https://reunir.unir.net/handle/123456789/12954 Mon, 04 Nov 2024 13:41:48 GMT 2024-11-04T13:41:48Z https://reunir.unir.net/handle/123456789/12985 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) https://reunir.unir.net/handle/123456789/12985 https://reunir.unir.net/handle/123456789/12984 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) https://reunir.unir.net/handle/123456789/12984 https://reunir.unir.net/handle/123456789/12983 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) https://reunir.unir.net/handle/123456789/12983 https://reunir.unir.net/handle/123456789/12982 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) https://reunir.unir.net/handle/123456789/12982 https://reunir.unir.net/handle/123456789/12981 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) https://reunir.unir.net/handle/123456789/12981 https://reunir.unir.net/handle/123456789/12980 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) https://reunir.unir.net/handle/123456789/12980 https://reunir.unir.net/handle/123456789/12979 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) https://reunir.unir.net/handle/123456789/12979 https://reunir.unir.net/handle/123456789/12978 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) https://reunir.unir.net/handle/123456789/12978 https://reunir.unir.net/handle/123456789/12977 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) https://reunir.unir.net/handle/123456789/12977 https://reunir.unir.net/handle/123456789/12976 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) https://reunir.unir.net/handle/123456789/12976 https://reunir.unir.net/handle/123456789/12975 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) https://reunir.unir.net/handle/123456789/12975 https://reunir.unir.net/handle/123456789/12974 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) https://reunir.unir.net/handle/123456789/12974 https://reunir.unir.net/handle/123456789/12973 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) https://reunir.unir.net/handle/123456789/12973 https://reunir.unir.net/handle/123456789/12966 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) https://reunir.unir.net/handle/123456789/12966 https://reunir.unir.net/handle/123456789/12965 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) https://reunir.unir.net/handle/123456789/12965 https://reunir.unir.net/handle/123456789/12964 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) https://reunir.unir.net/handle/123456789/12964 https://reunir.unir.net/handle/123456789/12963 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) https://reunir.unir.net/handle/123456789/12963 https://reunir.unir.net/handle/123456789/12962 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) https://reunir.unir.net/handle/123456789/12962 https://reunir.unir.net/handle/123456789/12961 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) https://reunir.unir.net/handle/123456789/12961 https://reunir.unir.net/handle/123456789/12960 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) https://reunir.unir.net/handle/123456789/12960 https://reunir.unir.net/handle/123456789/12959 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) https://reunir.unir.net/handle/123456789/12959 https://reunir.unir.net/handle/123456789/12958 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) https://reunir.unir.net/handle/123456789/12958 https://reunir.unir.net/handle/123456789/12957 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) https://reunir.unir.net/handle/123456789/12957 https://reunir.unir.net/handle/123456789/12956 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) https://reunir.unir.net/handle/123456789/12956 https://reunir.unir.net/handle/123456789/12955 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) https://reunir.unir.net/handle/123456789/12955