https://reunir.unir.net/handle/123456789/19204 2026-03-17T13:03:00Z https://reunir.unir.net/handle/123456789/19222 Evaluating Customer Segmentation Techniques in the Retail Sector Diyabi, Nur; Çakır, Duygu; Gül, Ömer Melih; Aytekin, Tevfik; Kadry, Seifedine In the current competitive corporate landscape, understanding client preferences and adapting marketing strategies accordingly has become crucial. This study evaluates the effectiveness of four machine learning algorithms (K-Means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Models (GMM), and Self-Organizing Maps (SOM)) for customer segmentation in the Turkish retail market. Two datasets were analyzed: a large-scale Turkish market sales dataset and a focused marketing campaign dataset. The research employed a comprehensive methodology encompassing data preparation, algorithm application, and performance evaluation using metrics such as the Calinski-Harabasz Index and Davies- Bouldin score. Results indicate that K-Means demonstrated superior performance in terms of interpretability and statistical validity. DBSCAN showed strengths in identifying non-spherical clusters, while GMM and SOM provided more granular segmentation. The findings offer actionable insights for Turkish retailers to optimize marketing strategies and enhance customer relationship management. This study contributes to the field of retail analytics by providing a methodological framework for evaluating customer segmentation techniques in specific market contexts. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T08:30:19Z No. of bitstreams: 1 Evaluating Customer Segmentation Techniques in the Retail Sector.pdf: 1344269 bytes, checksum: 24a95aafc7d1bc90e48f00971b2a85f4 (MD5); Made available in DSpace on 2026-03-11T08:30:19Z (GMT). No. of bitstreams: 1 Evaluating Customer Segmentation Techniques in the Retail Sector.pdf: 1344269 bytes, checksum: 24a95aafc7d1bc90e48f00971b2a85f4 (MD5) https://reunir.unir.net/handle/123456789/19221 A Multi-Session Evaluation of a Haptic Device in Normal and Critical Conditions: a Mars Analog Mission Manon, Julie; Vanderdonckt, Jean; Saint Guillain, Michael; Pletser, Vladimir; Wain, Cyril; Jacobs, Jean; Comein, Audrey; Drouet, Sirga; Meert, Julien; Sanchez Casla, Ignacio; Cartiaux, Olivier; Cornu, Olivier While visual interaction is typically evaluated as an instantaneous, one-shot activity that considers only a snapshot of factors, haptic interaction is more challenging to evaluate as it involves a continuous touch process evolving over time. To better understand how to evaluate haptic interaction, this paper performs a multisession evaluation of a haptic device to be used by astronauts in future lunar and Mars missions, based on eight factors. Three groups of two members (???? = 6 ) applied, either as operator or assistant, a newly developed external fixator (EZExFix) to fix a fracture of the tibial shaft. Astronauts had different levels of expertise, i.e., in anatomy, mechanical engineering, and without, and participated in eight timed runs. Among these eight matches, four sessions were conducted with different time frames and compared to a stress test, a reproduction of the experiment in very stressful conditions, and a session simulating critical conditions in an extra-vehicular activity. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T08:25:12Z No. of bitstreams: 1 A Multi-Session Evaluation of a Haptic Device in Normal and Critical Conditions.pdf: 877083 bytes, checksum: 85bc2ce8bad2a25e96029789a9e17eec (MD5); Made available in DSpace on 2026-03-11T08:25:12Z (GMT). No. of bitstreams: 1 A Multi-Session Evaluation of a Haptic Device in Normal and Critical Conditions.pdf: 877083 bytes, checksum: 85bc2ce8bad2a25e96029789a9e17eec (MD5) https://reunir.unir.net/handle/123456789/19220 Reliability of IBM’s Public Quantum Computers Pérez Antón, Raquel; Corbi, Alberto; López Sánchez, José Ignacio; Burgos, Daniel One of the challenges of the current ecosystem of quantum computers (QC) is the stabilization of the coherence associated with the entanglement of the states of their inner qubits. In this empirical study, we monitor the reliability of IBM’s public-access QCs network on a daily basis. Each of these state-of-the-art machines has a totally different qubit association, and this entails that for a given (same) input program, they may output a different set of probabilities for the assembly of results (including both the right and the wrong ones). Although we focus on the computing structure provided by the “Big Blue” company, our survey can be easily transferred to other currently available quantum mainframes. In more detail, we probe these quantum processors with an ad hoc designed computationally demanding quaternary search algorithm. As stated, this quantum program is executed every 24 hours (for nearly 100 days) and its goal is to put to the limit the operational capacity of this novel and genuine type of equipment. Next, we perform a comparative analysis of the obtained results according to the singularities of each computer and over the total number of executions. In addition, we subsequently apply (for 50 days) an improvement filtering to perform noise mitigation on the results obtained proposed by IBM. The Yorktown 5-qubit computer reaches noise filtering of up to 33% in one day, that is, a 90% confidence level is reached in the expected results. From our continuous and long-term tests, we derive that room still exists regarding the improvement of quantum calculators in order to guarantee enough confidence in the returned outcomes. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T08:16:47Z No. of bitstreams: 1 Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique.pdf: 1980156 bytes, checksum: 0aae57c42bf1ac2d4fbc69ed5fd20920 (MD5); Made available in DSpace on 2026-03-11T08:16:47Z (GMT). No. of bitstreams: 1 Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique.pdf: 1980156 bytes, checksum: 0aae57c42bf1ac2d4fbc69ed5fd20920 (MD5) https://reunir.unir.net/handle/123456789/19219 Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique Lakshmi, H. R.; Borra, Surekha With increasing copyright violation cases, watermarking of digital images is a very popular solution for securing online media content. Since some sensitive applications require image recovery after watermark extraction, reversible watermarking is widely preferred. This article introduces a Modified Quadratic Difference Expansion (MQDE) and fractal encryption-based reversible watermarking for securing the copyrights of images. First, fractal encryption is applied to watermarks using Tromino's L-shaped theorem to improve security. In addition, Cuckoo Search-Grey Wolf Optimization (CSGWO) is enforced on the cover image to optimize block allocation for inserting an encrypted watermark such that it greatly increases its invisibility. While the developed MQDE technique helps to improve coverage and visual quality, the novel data-driven distortion control unit ensures optimal performance. The suggested approach provides the highest level of protection when retrieving the secret image and original cover image without losing the essential information, apart from improving transparency and capacity without much tradeoff. The simulation results of this approach are superior to existing methods in terms of embedding capacity. With an average PSNR of 67 dB, the method shows good imperceptibility in comparison to other schemes. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T08:12:00Z No. of bitstreams: 1 Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique.pdf: 1980156 bytes, checksum: 0aae57c42bf1ac2d4fbc69ed5fd20920 (MD5); Made available in DSpace on 2026-03-11T08:12:00Z (GMT). No. of bitstreams: 1 Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique.pdf: 1980156 bytes, checksum: 0aae57c42bf1ac2d4fbc69ed5fd20920 (MD5) https://reunir.unir.net/handle/123456789/19218 Simulations for the Precise Modeling of Exercises Including Time, Grades and Number of Attempts Jiménez-Macías, Alberto; Muñoz-Merino, Pedro J.; Delgado Kloos, Carlos Students’ interactions with exercises can reveal interesting features that can be used to redesign or effectively use the exercises during the learning process. The precise modeling of exercises includes how grades can evolve, depending on the number of attempts and time spent on the exercises. A missing aspect is how a precise relationship among grades, number of attempts, and time spent can be inferred from student interactions with exercises using machine learning methods, and how it differs depending on different factors. In this study, we analyzed the application of different machine-learning methods for modeling different scenarios by varying the probability of answering correctly, dataset sizes, and distributions. The results show that the model converged when the probability of random guessing was low. For exercises with an average of 2 attempts, the model converged to 200 interactions. However, increasing the number of interactions beyond 200 does not affect the accuracy of the model. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T08:08:17Z No. of bitstreams: 1 Simulations for the Precise Modeling of Exercises Including Time, Grades and Number of Attempts.pdf: 730266 bytes, checksum: c4f311227cef1c69af7b2270a0dabecc (MD5); Made available in DSpace on 2026-03-11T08:08:17Z (GMT). No. of bitstreams: 1 Simulations for the Precise Modeling of Exercises Including Time, Grades and Number of Attempts.pdf: 730266 bytes, checksum: c4f311227cef1c69af7b2270a0dabecc (MD5) https://reunir.unir.net/handle/123456789/19217 Learning Analytics Icons: Easy Comprehension of Data Treatment Amo-Filva, Daniel; Alier, Marc; Fonseca, David; Garcia-Peñalvo, Francisco José; Casañ, María José The Learning Analytics approach adopted in education implies the gathering and processing of sensitive information and the generation of student profiles, which may have direct or indirect dire consequences for the students. The Educational institutions must manage this data processing according to the General Data Protection Regulation, respecting its principles of fairness when it comes to information gathering and processing. This implies that the students must be well informed and give explicit consent before their information is gathered and processed. The GDPR propose the usage of recognizable standardized icons to facilitate a general understanding and awareness of how personal data is deemed to be processed in each application context, like an online course. This paper presents a project that aims to provide a set of icons to inform about the treatment of educational data in the Learning Analytics processes and a survey about the student's comprehension of the icons, their meaning, and implications for their privacy and confidentiality. The result presented is a set of icons ready to be integrated into educational environments that apply Learning Analytics to increase transparency and facilitate the understanding of data processing. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-11T08:00:02Z No. of bitstreams: 1 Learning Analytics Icons Easy Comprehension of Data Treatment.pdf: 742924 bytes, checksum: d7cf786ac3a600774d92a70ed26dd70f (MD5); Made available in DSpace on 2026-03-11T08:00:02Z (GMT). No. of bitstreams: 1 Learning Analytics Icons Easy Comprehension of Data Treatment.pdf: 742924 bytes, checksum: d7cf786ac3a600774d92a70ed26dd70f (MD5) https://reunir.unir.net/handle/123456789/19215 An Effective Prediction Approach for the Management of Children Victims of Road Accidents Saadi, F.; Atmani, B.; Henni, F.; Benfriha, H.; Addou, Z.; Guerbouz, R. Road traffic generates a considerable number of accidents each year. The management of injuries caused by these accidents is becoming a real public health problem. Faced with this latter, we propose a new clinical decision making approach based on case-based reasoning (CBR) and data mining (DM) techniques to speed up and improve the care of an injured child. The main idea is to preprocess the dataset before using K Nearest Neighbor (KNN) Classification Model. In this paper, an efficient predictive model is developed to predict the admission procedure of a child victim of a traffic accident in pediatric intensive care units. The evaluation of the proposed model is conducted on a real dataset elaborated by the authors and validated by statistical analysis. This novel model executes a selection of relevant attributes using data mining technique and integrates a CBR system to retrieve similar cases from an archive of cases of patients successfully treated with the proposed treatment plan. The results revealed that the proposed approach outperformed other models and the results of previous studies by achieving an accuracy of 91.66%. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:52:58Z No. of bitstreams: 1 An Effective Prediction Approach for the Management of Children Victims of Road Accidents.pdf: 456184 bytes, checksum: 3965144c96757b44a1f1e1382039e0ad (MD5); Made available in DSpace on 2026-03-10T16:52:58Z (GMT). No. of bitstreams: 1 An Effective Prediction Approach for the Management of Children Victims of Road Accidents.pdf: 456184 bytes, checksum: 3965144c96757b44a1f1e1382039e0ad (MD5) https://reunir.unir.net/handle/123456789/19214 Traffic Optimization Through Waiting Prediction and Evolutive Algorithms García, Francisco Traffic optimization systems require optimization procedures to optimize traffic light timing settings in order to improve pedestrian and vehicle mobility. Traffic simulators allow obtaining accurate estimates of traffic behavior by applying different timing configurations, but require considerable computational time to perform validation tests. For this reason, this project proposes the development of traffic optimizations based on the estimation of vehicle waiting times through the use of different prediction techniques and the use of this estimation to subsequently apply evolutionary algorithms that allow the optimizations to be carried out. The combination of these two techniques leads to a considerable reduction in calculation time, which makes it possible to apply this system at runtime. The tests have been carried out on a real traffic junction on which different traffic volumes have been applied to analyze the performance of the system. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:44:42Z No. of bitstreams: 1 Traffic Optimization Through Waiting Prediction and Evolutive Algorithms.pdf: 711723 bytes, checksum: c097d0a6f750ca2a64f026156e4c9f85 (MD5); Made available in DSpace on 2026-03-10T16:44:42Z (GMT). No. of bitstreams: 1 Traffic Optimization Through Waiting Prediction and Evolutive Algorithms.pdf: 711723 bytes, checksum: c097d0a6f750ca2a64f026156e4c9f85 (MD5) https://reunir.unir.net/handle/123456789/19213 Use of Data Mining for Intelligent Evaluation of Imputation Methods la Red Martínez, David L.; Primorac, Carlos R. In real-world situations, researchers frequently face the difficulty of missing values (MV), i.e., values not observed in a data set. Data imputation techniques allow the estimation of MV using different algorithms, by means of which important data can be imputed for a particular instance. Most of the literature in this field deals with different imputation methods. However, few studies deal with a comparative evaluation of the different methods as to provide more appropriate guidelines for the selection of the method to be applied to impute data for specific situations. The objective of this work is to show a methodology for evaluating the performance of imputation methods by means of new metrics derived from data mining processes, using quality metrics of data mining models. We started from the complete dataset that was amputated with different amputation mechanisms to generate 63 datasets with MV; these were imputed using Median, k-NN, k-Means and Hot-Deck imputation methods. The performance of the imputation methods was evaluated using new metrics derived from quality metrics of the data mining processes, performed with the original full file and with the imputed files. This evaluation is not based on measuring the error when imputing (usual operation), but on considering the similarity of the values of the quality metrics of the data mining processes obtained with the original file and with the imputed files. The results show that –globally considered and according to the new proposed metric, the imputation methods that showed the best performance were k-NN and k-Means. An additional advantage of the proposed methodology is that it provides predictive data mining models that can be used a posteriori. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:41:16Z No. of bitstreams: 1 Use of Data Mining for Intelligent Evaluation of Imputation Methods.pdf: 791527 bytes, checksum: 3cd9625f80fe5307b0d325d9a030d7a7 (MD5); Made available in DSpace on 2026-03-10T16:41:16Z (GMT). No. of bitstreams: 1 Use of Data Mining for Intelligent Evaluation of Imputation Methods.pdf: 791527 bytes, checksum: 3cd9625f80fe5307b0d325d9a030d7a7 (MD5) https://reunir.unir.net/handle/123456789/19212 Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data Galphade, Manisha; Nikam, V. B.; Banerjee, Biplab; Kiwelekar, Arvind W.; Sharma, Priyanka Currently, wind power is the fast growing area in the domain of renewable energy generation. Accurate prediction of wind power output in wind farms is crucial for addressing the challenges associated the power grid. This precise forecasting enables grid operators to enhance safety and optimize grid operations by effectively managing fluctuations in power generation, ensuring a reliable and stable energy supply. In recent years, there has been a significant rise in research and investigations conducted in this field. This study aims to develop a multivariate short-term wind power forecasting (WPF) model with the objective of enhancing forecasting precision. Among the various prediction models, deep learning models such as Long Short-Term Memory (LSTM) have demonstrated outstanding performance in the field of WPF. By adding multiple layers of LSTM networks, the model can capture more complex patterns. To improve the performance, data preprocessing is carried out using two techniques such as removal of missing values and imputing missing values using Random Forest Regressor (RFR). The comparison between the proposed Stacked LSTM model and other methods including vector autoregressive (VAR), Multiple Linear Regression, Gated Recurrent Unit (GRU) and Bidirectional LSTM (BiLSTM) has been experimented on two datasets. The experimental results show that after imputing missing values using RFR, the Stacked LSTM is optimized model for better performance than above mentioned reference models. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:37:45Z No. of bitstreams: 1 Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.pdf: 1302353 bytes, checksum: 4543961938eeff2158406c9a8ac0a471 (MD5); Made available in DSpace on 2026-03-10T16:37:45Z (GMT). No. of bitstreams: 1 Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.pdf: 1302353 bytes, checksum: 4543961938eeff2158406c9a8ac0a471 (MD5) https://reunir.unir.net/handle/123456789/19211 Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation Martínez Comesaña, Miguel; Martínez Torres, Javier; Javier, Pablo; López Gómez, Javier Optimising the use of the photovoltaic (PV) energy is essential to reduce fossil fuel emissions by increasing the use of solar power generation. In recent years, research has focused on physical simulations or artifical intelligence models attempting to increase the accuracy of PV generation predictions. The use of simulated data as pre-training for deep learning models has increased in different fields. The reasons are the higher efficiency in the subsequent training with real data and the possibility of not having real data available. This work presents a methodology, based on an deep learning model optimised with specific techniques and pre-trained with synthetic data, to estimate the generation of a PV system. A case study of a photovoltaic installation with 296 PV panels located in northwest Spain is presented. The results show that the model with proper pre-training trains six to seven times faster than a model without pre-training and three to four times faster than a model pre-trained with non-accurate simulated data. In terms of accuracy and considering a homogeneous training process, all models obtained average relative errors around 12%, except the model with incorrect pre-training which performs worse. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:33:56Z No. of bitstreams: 1 Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation.pdf: 874414 bytes, checksum: e985093a6df3c4486e1d497ba70f6f66 (MD5); Made available in DSpace on 2026-03-10T16:33:56Z (GMT). No. of bitstreams: 1 Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation.pdf: 874414 bytes, checksum: e985093a6df3c4486e1d497ba70f6f66 (MD5) https://reunir.unir.net/handle/123456789/19210 Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence Ordoñez, Hugo; Timarán Pereira, Ricardo; González Sanabria, Juan Sebastián Introduction: Currently, homelessness should not be seen as just another problem, but as a reality of inequality and the absence of social justice. In this sense, homeless people are subjected to social disengagement, lack of job opportunities or the instability of these, insecurity circumstances, these aspects being one of the causes associated with the consumption or addiction to psychoactive substances. Data: To define the proposed approach, data from the Census of Street Inhabitants - CHC- 2021 of the National Administrative Department of Statistics (DANE), which contains 19,375 records and 25 columns, were used. Methodology: This article presents an artificial intelligence approach that implements a model based on machine learning algorithms for identifying addiction trends to psychoactive substances in street dwellers in Colombia. Conclusions: Based on the results obtained, it is evident that the approach can serve as a support for decision making by municipal administrations in the definition of social public policies for the street-dwelling population in Colombia. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:30:02Z No. of bitstreams: 1 Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence.pdf: 565233 bytes, checksum: b4e896828cd0cfad07a43ea55b209154 (MD5); Made available in DSpace on 2026-03-10T16:30:02Z (GMT). No. of bitstreams: 1 Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence.pdf: 565233 bytes, checksum: b4e896828cd0cfad07a43ea55b209154 (MD5) https://reunir.unir.net/handle/123456789/19209 Explainable Artificial Intelligence-Based Diseases Diagnosis From Unstructured Clinical Data and Decision Making Using Blockchain Technologies M., Sumathi; Raja, S.P. In the digital era, health information is stored in digital form for easy maintenance, analysis and transfer. The proficiency of manual illness diagnosis and drug prediction in the medical field depends on the expertise availability, and experience of the specialists. In emergency and abnormal situation, the patient’s life completely depends on expert’s availability. Therefore, a different approach is needed to get around the difficulties in managing emergency cases. Artificial intelligence helps to take decisions in an accurate manner but does not provide the details of the decisions. The ability to treat emergency patients entirely depends on the particular hospitals. The clinical data includes numerical results, text prescriptions, scanned images, etc. Therefore, managing unstructured data with care is necessary for making clinical decisions. An explainable artificial intelligence-based disease diagnosis and blockchain-based decision-making system are presented in this work to address these challenges and improve patient care. A natural language processing system analyzes the unstructured data to identify different types of data and explainable AI diagnosis disease with justification and reason for the prediction. An ant colony optimization-based recommender system examines the predicted decision and identifies the specific drug for the disease. The disease decision and drug information are kept in a permissioned blockchain for confirmation. Decisions are validated by more than 50% of the experts present in the permissioned blockchain network, which consists of experts from various regions. As a result, the quickest and most accurate decisions possible are taken to handle emergency situations. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:26:30Z No. of bitstreams: 1 Explainable Artificial Intelligence-Based Diseases.pdf: 755211 bytes, checksum: 7b0d1912a5c5a87d6259ed890b6da20c (MD5); Made available in DSpace on 2026-03-10T16:26:30Z (GMT). No. of bitstreams: 1 Explainable Artificial Intelligence-Based Diseases.pdf: 755211 bytes, checksum: 7b0d1912a5c5a87d6259ed890b6da20c (MD5) https://reunir.unir.net/handle/123456789/19208 Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets Bobadilla, Jesús; Gutiérrez, Abraham The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datasets. We have tested the GANRS method by creating multiple synthetic datasets from three different real datasets taken as a source. Experiments include variations in the number of users in the synthetic datasets, as well as a different number of samples. We have also selected six state-of-the-art collaborative filtering deep learning models to test both their comparative performance and the GANRS method. The results show a consistent behavior of the generated datasets compared to the source ones; particularly, in the obtained values and trends of the precision and recall quality measures. The tested deep learning models have also performed as expected on all synthetic datasets, making it possible to compare the results with those obtained from the real source data. Future work is proposed, including different cold start scenarios, unbalanced data, and demographic fairness. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:22:47Z No. of bitstreams: 1 Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets.pdf: 1472576 bytes, checksum: 2860c05b019e926dcea84f05f95619de (MD5); Made available in DSpace on 2026-03-10T16:22:47Z (GMT). No. of bitstreams: 1 Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets.pdf: 1472576 bytes, checksum: 2860c05b019e926dcea84f05f95619de (MD5) https://reunir.unir.net/handle/123456789/19207 Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality Izquierdo Domenech, Juan; Linares Pellicer, Jordi; Ferri Molla, Isabel Augmented reality (AR) has become a powerful tool for assisting operators in complex environments, such as shop floors, laboratories, and industrial settings. By displaying synthetic visual elements anchored in real environments and providing information for specific tasks, AR helps to improve efficiency and accuracy. However, a common bottleneck in these environments is introducing all necessary information, which often requires predefined structured formats and needs more ability for multimodal and Natural Language (NL) interaction. This work proposes a new method for dynamically documenting complex environments using AR in a multimodal, non-structured, and interactive manner. Our method employs Large Language Models (LLMs) to allow experts to describe elements from the real environment in NL and select corresponding AR elements in a dynamic and iterative process. This enables a more natural and flexible way of introducing information, allowing experts to describe the environment in their own words rather than being constrained by a predetermined structure. Any operator can then ask about any aspect of the environment in NL to receive a response and visual guidance from the AR system, thus allowing for a more natural and flexible way of introducing and retrieving information. These capabilities ultimately improve the effectiveness and efficiency of tasks in complex environments. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:18:55Z No. of bitstreams: 1 Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality.pdf: 1472569 bytes, checksum: 751baa5d6dfe340da811523a6afd0c1e (MD5); Made available in DSpace on 2026-03-10T16:18:55Z (GMT). No. of bitstreams: 1 Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality.pdf: 1472569 bytes, checksum: 751baa5d6dfe340da811523a6afd0c1e (MD5) https://reunir.unir.net/handle/123456789/19206 Distinguishing Human From Machine: A Review of Advances and Challenges in AI-Generated Text Detection Fariello, Serena The rise of Large Language Models (LLMs) has dramatically altered the generation and spreading of textual content. This advancement offers benefits in various domains, including medicine, education, law, coding, and journalism, but also has negative implications, mainly related to ethical concerns. Preventing measures to mitigate negative implications pass through solutions that distinguish machine-generated text from humanwritten text. This study aims to provide a comprehensive review of existing literature for detecting LLMgenerated texts. Emerging techniques are categorized into five categories: watermarking, feature-based, neural-based, hybrid, and human-aided methods. For each introduced category, strengths and limitations are discussed, providing insights into their effectiveness and potential for future improvements. Moreover, available datasets and tools are introduced. Results demonstrate that, despite the good delimited performance, the multitude of languages to recognize, hybrid texts, the continuous improvement of algorithms for text generation and the lack of regulation require additional efforts for efficient detection. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:12:44Z No. of bitstreams: 1 Distinguishing Human From Machine A Review of Advances and Challenges in AI-Generated Text Detection.pdf: 465865 bytes, checksum: 9b07ebd124a70095cc94411c6a5a1a6d (MD5); Made available in DSpace on 2026-03-10T16:12:44Z (GMT). No. of bitstreams: 1 Distinguishing Human From Machine A Review of Advances and Challenges in AI-Generated Text Detection.pdf: 465865 bytes, checksum: 9b07ebd124a70095cc94411c6a5a1a6d (MD5) https://reunir.unir.net/handle/123456789/19205 Editor’s Note Morente Molinera, Juan Antonio The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) publishes articles discussing the latest current topics in the research literature. The emergence of ChatGPT and other similar models based on deep learning are dramatically changing the way people understand and use artificial intelligence. Despite the significant advances made in these types of techniques, which have been enormous in recent years, new learning methods are still needed. Specifically, we require methods that allow us to handle data correctly in specific environments, as well as provide learning methods with the necessary explainability that allows us to understand how they are reasoning. The latter is essential for creating ethical learning methods that do not make unfair decisions based on biased information. It is also important to identify data that have, in some way, reflected the reprehensible attitudes and reasoning that we as fallible human beings sometimes have. In short, artificial intelligence should reflect, if possible, the best of us rather than the worst. With this goal in mind, it is common to see in this issue of the journal an abundance of articles proposing new learning methods, many of which are based on Deep Learning and Data Mining. There are also articles on large language models, which are extremely important in the current artificial intelligence landscape. Of course, there are also articles on optimization methods and quantum computers, which are also of great importance in the field of artificial intelligence. Although generative artificial intelligence models are perhaps the ones that have people most intrigued, this is not the only current application of artificial intelligence. We are seeing how renewable energies, in particular those that come from the sun and wind, are playing an increasingly important role in global energy generation. As seen in recent events, such as the general blackout in Spain, the electricity system needs new methods that allow adequate regulation to prevent all kinds of possible failures. In this issue, two articles present new applications of artificial intelligence methods to renewable energy generation systems. Also noteworthy within this issue is the application of artificial intelligence in the field of teaching, where the aim is to provide a better learning experience for students and teachers. Submitted by Angela María Porras Ruiz (angela.porras@unir.net) on 2026-03-10T16:09:26Z No. of bitstreams: 1 Editor’s Note.pdf: 72460 bytes, checksum: d61494befc2e8cee6c6f9678d703da8b (MD5); Made available in DSpace on 2026-03-10T16:09:26Z (GMT). No. of bitstreams: 1 Editor’s Note.pdf: 72460 bytes, checksum: d61494befc2e8cee6c6f9678d703da8b (MD5)