Martinez-Villaseñor, Lourdes

Main Affiliation

Preferred name

Official Name

Martínez Villaseñor, María de Lourdes

ORCID

0000-0002-9038-7821

Researcher ID

N-7607-2018

Scopus Author ID

55521085100

Now showing 1 - 10 of 94

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Human-Friendly Explanations Checklist for Reinforcement Learning: XRL H-F-E Checklist
(Springer Nature Switzerland, 2025)
Contreras Olivas, Daniel Adrian
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Explainable Reinforcement Learning (XRL) has emerged as a critical subfield at the intersection of reinforcement learning (RL) and Explainable Artificial Intelligence (XAI), aiming to render the decision-making processes of learning agents interpretable, transparent, and accessible to human users. This paper introduces a comprehensive evaluation framework, the XRL H-F-E Metrics, to assess the human-friendliness of explanations generated by XRL systems. Drawing from interdisciplinary literature in computer science, cognitive psychology, philosophy of science, and human-computer interaction, the framework is structured across four dimensions: foundational principles (e.g., correctness, robustness, bias mitigation), cognitively aligned explanation types (e.g., “why”, “why not”, counterfactuals), characteristics of “good” explanations (e.g., contrastiveness, selectivity, causality), and human-friendly presentation attributes (e.g., comprehensibility, interactivity, personalization). This checklist provides both a theoretical model and a practical tool for fostering transparency and trust in RL applications, while also identifying key directions for future research, including quantitative metrics, adaptive explanations, and emotionally responsive interfaces. ©The authors ©Springer.
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Ethical Challenges in Demand Prediction: A Case Study in the Wholesale Grocery Sector
(Instituto Politécnico Nacional. Centro de Investigación en Computación, 2025)
Duarte, Jorge
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Artificial Intelligence (AI) has emergedas a transformative tool in inventory management and demand prediction within the wholes ale grocerysector. By leveraging machine learning algorithms, businesses can analyze historical sales data, market trends, and seasonal variations to optimize inventory levels, reducing overstock and stockouts. AI-drivendemand prediction models provide accurate forecasts, enabling whole salers to anticipate customer needs and streamline supply chain operations. Thisarticle examines the ethical challenges associated with developing and implementing AI-driven demand prediction models in the wholesale grocery sector. As businesses seek to optimize their operations through artificial intelligence, significant ethical concerns arise that must be addressed to ensure responsible and fair implementation. This case study highlights the main ethical challenges identified in a grocery wholesaler, focusing on issues such as transparency, accountability, fairness, and human control. Through the analysis of aspecific demand prediction model, we discuss how these ethical concerns not only influence user acceptance of the model but also impact operational efficiency and customer satisfaction. The article aims to contribute to the ongoing dialogue on ethics in data science, providing insights and recommendations for companies looking to adopt predictive technologies ethically. ©The authors ©Computación y Sistemas © Instituto Politécnico Nacional. Centro de Investigación en Computación.
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Preface
(2025)
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Vazquez Roberto A
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Ochoa-ruiz Gilberto
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Real-Time Emotion Recognition in Intelligent Tutoring Systems
(Springer Nature Switzerland, 2025)
Alcauter, Iara
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This study investigates the integration of facial emotion recognition (FER) into Intelligent Tutoring Systems (ITS), with the aim of identifying the emotions that emerge throughout the learning process. Building on prior research, we argue that understanding students’ emotional states enables the delivery of more adaptive and effective feedback, thereby improving learning outcomes. A Deep Convolutional Neural Network (DCNN) was trained on the FER2013+ dataset, achieving a top-3 accuracy of 95.24% in classifying facial expressions across eight emotion categories. The model was integrated with MediaPipe to enable real-time emotion detection from video streams using a standard laptop camera, facilitating practical deployment in educational settings. Thirteen high school and early university students interacted with OATutor—an open-source ITS—while their facial expressions and on-screen activities were recorded. Emotional data from each frame was synchronized with an academic event log documenting actions such as starting a lesson, requesting help, or submitting answers. Results show that “surprise” was the most frequently observed emotion (over 85% of instances), whereas “anger,” “sadness,” and “contempt” appeared only in specific learning scenarios, particularly when students faced cognitive challenges or achieved multiple correct responses. Despite the absence of affective feedback from the system, students’ emotions fluctuated dynamically, suggesting active self-regulation processes. These findings demonstrate the feasibility of FER-enhanced ITS in real-world educational environments and underscore the need for future work integrating multimodal data and personalization strategies to optimize affective responsiveness in intelligent learning contexts. ©The authors ©Springer.
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A Novel Ethical Design Framework Applied to Image Classification Challenges in the Fashion Industry
(Springer Nature Switzerland, 2025)
Guillen Alvarez, Luis
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As artificial intelligence (AI) continues to play a pivotal role in image classification applications, the ethical implications of these technologies become increasingly significant. This paper explores the intersection of AI and ethics in the context of image classification, specifically focusing on the application of ethical design principles through a framework for a use of case in the fashion industry involving bags images and social media. This work delves into the integration of a comprehensive ethical framework around all the design process. The case study involves the development and implementation of a neural network tailored for bag image classification, leveraging transfer learning techniques. Through a meticulous examination of the ethical dimensions inherent in image classification, the study aims to establish a foundation for responsible and transparent AI practices. ©The authors ©Springer.
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Preface : Artificial Intelligence – COMIA 2025 17th Mexican Congress, Mexico City, Mexico, May 12–16, 2025, Proceedings, Part I
(Springer Science and Business Media Deutschland GmbH, 2025)
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Martínez-Seis, Bella
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Pichardo-Lagunas, Obdulia
The Mexican Conference on Artificial Intelligence (COMIA) is an annual academic event organized by the Mexican Society for Artificial Intelligence (SMIA, Sociedad Mexicana de Inteligencia Artificial) since 2008. COMIA serves as the main national forum in the field of artificial intelligence (AI) and represents a key meeting point for the country’s academic and scientific community dedicated to AI. This year, COMIA 2025 was organized by the SMIA in collaboration with Universidad Panamericana; and was held from May 12 to 16, 2025, in Mexico City, Mexico.© The author ©Springer.
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MICAI and the Making of AI in Mexico Through 25 Years of Data-Driven Insight
(Springer Nature Switzerland, 2025)
Avalos-Gauna, Edgar
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Palafox Novack, Leon Felipe
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This study investigates the integration of facial emotion recognition (FER) into Intelligent Tutoring Systems (ITS), with the aim of identifying the emotions that emerge throughout the learning process. Building on prior research, we argue that understanding students’ emotional states enables the delivery of more adaptive and effective feedback, thereby improving learning outcomes. A Deep Convolutional Neural Network (DCNN) was trained on the FER2013+ dataset, achieving a top-3 accuracy of 95.24% in classifying facial expressions across eight emotion categories. The model was integrated with MediaPipe to enable real-time emotion detection from video streams using a standard laptop camera, facilitating practical deployment in educational settings. Thirteen high school and early university students interacted with OATutor—an open-source ITS—while their facial expressions and on-screen activities were recorded. Emotional data from each frame was synchronized with an academic event log documenting actions such as starting a lesson, requesting help, or submitting answers. Results show that “surprise” was the most frequently observed emotion (over 85% of instances), whereas “anger,” “sadness,” and “contempt” appeared only in specific learning scenarios, particularly when students faced cognitive challenges or achieved multiple correct responses. Despite the absence of affective feedback from the system, students’ emotions fluctuated dynamically, suggesting active self-regulation processes. These findings demonstrate the feasibility of FER-enhanced ITS in real-world educational environments and underscore the need for future work integrating multimodal data and personalization strategies to optimize affective responsiveness in intelligent learning contexts. ©The authors ©Springer.
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Ethical Considerations in the Development of Recommendation Systems: Boutique Travel Agency
(Springer Nature Switzerland, 2025)
Alćazar-Pérez, Rodolfo
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This paper explores the ethical implications of implementing a recommendation system within a boutique travel agency specialized in personalized group travel experiences. It covers legal frameworks, internal policies, risk-benefit analyses, and ethical design principles to ensure fair, transparent, and privacy-respecting use of AI. ©The authors ©Springer.
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Towards a Distributed-Based Learning Robot from Scratch via Neuro-Evolutionary Computation
(Springer Nature Switzerland, 2025)
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In the context of Industry 4.0, multi-robot systems (MRS) have become essential for enhancing the adaptability and efficiency of flexible manufacturing systems, enabling rapid responses to market demands through personalized customization. Effective collaboration among multiple robots requires advanced communication, shared goal alignment, and the ability to gather and process environmental data to execute coordinated actions with precision. Despite their advantages, improving efficiency of robot learning still remains a crucial challenge, particularly in flexible manufacturing multi-robot systems, where generalization across diverse scenarios is essential for effective deployment. In this work, we propose the use of neuro-evolutionary computation to solve the particular learning-from-scratch problem in a multi-robot system. This approach consists of an architecture of a decentralized MRS in which a local controller per robot is based on Artificial Hydrocarbon Networks machine learning model. Also, it includes a learning strategy via Wound Treatment Optimization algorithm. We implement the architecture in a simulated environment to solve the mountain car domain. Experimental results and a comparison with reinforcement learning validate the ability of this approach to learn a task from scratch without prior explicit data. We anticipate the applicability of this approach in smart factories or autonomous vehicles. ©The authors ©Springer.
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Risk Factors for Hypertension and Health Policy
(Springer Nature Switzerland, 2025)
Navarro Mentado, Maximino
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Salazar Altamirano, Vladimir
Hypertension (HTN) remains a leading cause of global morbidity and mortality, with its prevalence rapidly increasing in middle-income countries like Mexico. This study leverages data from the National Health and Nutrition Survey (ENSANUT) 2022–2023 to identify significant risk factors for HTN using machine learning models and explainable artificial intelligence techniques. Methods: We analyzed 8,650 Mexican adults aged 20 years using logistic regression, random forest, and extreme gradient boosting (XGBoost) classifiers, optimized through Optuna framework. Hypertension was defined using ACC/AHA 2017 criteria (130/80 mmHg or previous diagnosis). SHAP, LIME, and ELI5 were employed for model interpretation. Performance was evaluated using stratified 5-fold cross-validation and external validation on ENSANUT 2023 data. Results: XGBoost demonstrated superior predictive performance (ROC-AUC: 0.758, F1-score: 0.703) compared to logistic regression and random forest. Key risk factors identified were age, body mass index (BMI), gender, family history of hypertension (maternal and paternal), and physical activity—particularly sedentary behavior. SHAP-based subgroup analysis revealed that age and BMI consistently emerged as the most influential factors across both genders, with family history of hypertension (maternal and paternal) also showing significant importance. Gender-specific differences were subtle, with similar risk factor patterns observed in men and women. Conclusions: Machine learning models, particularly XGBoost with explainable AI techniques, effectively predict hypertension risk using nationally representative Mexican data. These findings support implementation of ML-driven risk stratification in primary care and inform targeted public health interventions. The integration of XAI methods provides transparent, interpretable predictions suitable for clinical decision-making and policy development in Mexico’s healthcare system. ©The authors ©Springer.

Martinez-Villaseñor, Lourdes

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