https://reunir.unir.net/handle/123456789/19223 Wed, 11 Mar 2026 12:41:02 GMT 2026-03-11T12:41:02Z https://reunir.unir.net/handle/123456789/19242 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) https://reunir.unir.net/handle/123456789/19242 https://reunir.unir.net/handle/123456789/19239 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) https://reunir.unir.net/handle/123456789/19239 https://reunir.unir.net/handle/123456789/19238 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) https://reunir.unir.net/handle/123456789/19238 https://reunir.unir.net/handle/123456789/19237 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) https://reunir.unir.net/handle/123456789/19237 https://reunir.unir.net/handle/123456789/19236 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) https://reunir.unir.net/handle/123456789/19236 https://reunir.unir.net/handle/123456789/19234 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) https://reunir.unir.net/handle/123456789/19234 https://reunir.unir.net/handle/123456789/19233 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) https://reunir.unir.net/handle/123456789/19233 https://reunir.unir.net/handle/123456789/19232 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) https://reunir.unir.net/handle/123456789/19232 https://reunir.unir.net/handle/123456789/19231 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) https://reunir.unir.net/handle/123456789/19231 https://reunir.unir.net/handle/123456789/19229 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) https://reunir.unir.net/handle/123456789/19229 https://reunir.unir.net/handle/123456789/19228 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) https://reunir.unir.net/handle/123456789/19228 https://reunir.unir.net/handle/123456789/19227 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) https://reunir.unir.net/handle/123456789/19227 https://reunir.unir.net/handle/123456789/19226 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) https://reunir.unir.net/handle/123456789/19226 https://reunir.unir.net/handle/123456789/19225 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) https://reunir.unir.net/handle/123456789/19225 https://reunir.unir.net/handle/123456789/19224 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) https://reunir.unir.net/handle/123456789/19224