https://reunir.unir.net/handle/123456789/19152 2026-03-15T11:24:16Z 2026-03-15T11:24:16Z Sánchez Canella, Fernando Pascual Espada, Jordán Cid Rico, Irene https://reunir.unir.net/handle/123456789/19202 2026-03-10T15:47:39Z

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)

Kollmorgen, Jessica Hinderks, Andreas Thomaschewski, Jörg https://reunir.unir.net/handle/123456789/19201 2026-03-10T15:44:06Z

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)

Tejero, Alberto Pisoni, Galena Lashkari, Ziba Habibi Rios Aguilar, Sergio https://reunir.unir.net/handle/123456789/19200 2026-03-10T15:39:48Z

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)

Martínez Núñez, Domingo López Hernández, Fernando Carlos Rainer Granados, J. Javier https://reunir.unir.net/handle/123456789/19198 2026-03-10T15:10:54Z

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)

Guo, Tiancun Zhou, Qiang Gao, Mingliang Jeon, Gwanggil Camacho, David https://reunir.unir.net/handle/123456789/19197 2026-03-10T15:01:51Z

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)

Suruliandi, A. Rayan, R. Ame Raja, S. P. https://reunir.unir.net/handle/123456789/19196 2026-03-10T14:57:49Z

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)

Carstensen, Simen Lin, Jerry Chun Wei https://reunir.unir.net/handle/123456789/19195 2026-03-10T14:48:57Z

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)

Zhang, Linhao Jin, Li Xu, Guangluan Li, Xiaoyu Sun, Xian Zhang, Zequn Zhang, Yanan https://reunir.unir.net/handle/123456789/19194 2026-03-10T14:44:20Z

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)

Paramasivam, Ramya Lavanya, K. Divakarachari, Parameshachari Bidare Camacho, David https://reunir.unir.net/handle/123456789/19192 2026-03-10T13:02:22Z

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)

Manoj, S. Oswalt Kumar, Abhishek Dubey, Ashutosh Kumar Ananth, J. P. https://reunir.unir.net/handle/123456789/19156 2026-03-09T16:56:05Z

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)

Diaz Pacheco, AngeL Álvarez Carmona, Miguel A. Rodríguez González, Ansel Y. Carlos, Hugo Aranda, Ramón https://reunir.unir.net/handle/123456789/19155 2026-03-09T16:52:08Z

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)

Alotaibi, Basmah K. Khan, Fakhri Alam Qawqzeh, Yousef Jeon, Gwanggil Camacho, David https://reunir.unir.net/handle/123456789/19154 2026-03-09T16:48:22Z

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)

García Martínez Eyre i Canals, Yamila https://reunir.unir.net/handle/123456789/19153 2026-03-09T16:43:57Z

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)