMAI1-2-8.pdf: 868442 bytes, checksum: 1513f19d55fc2c6fc2cee9ec91b08900 (MD5); Made available in DSpace on 2019-09-27T09:47:56Z (GMT). No. of bitstreams: 1 IJIMAI1-2-8.pdf: 868442 bytes, checksum: 1513f19d55fc2c6fc2cee9ec91b08900 (MD5)

Mapping Persian Words to WordNet Synsets

Mapping Persian Words to WordNet Synsets Dehkharghani, Rahim; Shamsfard, Mehrnoush Lexical ontologies are one of the main resources for developing natural language processing and semantic web applications. Mapping lexical ontologies of different languages is very important for inter-lingual tasks. On the other hand mapping approaches can be implied to build lexical ontologies for a new language based on pre-existing resources of other languages. In this paper we propose a semantic approach for mapping Persian words to Princeton WordNet Synsets. As there is no lexical ontology for Persian, our approach helps not only in building one for this language but also enables semantic web applications on Persian documents. To do the mapping, we calculate the similarity of Persian words and English synsets using their features such as super-classes and subclasses, domain and related words. Our approach is an improvement of an existing one applying in a new domain, which increases the recall noticeably. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-27T09:35:33Z No. of bitstreams: 1 IJIMAI1-2-2.pdf: 789896 bytes, checksum: 13815ada453cbd7f7af59e8d38b469f0 (MD5); Made available in DSpace on 2019-09-27T09:35:33Z (GMT). No. of bitstreams: 1 IJIMAI1-2-2.pdf: 789896 bytes, checksum: 13815ada453cbd7f7af59e8d38b469f0 (MD5)

Automated code generation support for BI with MDA TALISMAN

Automated code generation support for BI with MDA TALISMAN García-Díaz, Vicente; Fernández-Fernández, Héctor; Palacios-González, Elías; Pelayo García-Bustelo, B. Cristina; Sanjuán Martínez, Óscar; Cueva Lovelle, Juan Manuel Model Driven Engineering (MDE) is gaining ever more strength due to the fact that with MDE the software development can be much more productive and this is the way to go closer to real software industrialization. With MDA TALISMAN, we have succeeded in creating complex software solutions for food traceability adapted to different customers, ready to be deployed. We rely on the approach to MDE most extended at present, MDA (Model-Driven Development) but as we shall see, we also use the main pillars that support the Software Factories, The proposal from Microsoft to MDE. Besides, in this paper we present five cases of success with MDA TALISMAN. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-27T09:16:48Z No. of bitstreams: 1 IJIMAI1-2-15.pdf: 501872 bytes, checksum: 8f5e3265bc18789641246aab63209d24 (MD5); Made available in DSpace on 2019-09-27T09:16:48Z (GMT). No. of bitstreams: 1 IJIMAI1-2-15.pdf: 501872 bytes, checksum: 8f5e3265bc18789641246aab63209d24 (MD5)

Uncoolness factor of collaborative Web Mining Tools (WMT)

Uncoolness factor of collaborative Web Mining Tools (WMT) Chulilla, Juan Luis; Azagra, Pilar The recent development of social mining is a useful and direct analogy to talking about the less visible part of the adoption of successive waves of social software. The striking fact of visibility decrease as each type of social software matures should be taken into account for any comprehensive analysis of the relation between collectives and Internet technologies. One of the main results of this relation is the social data mining of Internet, which both gives sense to virtual communities and produces contents via feedback. We are just at the beginning of the adoption of new ways of social data mining, which will be significant when grow mature and become invisible. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-23T12:34:11Z No. of bitstreams: 1 IJIMAI1-2-9.pdf: 43438 bytes, checksum: bf26ec862bec54ad180df037922d2043 (MD5); Made available in DSpace on 2019-09-23T12:34:11Z (GMT). No. of bitstreams: 1 IJIMAI1-2-9.pdf: 43438 bytes, checksum: bf26ec862bec54ad180df037922d2043 (MD5)

A Mix Model of Discounted Cash-Flow and OWA Operators for Strategic Valuation

A Mix Model of Discounted Cash-Flow and OWA Operators for Strategic Valuation Doña, Jesús M.; la Red, David L.; Peláez, José I. The stock market volatility and the actual stock Exchange activity have increased the need of counting with effective methods on the part of financial analysts to achieve a division in relation to the investment actions, being also growing the demand of methodological instruments that reduce and minimize the risks and uncertainty when valuating financial actives and companies. These systems not only must use quantitative information but the inclusion of qualitative information must also bear heavily on them, as an improvement element in the adjustment of these valuating methods, with the aim of throwing a more well-conceived or less mistaken decision. In this work, the use of Discounted Cash-Flow model is proposed, with quantitative information together with the OWA operators as an inclusion method of ualitative information in the traditional valuating models, with the aim of generating an strategic valuating system which allows to develop more agreed and less mistaken valuations. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-23T12:21:40Z No. of bitstreams: 1 IJIMAI1-2-4.pdf: 336581 bytes, checksum: f818a96708d5b6670d55315ef5030b9c (MD5); Made available in DSpace on 2019-09-23T12:21:40Z (GMT). No. of bitstreams: 1 IJIMAI1-2-4.pdf: 336581 bytes, checksum: f818a96708d5b6670d55315ef5030b9c (MD5)

PredicForex. A tool for a reliable market. Playing with currencies

PredicForex. A tool for a reliable market. Playing with currencies Aguilera Collar, J.; González-Cebrián Toba, R.; Cortés Velasco, C. The Forex market is a very interesting market. Finding a suitable tool to forecast currency behavior will be of great interest. It is almost impossible to find a 100 % reliable tool. This market is like any other one, unpredictable. However we developed a very interesting tool that makes use of WebCrawler, data mining and web services to offer and forecast an advice to any user or broker. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-20T12:54:11Z No. of bitstreams: 1 IJIMAI1-2-10.pdf: 329672 bytes, checksum: 16f9939599316341158ac478626de3f5 (MD5); Made available in DSpace on 2019-09-20T12:54:11Z (GMT). No. of bitstreams: 1 IJIMAI1-2-10.pdf: 329672 bytes, checksum: 16f9939599316341158ac478626de3f5 (MD5)

Developing a Business Application with BPM and MDE

Developing a Business Application with BPM and MDE Fernández-Fernández, Héctor; Palacios-González, E.; García-Díaz, Vicente; Pelayo García-Bustelo, B. Cristina; Sanjuán Martínez, Óscar; Cueva Lovelle, Juan Manuel In this paper we have designed an architecture for the generation of a business application, that allows to business users to adapt their processes to the constant change. At the moment all the architectures based to a great extent on SOA allow to modify the processes in a short period of time, but we go beyond and give the possibility to the business user of modifying their processes. To design this architecture, we rely on the fundamental use of two technologies: BPM (Business Process Modeling) and MDE (Model Driven Engineering). Inside these technologies we focus on the creation of a business process notation extended from BPMN that is agile, easy to learn and design, and capable to provide semantic information about the process. Therefore this notation allows business process to modify their processes to achieve the proposed goal. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-20T12:25:04Z No. of bitstreams: 1 IJIMAI1-2-5.pdf: 487051 bytes, checksum: f6ee44c63901934e23b1f9b2b887c76d (MD5); Made available in DSpace on 2019-09-20T12:25:04Z (GMT). No. of bitstreams: 1 IJIMAI1-2-5.pdf: 487051 bytes, checksum: f6ee44c63901934e23b1f9b2b887c76d (MD5)

Prospecting the future with AI

Prospecting the future with AI Miguel Castillo, Jose; Cortes, Conchi; Gonzalez, Julian; Benito, Armando If we were able to foresee the future, we could be prepared to reduce the impact of bad situations as well as getting the most of profiting periods. Our world is a dynamic system that evolves as time goes by. The number of variables that can influence in future situations outnumbers our capacity of prediction at a first glance. This article will show an alternative way to foresee potential future scenarios based on human experts’ opinion, what can be considered as a knowledge modeling tool. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-20T12:00:14Z No. of bitstreams: 1 IJIMAI1-2-1.pdf: 457589 bytes, checksum: 5552da678a871746e4fa181b223bf5e1 (MD5); Made available in DSpace on 2019-09-20T12:00:14Z (GMT). No. of bitstreams: 1 IJIMAI1-2-1.pdf: 457589 bytes, checksum: 5552da678a871746e4fa181b223bf5e1 (MD5)

General purpose MDE tools

General purpose MDE tools Palacios-González, Elías; Fernández-Fernández, Héctor; García-Díaz, Vicente; Pelayo García-Bustelo, B. Cristina; Cueva Lovelle, Juan Manuel; Sanjuán Martínez, Óscar MDE paradigm promises to release developers from writing code. The basis of this paradigm consists in working at such a level of abstraction that will make it easyer for analysts to detail the project to be undertaken. Using the model described by analysts, software tools will do the rest of the task, generating software that will comply with customer's defined requirements. The purpose of this study is to compare general purpose tools available right now that enable to put in practice the principles of this paradigm and aimed at generating a wide variety of applications composed by interactive multimedia and artificial intelligence components. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-20T11:31:19Z No. of bitstreams: 1 IJIMAI1-1-12.pdf: 3751311 bytes, checksum: 298038ce622c85ddfdc78ed6a05caffc (MD5); Made available in DSpace on 2019-09-20T11:31:19Z (GMT). No. of bitstreams: 1 IJIMAI1-1-12.pdf: 3751311 bytes, checksum: 298038ce622c85ddfdc78ed6a05caffc (MD5)

Software fires detection and extinction for forest

Software fires detection and extinction for forest Utande González de la Higuera, Nuria; García Seco, Juan Carlos This article shows the most usual fire detection and forest extinction application technologies at present. We will see all different methods used by these applications that can be found in the Market and some examples. Also, some basic questions about the most influent parameters when a fire must be extinct are shown. Finally, after having shown all the technologies, we will build a model about an intelligent system which not only detects, but also extinguish wildfires Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-20T09:02:14Z No. of bitstreams: 1 IJIMAI1-1-6.pdf: 3775660 bytes, checksum: 862139236c31b644bf7ea5c0237f7ed7 (MD5); Made available in DSpace on 2019-09-20T09:02:14Z (GMT). No. of bitstreams: 1 IJIMAI1-1-6.pdf: 3775660 bytes, checksum: 862139236c31b644bf7ea5c0237f7ed7 (MD5)

Iris recognition using the JAVAVis Library

Iris recognition using the JAVAVis Library Larrumbe Hidalgo, V.; Martin García, L.; Taboada Lorenzo, M. This project has been created to develop a biometric identification system through a man’s iris using a computer to perform the processing of the pictures. To develop this application, and to differentiate the project from others who have already implemented, we have used the image processing library JAVAVis and JAVA as a programming language. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-20T08:11:10Z No. of bitstreams: 1 IJIMAI1-1-7.pdf: 3755097 bytes, checksum: 6ee8ee16e1702843764a1190d3e769b2 (MD5); Made available in DSpace on 2019-09-20T08:11:10Z (GMT). No. of bitstreams: 1 IJIMAI1-1-7.pdf: 3755097 bytes, checksum: 6ee8ee16e1702843764a1190d3e769b2 (MD5)

ADT-3D Tumor Detection Assistant in 3D

ADT-3D Tumor Detection Assistant in 3D García Castellot, Jaime; Lazcano Bello, Jaime The present document describes ADT-3D (Three-Dimensional Tumor Detector Assistant), a prototype application developed to assist doctors diagnose, detect and locate tumors in the brain by using CT scan. The reader may find on this document an introduction to tumor detection; ADT-3D main goals; development details; description of the product; motivation for its development; result’s study; and areas of applicability. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-19T11:40:57Z No. of bitstreams: 1 IJIMAI1-1-2.pdf: 3816642 bytes, checksum: 1c415831be2fb8fc6a52914fe8dd9ea3 (MD5); Made available in DSpace on 2019-09-19T11:40:57Z (GMT). No. of bitstreams: 1 IJIMAI1-1-2.pdf: 3816642 bytes, checksum: 1c415831be2fb8fc6a52914fe8dd9ea3 (MD5)

Computer-aided diagnosis of pancreatic and lung cancer

Computer-aided diagnosis of pancreatic and lung cancer Núñez Díaz, A. Álvaro; Lancho Tofé, B. Luis When we talk about cancer diagnosis the most important thing is early diagnosis to prevent cancer cells from spreading. We may also consider the high cost of diagnostic tests. Our approach seeks to address both problems. It uses a software based on Bayesian networks that simulates the causeeffect relationships and gets the chance of suffering a pancreatic cancer or lung cancer. This software would support doctors and save a lot of time and resources . Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-19T11:12:47Z No. of bitstreams: 1 IJIMAI1-1-3.pdf: 3794764 bytes, checksum: 383b2b9b752cbafa07428cc9dc9ed8de (MD5); Made available in DSpace on 2019-09-19T11:12:47Z (GMT). No. of bitstreams: 1 IJIMAI1-1-3.pdf: 3794764 bytes, checksum: 383b2b9b752cbafa07428cc9dc9ed8de (MD5)

AI Sport Forecast Software

AI Sport Forecast Software Cerezo Takahashi, Kiyomi This article aims to explain the development of an application whose function is to predict the results of different sporting encounters. To do this an analysis of the influential factors, algorithms and technology implemented, will be carried out. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-19T10:32:10Z No. of bitstreams: 1 IJIMAI1-1-10.pdf: 3789272 bytes, checksum: 80c5dc727b142758888e194396d4451c (MD5); Made available in DSpace on 2019-09-19T10:32:10Z (GMT). No. of bitstreams: 1 IJIMAI1-1-10.pdf: 3789272 bytes, checksum: 80c5dc727b142758888e194396d4451c (MD5)

EvoWild: a demosimulator about wild life

EvoWild: a demosimulator about wild life Mey Rodríguez, Macarena; Palacio Gayoso, Eduardo During the last years we can see how AI (Artificial Intelligence) is reappearing because of technological improvements. These improvements make possible the management of large groups of information with acceptable reply times.On the other hand, cost reductions in technology make possible that an investigation field like AI becomes to an inversion field closer to scale economies, that's why it'll be economically profitable to invert in this type of applications.One of the fastest consequences is the AI implantation in a big amount of devices of our environment, cell telephones, palms and of course, in the video game industry.This is the reason that took us to develop EvoWild, a simulation about wild life that has video game format and tools but at the same time implements AI algorithms like genetic algorithms and reasoning based in cases. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-19T09:05:39Z No. of bitstreams: 1 IJIMAI1-1-4.pdf: 3771345 bytes, checksum: 2a0c34e4083a4fd2f28d3ed09c63e06e (MD5); Made available in DSpace on 2019-09-19T09:05:39Z (GMT). No. of bitstreams: 1 IJIMAI1-1-4.pdf: 3771345 bytes, checksum: 2a0c34e4083a4fd2f28d3ed09c63e06e (MD5)

IMusic: a Bluetooth music application

IMusic: a Bluetooth music application Aguilera Manglano, Angel; Sierra Espín, Borja Nowadays, we can see a huge amount of customized services in lots of applications. A more customized experience get a real full satisfaction to the customers. The project was born with the aim of providing this kind of service. Thus a client takes part in the decision‐ making of the local songs, creating a playlist with songs ordered as the customer’s data preferences. The way is easy: a customer who enters the premises, install the software that local provides and then he has to indicate their customer likes and get it transmitted via Bluetooth. This information is received by the customer attention server and it generates a playlist with personalized songs. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-18T12:50:11Z No. of bitstreams: 1 IJIMAI1-1-11.pdf: 3809872 bytes, checksum: f11c4cae1d070d91e2c0de38c1242802 (MD5); Made available in DSpace on 2019-09-18T12:50:11Z (GMT). No. of bitstreams: 1 IJIMAI1-1-11.pdf: 3809872 bytes, checksum: f11c4cae1d070d91e2c0de38c1242802 (MD5)

Intelligent Garbage Classifier

Intelligent Garbage Classifier Salmador, Alvaro; Pérez Cid, Javier; Rodríguez Novelle, Ignacio IGC (Intelligent Garbage Classifier) is a system for visual classification and separation of solid waste products. Currently, an important part of the separation effort is based on manual work, from household separation to industrial waste management. Taking advantage of the technologies currently available, a system has been built that can analyze images from a camera and control a robot arm and conveyor belt to automatically separate different kinds of waste. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-09-18T11:36:40Z No. of bitstreams: 1 IJIMAI1-1-5.pdf: 3758928 bytes, checksum: fc481fbb53f42f03a92bb7320cd0a061 (MD5); Made available in DSpace on 2019-09-18T11:36:40Z (GMT). No. of bitstreams: 1 IJIMAI1-1-5.pdf: 3758928 bytes, checksum: fc481fbb53f42f03a92bb7320cd0a061 (MD5)

Social maturity of WWW and AI feedback: opportunities for an additional human revolution

Social maturity of WWW and AI feedback: opportunities for an additional human revolution Chulilla-Cano, Juan Luis; Azagra Albericio, Pilar In these days, Internet Adoption Curve in developed countries belongs to an interval between early majority and late majority. There is already a significant population profile which can be defined as 'digital natives', more or less isolated from their 'digital immigrants' thanks to the abyss defined by the decisive integration of Internet in digital natives' daily lifes. This situation is actually significant if one takes in mind that digital persona of digital natives is much more than a mirror of, let's say, actual persona and both belongs to a new, multifaced entity still not well understood. Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2019-09-18T09:34:15Z No. of bitstreams: 1 IJIMAI1-1-1.pdf: 3724524 bytes, checksum: faf73ab04a82775073ccf96e0fe0d27d (MD5); Made available in DSpace on 2019-09-18T09:34:15Z (GMT). No. of bitstreams: 1 IJIMAI1-1-1.pdf: 3724524 bytes, checksum: faf73ab04a82775073ccf96e0fe0d27d (MD5)

DSMUS 1.0

DSMUS 1.0 Redondo, Sergio; Sainz, Héctor; Chacón, Arucas This paper shows how we have developed a video game for Nintendo DS in order to play “Mus”, one of the most popular Spanish card games, thereby we considered designing a video game would be an interesting way of applying our acquired knowledge about artificial intelligence. Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2019-09-16T13:56:30Z No. of bitstreams: 1 DSMUS_1-0.pdf: 3763849 bytes, checksum: c9feb8ab9353a1fd5039cb4c86ed6be3 (MD5); Made available in DSpace on 2019-09-16T13:56:30Z (GMT). No. of bitstreams: 1 DSMUS_1-0.pdf: 3763849 bytes, checksum: c9feb8ab9353a1fd5039cb4c86ed6be3 (MD5)

Semi-Automated Correction Tools for Mathematics-Based Exercises in MOOC Environments

Semi-Automated Correction Tools for Mathematics-Based Exercises in MOOC Environments Corbi, Alberto ; Burgos, Daniel Massive Open Online Courses (MOOCs) allow the participation of hundreds of students who are interested in a wide range of areas. Given the huge attainable enrollment rate, it is almost impossible to suggest complex homework to students and have it carefully corrected and reviewed by a tutor or assistant professor. In this paper, we present a software framework that aims at assisting teachers in MOOCs during correction tasks related to exercises in mathematics and topics with some degree of mathematical content. In this spirit, our proposal might suit not only maths, but also physics and technical subjects. As a test experience, we apply it to 300+ physics homework bulletins from 80+ students. Results show our solution can prove very useful in guiding assistant teachers during correction shifts and is able to mitigate the time devoted to this type of activities. Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2017-10-31T11:17:58Z No. of bitstreams: 0; Made available in DSpace on 2017-10-31T11:17:58Z (GMT). No. of bitstreams: 0

Detection of Adverse Reaction to Drugs in Elderly Patients through Predictive Modeling

Detection of Adverse Reaction to Drugs in Elderly Patients through Predictive Modeling San Miguel Carrasco, Rafael Geriatrics Medicine constitutes a clinical research field in which data analytics, particularly predictive modeling, can deliver compelling, reliable and long-lasting benefits, as well as non-intuitive clinical insights and net new knowledge. The research work described in this paper leverages predictive modeling to uncover new insights related to adverse reaction to drugs in elderly patients. The differentiation factor that sets this research exercise apart from traditional clinical research is the fact that it was not designed by formulating a particular hypothesis to be validated. Instead, it was data-centric, with data being mined to discover relationships or correlations among variables. Regression techniques were systematically applied to data through multiple iterations and under different configurations. The obtained results after the process was completed are explained and discussed next. Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2017-10-23T16:13:43Z No. of bitstreams: 0; Made available in DSpace on 2017-10-23T16:13:43Z (GMT). No. of bitstreams: 0

Legal Effects of Link Sharing in Social Networks

Legal Effects of Link Sharing in Social Networks Gil López, Eugenio; Castillo Sanz, Andrés G Knowledge sharing among individuals has changed deeply with the advent of social networks in the environment of Web 2.0. Every user has the possibility of publishing what he or she deems of interest for their audience, regardless of the origin or authorship of the piece of knowledge. It is generally accepted that as the user is sharing a link to a document or video, for example, without getting paid for it, there is no point in worrying about the rights of the original author. It seems that the concepts of authorship and originality is about to disappear as promised the structuralists fifty years ago. Nevertheless the legal system has not changed, nor have the economic interests concerned. This paper explores the last developments of the legal system concerning these issues Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2017-10-10T15:29:52Z No. of bitstreams: 0; Made available in DSpace on 2017-10-10T15:29:52Z (GMT). No. of bitstreams: 0

Maths: from distance to e-learning

Maths: from distance to e-learning Álvarez, D.; Moreno, D.; Orduna, P.; Pascual López, Virginia; San Vicente, F. J. New technological progress and especially the use of Internet have implied a new paradigm on education, and nowadays one of its most prominent features is the rise of a new approach based on an instruction beyond the solid walls of schools and characterized by mobility. That is, e-learning. However, its origins and concept can be traced in time. This paper, focused on mathematics, deals with its evolution, antecedents and present status. Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2017-10-09T16:04:11Z No. of bitstreams: 0; Made available in DSpace on 2017-10-09T16:04:11Z (GMT). No. of bitstreams: 0

ICTs and School Education

ICTs and School Education Aris, Nuria ; Orcos, Lara Nowadays, there exist lots of ICTs that teachers use as teaching tools. In this work, we introduce the theoretical context of the study of using ICTs in school education, then we present the method that will be used in order to achieve our goals. This work constitutes the groundwork to continue the study of ICT and its use in teaching. Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2017-10-09T15:54:28Z No. of bitstreams: 0; Made available in DSpace on 2017-10-09T15:54:28Z (GMT). No. of bitstreams: 0

UX of social network Edmodo in undergraduate engineering students

UX of social network Edmodo in undergraduate engineering students Gómez, Angélica; Magreñán, Á. Alberto ; Orcos, Lara The main objective of this research is to describe the use that students make of an academic SNS (social network service) and detail the relationship between socio-demographic and academic factors associated with the use of EDMODO and the perception of the contribution to the acquisition of skills for the future career. In the analysis of user experience, participants positively evaluated EDMODO and found that the level of satisfaction is positively associated with the academic results obtained, and negatively with perceived usefulness in terms of the impact on their grades. Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2017-10-09T15:34:43Z No. of bitstreams: 0; Made available in DSpace on 2017-10-09T15:34:43Z (GMT). No. of bitstreams: 0

SRec and VAST: Visualizing Software with a Student-Centered Aim

SRec and VAST: Visualizing Software with a Student-Centered Aim Almeida-Martínez, Francisco Javier ; Pérez-Carrasco, Antonio This paper reviews software visualization focused on the educational environment. Software visualization is a very wide study field, so we have focused on two areas: recursion visualization and parsers' visualization. The paper contains a retrospective about what has been made on it, what lacks we have found and the solution provided by the authors: SRec and VAST, two software tools trying to make a significant difference between them and the software made before. Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2017-10-07T12:06:56Z No. of bitstreams: 0; Made available in DSpace on 2017-10-07T12:06:56Z (GMT). No. of bitstreams: 0

A Quantitative Justification to Dynamic Partial Replication of Web Contents through an Agent Architecture

A Quantitative Justification to Dynamic Partial Replication of Web Contents through an Agent Architecture Torres-Franco, Enrique; García, José Daniel; Sanjuán Martínez, Óscar; Joyanes Aguilar, Luis; González-Crespo, Rubén The most usual solution to improve the performance of a Web server is based on building a distributed architecture, where the Web server is offered from a set of nodes. The most widely distributed architecture is based on Web clusters including a Web switch. The Web switch is responsible for deciding which site's node must attend which request. When deciding where elements are stored the classical solution was to fully replicate all contents in every server node. However, partial replication may require a fraction of storage while offering the same level of reliability. In this paper we report a solution based on dynamic partial replication where the number of replicas for each file and its management is handled by an agent architecture. We compare our solution with full replication and with static partial replication both in terms of storage capacity consumption and service time. Our results show that our proposed solution provides equivalent performance with a better use of disk storage capacity. Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2017-10-07T12:01:36Z No. of bitstreams: 0; Made available in DSpace on 2017-10-07T12:01:36Z (GMT). No. of bitstreams: 0