International Journal of Combinatorial Optimization Problems and Informatics
type: channel
description: The International Journal of Combinatorial Optimization Problems and Informatics (IJCOPI) aims to provide a forum for the dissemination and discussion of research in combinatorial optimization and informatics. The journal publishes high-quality, peer-reviewed articles with emphasis on new problems, methods, techniques, and algorithms. IJCOPI is an open access journal that supports the dissemination of scientific knowledge through electronic publication. A rigorous peer review process ensures the validity, originality, and relevance of all published contributions. The journal follows a regular publication schedule, organizing issues throughout the year. Articles may be published as part of scheduled issues to ensure timely dissemination of research results. IJCOPI publishes three regular issues per year, corresponding to the periods January–April, May–August, and September–December. Additional special issues may be published when academically justified. The journal encourages contributions that support the advancement of the United Nations Sustainable Development Goals (SDGs), particularly in areas related to industry, innovation, and infrastructure (SDG 9), as well as the application of optimization and informatics to societal and technological challenges. Type of publication The International Journal of Combinatorial Optimization Problems and Informatics (IJCOPI) is a specialised scientific research journal that publishes peer-reviewed original research articles in combinatorial optimisation, informatics, artificial intelligence, data science, operations research and related interdisciplinary fields.
publisher: Editorial Académica Dragón Azteca
language: en-US
publication name: International Journal of Combinatorial Optimization Problems and Informatics
issn: 2007-1558
items: Topological and Self-Structured Approaches to Supervised Anomaly Detection in Econometrics
items: Mitigating the saturation gap: Inverse prime-density scaling for high-stakes probabilistic modeling
items: A new approach for microcalcification detection using hybrid filtering and DBSCAN segmentation
items: Enhanced CO₂ Forecasting in Indoor Environments Using Advanced LSTM Models
items: Four Lines, Different Deaths: Exploring the Classification of Humor in Calaveritas Poems
items: Optimizing a Smart City’s Urban Park Lighting using Monkey Algorithm
items: Brief Survey of Ethical Artificial Intelligence and its commitment to Self-Regulated Learning
items: Implementation of Scrum Framework and Trello-Based Visual Management for Production Process Optimization: A Mixed-Methods Case Study in a Sports Apparel Manufacturing Company
items: Emotional AI in the Workplace: Systematic Review of Effects on Employee Well-Being, Productivity, and Organizational Performance
items: Intellekt: A Machine Learning Based Framework for Advanced Muscle Strain Severity Detection Using IoT Devices
items: Technology Applied to Identifying Areas without Access to Drinking Water: Challenges and Opportunities
items: Blockchain for electronic voting: A security approach
items: Human-Machine Interfaces in Health: A Critical Review and Framework of Human-Centered Design Practices
items: Interactive 3D Visualization Engine for Molecular Docking and Structural Complex Analysis
items: The Big data in organizations: A systematic review of impact, efficiency, and predictive capacities
items: The Use of renewable energies in educational institutions: feasibility and environmental benefits
items: The Automotive sector on the industry side
items: A Hybrid Bio-Inspired Roach Infestation Algorithm with Interval Type-2 Fuzzy Adapter for Large-Scale Optimization: An extensive Evaluation
items: Spectral analysis of distorted and autoencoder-reconstructed MNIST images in a kernel-induced feature space
items: EMG Hand-Gesture Classification Using Time, Frequency and Time–Frequency Domain Features with Machine-Learning Techniques
items: Generation of a Multispectral Dataset Based on Satellite Images for Poverty Prediction at Municipal Level in Mexico

Topological and Self-Structured Approaches to Supervised Anomaly Detection in Econometrics
type: item
description: This article proposes a robust econometric framework for anomaly detection in nonstationary time series affected by noise, outliers, and regime shifts. The method combines windowed feature construction, supervised learning, and stability-oriented regularization, while enabling optional topological and structural diagnostics to corroborate detected transitions. A reproducible pipeline trains models, calibrates decision thresholds, and preserves artifacts for transparent validation, including metrics, figures, and segment-level summaries. Experiments show consistent discrimination, improved reliability under distributional change, and interpretable latent representations that support operational monitoring. The results demonstrate that integrating robustness principles with structured diagnostics yields actionable early-warning signals for complex dynamic systems in practice. Spanish-language metadata / Metadatos en español Título en español: Enfoques topológicos y autoestructurados para la detección supervisada de anomalías en econometría Resumen: Este artículo propone un marco econométrico robusto para la detección de anomalías en series temporales no estacionarias afectadas por ruido, valores atípicos y cambios de régimen. El método combina la construcción de características por ventanas, el aprendizaje supervisado y la regularización orientada a la estabilidad, al tiempo que permite realizar diagnósticos topológicos y estructurales opcionales para corroborar las transiciones detectadas. Un proceso reproducible entrena modelos, calibra umbrales de decisión y conserva los datos para una validación transparente, incluyendo métricas, figuras y resúmenes a nivel de segmento. Los experimentos muestran una discriminación consistente, una mayor fiabilidad ante cambios en la distribución y representaciones latentes interpretables que facilitan la supervisión operativa. Los resultados demuestran que la integración de principios de robustez con diagnósticos estructurados per…
creator: Jaime Aguilar-Ortiz
creator: Carlos R. Domínguez-Mayorga
creator: Ocotlán Diáz-Parra
creator: Jorge A. Ruiz-Vanoye
creator: Francisco R. Trejo-Macotela
creator: Marco A. Vera-Jiménez
creator: Víctor M. Zamudio-García
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 1
ending page: 36
doi: 10.61467/2007.1558.2026.v17i3.857

Mitigating the saturation gap: Inverse prime-density scaling for high-stakes probabilistic modeling
type: item
description: Learning under severe class imbalance exposes a critical limitation of standard probabilistic gates: saturation at large pre-activations, which leads to vanishing gradients and poor minority-class recall. Inspired by the Prime Number Theorem, we introduce PrimeSigmoid, a logarithmically moderated nonlinearity that preserves gradient sensitivity in high-activation regimes while maintaining probabilistic interpretability. The contribution of this work is centered on three key pillars: (i) Mitigating the Saturation Gap, where logarithmic moderation prevents the model from ignoring rare events; (ii) Structural Robustness, demonstrated through an exhaustive grid study of 63 architectural combinations where PrimeSigmoid consistently dominates across heterogeneous hidden representations; and (iii) Probabilistic Integrity, showing that significant gains in Recall and Matthews Correlation Coefficient (MCC) are achieved without degrading AUC or LogLoss calibration. These results position logarithmically moderated nonlinearities as a principled mechanism for robust classification in high-stakes domains such as financial risk modeling. Spanish-language metadata / Metadatos en español Título en español: Reducción de la brecha de saturación: escalado inverso de la densidad de números primos para la modelización probabilística de alto riesgo Resumen: El aprendizaje en condiciones de desequilibrio grave entre clases pone de manifiesto una limitación crítica de las puertas probabilísticas estándar: la saturación ante preactivaciones elevadas, lo que conduce a la desaparición de los gradientes y a una baja recuperación de las clases minoritarias. Inspirándonos en el teorema de los números primos, presentamos PrimeSigmoid, una no linealidad moderada logarítmicamente que conserva la sensibilidad de los gradientes en regímenes de alta activación, al tiempo que mantiene la interpretabilidad probabilística. La contribución de este trabajo se centra en tres pilares fundamentales: (i) la mi…
creator: Jaime Aguilar-Ortiz
creator: Manuel Francisco Gutiérrez-Salinas
creator: Carlos R. Domínguez-Mayorga
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 37
ending page: 52
doi: 10.61467/2007.1558.2026.v17i3.1331

A new approach for microcalcification detection using hybrid filtering and DBSCAN segmentation
type: item
description: In the field of medicine, various methods are available for detecting abnormalities in breast cancer diagnostic procedures. Mammography is the most commonly used method for microcalcification (MC) detection in the early stages. Moreover, the geometric characteristics of MCs evolve through different stages. Several studies have addressed the detection of MCs. In the first step, characteristic enhancement is used to isolate MCs from the whole image. In the final steps, classical clustering techniques are applied. Different studies focus on enhancing characteristics in the initial stage, employing separation techniques to distinguish the present objects from the entire image, and concluding with classical grouping techniques. Spanish-language metadata / Metadatos en español Título en español: Un nuevo enfoque para la detección de microcalcificaciones mediante filtrado híbrido y segmentación DBSCAN Resumen: En el campo de la medicina, existen diversos métodos para detectar anomalías en los procedimientos de diagnóstico del cáncer de mama. La mamografía es el método más utilizado para la detección de microcalcificaciones (MC) en las etapas tempranas. Además, las características geométricas de las MC evolucionan a lo largo de las diferentes etapas. Varios estudios han abordado la detección de clústeres de objetos (MC). En la primera etapa, se utiliza el realce de características para aislar los MC de la imagen completa. En las etapas finales, se aplican técnicas clásicas de agrupamiento. Diversos estudios se centran en el realce de características en la etapa inicial, empleando técnicas de separación para distinguir los objetos presentes del resto de la imagen, y concluyendo con técnicas clásicas de agrupamiento. Palabras Claves: Cáncer de mama, microcalcificaciones, entropía, morfología, agrupamiento, DBSCAN Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i3.1151 Dimensions. Open Alex.
creator: Ramón Osvaldo Guardado Medina
creator: Arturo García García
creator: Javier Claustro Bobadilla
creator: Luis Gerardo Rodarte Franco
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 53
ending page: 70
doi: 10.61467/2007.1558.2026.v17i3.1151

Enhanced CO₂ Forecasting in Indoor Environments Using Advanced LSTM Models
type: item
description: Accurate forecasting of CO₂ levels in indoor environments is essential for effective air quality management and public health protection. This study assesses the performance of four Long Short-Term Memory (LSTM)-based models—LSTM, Spatial LSTM (sLSTM), Memory-Augmented LSTM (mLSTM), and Extended LSTM (xLSTM)—for CO₂ prediction. The results demonstrate that xLSTM consistently outperforms the others models across multiple evaluation metrics, establishing it as a highly reliable option for air quality monitoring. The research underscores the relevance of precise CO₂ forecasting for policymakers and building managers in optimizing indoor environments conditions. Future work will focus in integrating xLSTM into a real-time monitoring system powered by Big Data and Internet of Things (IoT) technologies, incorporating multiple forecasting algorithms. This approach aims to enhance indoor air quality management and support respiratory diseases prevention through continuous monitoring and advanced predictive analysis. Spanish-language metadata / Metadatos en español Título en español: Mejora de la predicción de CO₂ en ambientes interiores mediante modelos LSTM avanzados Resumen: La predicción precisa de los niveles de CO₂ en ambientes interiores es esencial para una gestión eficaz de la calidad del aire y la protección de la salud pública. Este estudio evalúa el desempeño de cuatro modelos basados en la red de memoria a corto y largo plazo (LSTM) —LSTM, LSTM espacial (sLSTM), LSTM con memoria aumentada (mLSTM) y LSTM extendida (xLSTM)— para la predicción de CO₂. Los resultados demuestran que xLSTM supera sistemáticamente a los demás modelos en múltiples métricas de evaluación, lo que lo consolida como una opción altamente confiable para el monitoreo de la calidad del aire. La investigación destaca la importancia de contar con pronósticos precisos de CO₂ para que los responsables políticos y los administradores de edificios puedan optimizar las condiciones ambientales en los i…
creator: Ariel Isaac Posada Barrera
creator: Laura Margarita Rodríguez Peralta
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 71
ending page: 81
doi: 10.61467/2007.1558.2026.v17i3.897

Four Lines, Different Deaths: Exploring the Classification of Humor in Calaveritas Poems
type: item
description: Calaveritas are seen as a sort of poem or ode to the dead, since is a Mexican tradition linked to the Day of the death, in which through text you could see the personification of death and popular characters been mock and satirize, this topic is quite interesting because it have humor and the structure of the more serious poem. The classification of this textual genre could help locate text that contain humor with unconventional variants or structures and to preserver in a way this written scheme. To tackle this task, it was decided use a machine learning approach for the baseline, taking quite good results around 94% on the F1-score for the top methods of the baseline, in this case the main approach was to finetune Transformers like BETO or BERT-multilingual obtaining 98% and 97% on de F1-score and analyze the similarities and to observer the characteristic inherent to each class. The classes were quite separable since the calaveritas are more near related to humor than to the classical approach of poem, since the text of these classes contain words that are more easily identifiable. Given the observed degree of separability between classes, we sought to ensure that the classification was not primarily driven by topical information. To this end, we masked the most frequent words in each class as a preliminary control experiment, the produced results were broadly comparable to those obtained through fine-tuning our main models, suggesting that structural features may play a role in the classification process. In a way humour intervenes to create a poem-like structure with a humoristic content, A hybrid, perhaps. Spanish-language metadata / Metadatos en español Título en español: Cuatro versos, diferentes muertes: un análisis de la clasificación del humor en los poemas de calaveritas Resumen: Las calaveritas se consideran una especie de poema u oda a los muertos, ya que se trata de una tradición mexicana vinculada al Día de los Muertos, en la que, a través del texto,…
creator: Victor Manuel Palma
creator: Liana Ermakova
creator: Grigori Sidorov
creator: Carolina Palma Preciado
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 82
ending page: 97
doi: 10.61467/2007.1558.2026.v17i3.1348

Optimizing a Smart City’s Urban Park Lighting using Monkey Algorithm
type: item
description: Mexico City, one of the world's most densely populated metropolises, features numerous urban parks for recreational use. To accommodate nocturnal visitors, these parks require well-designed lighting systems. Proper lighting is crucial to avoid negative impacts on ecosystems and human health while ensuring safety, preventing accidents, and reducing light pollution. This study utilizes the "Monkey Algorithm" to optimize the number and placement of luminaires in Mexico City parks. This algorithm, inspired by monkeys' resource-finding behaviors, involves three steps: climbing, watch-jump, and somersault processes. Factors such as lamp position, height, power consumption, and light intensity are considered to maximize coverage. The research adheres to strict electrical installation standards (NOM-001-SEDE-2012) and seeks luminous efficiency as per NOM-030-ENER-2016 and NOM-031-ENER-2012, which set the energy efficiency requirements for LED luminaires in public areas. Spanish-language metadata / Metadatos en español Título en español: Optimización del alumbrado de los parques urbanos de una ciudad inteligente mediante el algoritmo Monkey Resumen: La Ciudad de México, una de las metrópolis más densamente pobladas del mundo, cuenta con numerosos parques urbanos destinados al uso recreativo. Para dar cabida a los visitantes nocturnos, estos parques requieren sistemas de iluminación bien diseñados. Una iluminación adecuada es fundamental para evitar impactos negativos en los ecosistemas y la salud humana, al tiempo que garantiza la seguridad, previene accidentes y reduce la contaminación lumínica. Este estudio utiliza el «algoritmo del mono» para optimizar el número y la ubicación de las luminarias en los parques de la Ciudad de México. Este algoritmo, inspirado en el comportamiento de los monos a la hora de buscar recursos, consta de tres pasos: trepar, saltar y dar volteretas. Se tienen en cuenta factores como la posición de las lámparas, la altura, el consumo de energía y …
creator: Dorey Orozco-Paz
creator: Emilio Batres-Guillén
creator: Carlos Alberto Ochoa-Zezzatti
creator: Rafael Torres Escobar
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 98
ending page: 109
doi: 10.61467/2007.1558.2026.v17i3.443

Brief Survey of Ethical Artificial Intelligence and its commitment to Self-Regulated Learning
type: item
description: The increased use of Artificial Intelligence in education has created opportunities and concerns regarding its use, from an ethical perspective to the cognitive development of students. Various tools based on Artificial Intelligence, such as intelligent tutoring systems, generative models, intelligent assessment, personalised learning, and educational robotics, are transformative examples that promote individual student development. However, these same tools, or those that use Generative Artificial Intelligence, for example, raise questions about their ethical use. This article presents a brief overview of Educational Artificial Intelligence (EAI) and its integration into the teaching-learning process, considering ethical and cybersecurity issues in a self-regulated learning environment, where students must have the ethical capacity to use Artificial Intelligence-based tools to guide their learning. Spanish-language metadata / Metadatos en español Título en español: Breve repaso a la inteligencia artificial ética y su compromiso con el aprendizaje autorregulado Resumen: El creciente uso de la inteligencia artificial en la educación ha generado tanto oportunidades como inquietudes en torno a su aplicación, desde una perspectiva ética hasta el desarrollo cognitivo de los estudiantes. Diversas herramientas basadas en la inteligencia artificial, como los sistemas de tutoría inteligente, los modelos generativos, la evaluación inteligente, el aprendizaje personalizado y la robótica educativa, son ejemplos transformadores que promueven el desarrollo individual de los estudiantes. Sin embargo, estas mismas herramientas, o aquellas que utilizan la inteligencia artificial generativa, por ejemplo, plantean interrogantes sobre su uso ético. Este artículo presenta una breve visión general de la inteligencia artificial educativa (EAI) y su integración en el proceso de enseñanza-aprendizaje, teniendo en cuenta cuestiones éticas y de ciberseguridad en un entorno de aprendizaje auto…
creator: Alejandro Fuentes-Penna
creator: Ricardo A. Barrera-Cámara
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 110
ending page: 121
doi: 10.61467/2007.1558.2026.v17i3.1242

Implementation of Scrum Framework and Trello-Based Visual Management for Production Process Optimization: A Mixed-Methods Case Study in a Sports Apparel Manufacturing Company
type: item
description: This study addresses operational inefficiencies in small-scale manufacturing environments, particularly within sports apparel production, where fragmented workflows and limited coordination hinder productivity. Despite the growing adoption of agile methodologies, their application in traditional manufacturing contexts remains underexplored. This research proposes a Scrum-based framework integrated with Trello as a visual management platform to optimize five critical stages of the production chain: fabric selection, cutting, sewing, ironing, and packaging. A mixed-methods, applied research design was employed, combining diagnostic and descriptive analysis in a cross-sectional, non-experimental field study. Data were collected through a structured instrument consisting of 74 items administered to the entire workforce (n=16), enabling a systematic evaluation of operational bottlenecks. The implementation of this agile framework yielded significant performance improvements. Results show a 43% reduction in downtime, a 62.5% decrease in material waste, and a 150% increase in task management capacity, alongside a 43.7% improvement in internal communication. These findings demonstrate the effectiveness of integrating Scrum principles with digital visual management tools as a scalable approach for enhancing productivity in manufacturing SMEs. This study contributes to the literature by providing a real-world application and comparative analysis of agile methodologies in non-software environments, establishing a replicable benchmark framework for process optimization in industrial contexts. The results highlight the potential of agile systems to foster operational efficiency, team cohesion, and continuous improvement in production settings. Spanish-language metadata / Metadatos en español Título en español: Implementación del marco Scrum y la gestión visual basada en Trello para la optimización de los procesos de producción: un estudio de caso con métodos mixtos en una empres…
creator: Jorge A. Jiménez-Martínez
creator: Mayra L. González-Mosqueda
creator: Eli S. Meza-Ramirez
creator: Eric Simancas Acevedo
creator: Karla Martínez-Tapia
creator: Morayma G. Orán-Muñoz
creator: Alfonso Ávila-Pérez Tagle
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 122
ending page: 145
doi: 10.61467/2007.1558.2026.v17i3.1367

Emotional AI in the Workplace: Systematic Review of Effects on Employee Well-Being, Productivity, and Organizational Performance
type: item
description: The rapid integration of Artificial Intelligence (AI) and affective computing technologies into organisational environments is transforming workplace dynamics. However, their impact on employee well-being and productivity remains fragmented and insufficiently synthesised. This study addresses this research gap through a systematic review of AI-powered emotional intelligence systems. It focuses on their effects across three core dimensions: employee attitudes (job satisfaction, motivation, adaptability), workplace behaviours (performance, creativity, technology adoption), and organisational dynamics (leadership, trust, team cohesion). Following the PRISMA framework, this study conducts a systematic evaluation and comparative analysis of the state-of-the-art literature. It identifies key patterns, methodological trends, and underexplored areas. The findings suggest that emotional AI systems may enhance employee engagement and organisational productivity when implemented within ethically grounded and transparent frameworks. However, the review also highlights critical challenges related to privacy, emotional surveillance, algorithmic bias, and employee trust. This study contributes a structured framework that clarifies the role of emotional AI in organisational contexts and outlines actionable, scalable strategies for real-world application. By consolidating dispersed evidence and proposing directions for future research, this study is intended to provide a benchmark for subsequent investigations into AI-driven emotional intelligence and its implications for sustainable, human-centred workplaces. Spanish-language metadata / Metadatos en español Título en español: La IA emocional en el lugar de trabajo: revisión sistemática de sus efectos sobre el bienestar de los empleados, la productividad y el desempeño organizacional Resumen: La rápida integración de la inteligencia artificial (IA) y las tecnologías de computación afectiva en los entornos organizacionales está trans…
creator: Jorge A. Ruiz-Vanoye
creator: Jaime Aguilar-Ortiz
creator: Ocotlán Diaz-Parra
creator: Ricardo A. Barrera-Cámara
creator: Alejandro Fuentes-Penna
creator: Miguel Á. Ruiz-Jaimes
creator: Yadira Toledo-Navarro
creator: Marco A. Vera-Jiménez
creator: Alicia Ortiz-Montes
creator: Marco Tulio Valdez-Acosta
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 146
ending page: 165
doi: 10.61467/2007.1558.2026.v17i3.1398

Intellekt: A Machine Learning Based Framework for Advanced Muscle Strain Severity Detection Using IoT Devices
type: item
description: As global populations grow and technology advances, daily life is increasingly shaped by digital tools like computers and smart devices. However, prolonged use has led to rising physical and mental health issues, particularly due to poor sitting posture. Posture-related strain, often overlooked, contributes significantly to musculoskeletal issues including back, neck, shoulder, and wrist pain, and may also be associated with sleep disturbances and elevated stress levels. To the best of our knowledge based on existing literature, this is the first study to introduce Intellekt, a machine learning–based framework for advanced muscle strain severity detection using IoT devices that integrates both posture and muscle strain detection into a unified, low-cost ($23 hardware) framework aimed at mitigating these risks. Specifically, this study makes four key contributions: (1) We created a novel real-time dataset by collecting electromyography (EMG) and posture data from participants in university, bank, and industrial environments, acquiring diverse muscle strain patterns validated against clinical assessment protocols; (2) We designed a two-part hardware framework consisting of Posture Detection (PD) and Strain Detection (SD) modules using Node MCU ESP8266, ultrasonic sensor HC SR04, EMG sensor, and buzzer for real-time user monitoring, featuring EMG-specific signal processing including band pass filtering, rectification, and RMS smoothing; (3) We proposed and evaluated a custom hybrid machine learning approach, referred to as the Intellekt model, that categorizes muscle strain severity into mild, moderate, and severe using EMG signals, with thresholds clinically correlated to tissue damage levels; (4) Our proposed model Intellekt achieved an accuracy of 99% (95% CI: 99.1-99.8%) with 15.2ms inference latency. Spanish-language metadata / Metadatos en español Título en español: Intellekt: un marco basado en el aprendizaje automático para la detección avanzada de la gravedad …
creator: Umyh Habiba
creator: Muhammad Ahmad
creator: Fida Ullah
creator: Muhammad Imran
creator: Ildar Batyrshin
creator: Grigori Sidorov
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 166
ending page: 181
doi: 10.61467/2007.1558.2026.v17i3.1395

Technology Applied to Identifying Areas without Access to Drinking Water: Challenges and Opportunities
type: item
description: Safe access to drinking water remains a critical priority for governments and international organizations. The World Health Organization estimates that more than 2.2 billion people lack safely managed water services, which has serious consequences for health and economic development. In this context, different technologies, and especially artificial intelligence (AI), are emerging as innovative tools that can help identify, more accurately and quickly, areas with inadequate drinking water service coverage. (WHO & UNICEF, 2021) This study analyzes the applications of machine learning and statistical techniques to identify sectors of society with a lack of drinking water supply. It also describes methodological approaches that integrate sociodemographic, environmental, and water infrastructure data with predictive models based on neural networks and decision trees. (Mutiara, Andriani, & Rahadi, 2021) Finally, the challenges associated with data quality, the replicability of models in different contexts, and the need for public policies that promote the responsible adoption of these cutting-edge technologies are discussed. (Shahriar, Rahman, & Islam, 2023). Spanish-language metadata / Metadatos en español Título en español: La tecnología aplicada a la identificación de zonas sin acceso al agua potable: retos y oportunidades Resumen: El acceso seguro al agua potable sigue siendo una prioridad fundamental para los gobiernos y las organizaciones internacionales. La Organización Mundial de la Salud calcula que más de 2.200 millones de personas carecen de servicios de agua gestionados de forma segura, lo que tiene graves consecuencias para la salud y el desarrollo económico. En este contexto, diversas tecnologías, y especialmente la inteligencia artificial (IA), se están perfilando como herramientas innovadoras que pueden ayudar a identificar, con mayor precisión y rapidez, las zonas con una cobertura insuficiente del servicio de agua potable. (OMS y UNICEF, 2021). Este est…
creator: Miguel Angel Torres González
creator: Rosalía América Juárez Aguirre
creator: Manuel Antonio Vivanco Cabecera
creator: Sonia Gómez Gómez
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 182
ending page: 189
doi: 10.61467/2007.1558.2026.v17i3.1358

Blockchain for electronic voting: A security approach
type: item
description: This paper proposes an electronic voting system based on blockchain technology with the aim of guaranteeing the integrity, transparency, and automation of the electoral process. The developed solution uses smart contracts for the decentralized registration and counting of votes, eliminating human intervention in results processing and ensuring the immutability of the stored information. The results obtained demonstrate that the system prevents duplicate votes, preserves the traceability of each record, and generates high levels of trust among participating users. The research demonstrates the viability of blockchain technology as an alternative to strengthen the reliability of digital electoral processes and establishes a solid foundation for future large-scale implementations. Spanish-language metadata / Metadatos en español Título en español: La cadena de bloques para el voto electrónico: un enfoque de seguridad Resumen: Este artículo propone un sistema de votación electrónica basado en la tecnología blockchain con el objetivo de garantizar la integridad, la transparencia y la automatización del proceso electoral. La solución desarrollada utiliza contratos inteligentes para el registro y el recuento descentralizados de los votos, lo que elimina la intervención humana en el procesamiento de los resultados y garantiza la inmutabilidad de la información almacenada. Los resultados obtenidos demuestran que el sistema evita los votos duplicados, garantiza la trazabilidad de cada registro y genera un alto nivel de confianza entre los usuarios participantes. La investigación demuestra la viabilidad de la tecnología blockchain como alternativa para reforzar la fiabilidad de los procesos electorales digitales y sienta una base sólida para futuras implementaciones a gran escala. Palabras Claves: Cadena de bloques, Voto electrónico, Ciberseguridad, Descentralización, Integridad de los datos Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i3.1272 Dimension…
creator: Miguel Enrique De-La-Rosa-Lopez
creator: Evangelina Lezama-León
creator: Alonso Ernesto Solís-Galindo
creator: Israel Acuña-Galv´án
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 190
ending page: 198
doi: 10.61467/2007.1558.2026.v17i3.1272

Human-Machine Interfaces in Health: A Critical Review and Framework of Human-Centered Design Practices
type: item
description: The design of human-machine interfaces (HMIs) in healthcare is a determining factor for the safety, efficiency, and accessibility of medical technologies worldwide. This article presents a structured review of the scientific and regulatory literature published to 2025, with the main objective of identifying the principles, limitations, and best practices applicable to the development of interactive systems in the healthcare sector. This analysis integrates a range of contributions from biomedical engineering, cognitive ergonomics, and user-centered design, highlighting key challenges associated with technological complexity and user diversity. Based on this review, a best practice framework is proposed, consisting of four pillars: visual design, interaction and control, accessibility and inclusion, and continuous evaluation. This framework translates international standards into a series of operational actions that strengthen patient safety and promote a more humanistic and inclusive design focused on the user. The results presented here demonstrate the need to consolidate a set of interdisciplinary methodologies that guarantee usability and equity in technological innovation in health. Spanish-language metadata / Metadatos en español Título en español: Interfaces hombre-máquina en el ámbito de la salud: una revisión crítica y un marco de prácticas de diseño centrado en las personas Resumen: El diseño de las interfaces hombre-máquina (IHM) en el ámbito de la salud es un factor determinante para la seguridad, la eficiencia y la accesibilidad de las tecnologías médicas en todo el mundo. Este artículo presenta una revisión estructurada de la literatura científica y normativa publicada hasta 2025, con el objetivo principal de identificar los principios, las limitaciones y las mejores prácticas aplicables al desarrollo de sistemas interactivos en el sector de la salud. Este análisis integra una serie de aportaciones procedentes de la ingeniería biomédica, la ergonomía co…
creator: José Pablo Benitez Mazariegos
creator: Evelin Gutiérrez Moreno
creator: Justo Fabian Montiel Hernández
creator: Javier Hernández Pérez
creator: Mario Oscar Ordaz Oliver
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 199
ending page: 210
doi: 10.61467/2007.1558.2026.v17i3.1285

Interactive 3D Visualization Engine for Molecular Docking and Structural Complex Analysis
type: item
description: This paper presents the initial development of a new docking analysis tool leveraging WebGL for real-time molecular visualization directly in the browser eliminating the installation burden and offering a fresh level of interactivity and modularity in docking result interpretation. The methodology consists of developing a web API in the Python programming language, implementing the interaction between the React Framework and the underlaying Python script to process data, cluster the docked conformation, calculate the secondary structure of the protein PDB file and retrieve the results to the React Framework to visual representation. The tool renders visual components of the 3D molecular structure viewer in react-three/fiber, react-three/drei, and WebGL, providing a user-friendly and modern interface; and leveraging the computational pipeline capabilities to reduce CPU load to the WebGL engine. Spanish-language metadata / Metadatos en español Título en español: Motor de visualización 3D interactivo para el acoplamiento molecular y el análisis de complejos estructurales Resumen: Este artículo presenta el desarrollo inicial de una nueva herramienta de análisis de acoplamiento que aprovecha WebGL para la visualización molecular en tiempo real directamente en el navegador, lo que elimina la necesidad de instalación y ofrece un nuevo nivel de interactividad y modularidad en la interpretación de los resultados del acoplamiento. La metodología consiste en desarrollar una API web en el lenguaje de programación Python, implementando la interacción entre el marco React y el script de Python subyacente para procesar datos, agrupar la conformación acoplada, calcular la estructura secundaria del archivo PDB de la proteína y enviar los resultados al marco React para su representación visual. La herramienta representa los componentes visuales del visor de estructuras moleculares en 3D en React-Three/Fiber, React-Three/Drei y WebGL, lo que ofrece una interfaz moderna y fácil de usar…
creator: Ernesto Alan Borjas Torres
creator: Carlos A. García Pérez
creator: Adolfo Josué Rodríguez Rodríguez
creator: Alfredo Juárez Saldivar
creator: Wenceslao Eduardo Rodríguez Rodríguez
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 211
ending page: 218
doi: 10.61467/2007.1558.2026.v17i3.1274

The Big data in organizations: A systematic review of impact, efficiency, and predictive capacities
type: item
description: The rapid growth of data volumes in organizational environments has accelerated the adoption of Big Data and advanced analytics as key components of digital transformation. However, alongside potential gains in efficiency, innovation, and evidence-based decision-making, significant risks and challenges arise in technological, organizational, ethical, and human-capital domains. This study aims to conduct a systematic review of recent literature on Big Data, artificial intelligence, and machine learning applied to decision-making in business and organizational contexts, to identify their main uses and applications, as well as the benefits, risks, challenges, and factors influencing effective adoption. The review followed PRISMA Statement recommendations for the search, selection, and critical appraisal of studies, applying predefined inclusion and exclusion criteria. The study concludes that responsible adoption of these technologies requires robust infrastructures, solid data governance frameworks, strategic leadership, continuous upskilling, and an organizational culture oriented toward ethics and the critical use of information. Spanish-language metadata / Metadatos en español Título en español: El big data en las organizaciones: una revisión sistemática del impacto, la eficiencia y las capacidades predictivas Resumen: El rápido crecimiento del volumen de datos en los entornos organizacionales ha acelerado la adopción del Big Data y la analítica avanzada como componentes clave de la transformación digital. Sin embargo, junto con las posibles ventajas en materia de eficiencia, innovación y toma de decisiones basada en datos, surgen riesgos y desafíos significativos en los ámbitos tecnológico, organizacional, ético y del capital humano. El objetivo de este estudio es realizar una revisión sistemática de la bibliografía reciente sobre el big data, la inteligencia artificial y el aprendizaje automático aplicados a la toma de decisiones en contextos empresariales y orga…
creator: Ulises Daniel Barradas Arenas
creator: José Felipe Cocón Juárez
creator: Dámaris Pérez Cruz
creator: Ma. del Rosario Vázquez Aragón
creator: José Gabriel Reding Domínguez
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 219
ending page: 233
doi: 10.61467/2007.1558.2026.v17i3.1338

The Use of renewable energies in educational institutions: feasibility and environmental benefits
type: item
description: The use of renewable energy has become a strategic alternative in the face of growing energy demand and the need to reduce polluting emissions that affect the environment. In the context of educational institutions, the transition to clean sources is particularly relevant, as it not only generates economic savings but also contributes to raising environmental awareness among students and communities. The aim of this study is to analyze the feasibility of implementing renewable energy systems in schools and universities, as well as to identify the environmental benefits derived from their application. Through a literature review and analysis of documented cases, it is evident that technologies such as solar and wind energy can cover a large part of the energy needs of educational centers, achieving a significant reduction in the carbon footprint and optimization of operating costs in the medium term. It is concluded that the incorporation of renewable energies in the education sector is not only feasible from a technical and economic point of view but also constitutes an effective strategy for promoting sustainability and strengthening the social responsibility of institutions. Spanish-language metadata / Metadatos en español Título en español: El uso de energías renovables en las instituciones educativas: viabilidad y beneficios medioambientales Resumen: El uso de energías renovables se ha convertido en una alternativa estratégica ante la creciente demanda energética y la necesidad de reducir las emisiones contaminantes que afectan al medio ambiente. En el contexto de las instituciones educativas, la transición hacia fuentes limpias reviste especial importancia, ya que no solo genera ahorros económicos, sino que también contribuye a fomentar la conciencia ambiental entre los estudiantes y las comunidades. El objetivo de este estudio es analizar la viabilidad de implementar sistemas de energía renovable en escuelas y universidades, así como identificar los beneficios…
creator: Francisco Fabián Tobías Macías
creator: Gustavo Emilio Rojo Velázquez
creator: Juan Antonio Serrato Frausto
creator: Carlos Hernández Santos
creator: Roxana García Andrade
creator: Amadeo Hernández Hernández
creator: Gerson Lozano Orta
creator: Silvia Imelda Castro Elizondo
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 234
ending page: 248
doi: 10.61467/2007.1558.2026.v17i3.1323

The Automotive sector on the industry side
type: item
description: The Mexican automotive industry is a pillar of the national economy, with a strong presence of assembly plants from global manufacturers such as Volkswagen, General Motors, Nissan, and Audi. Thanks to its strategic location and competitive production costs, Mexico has established itself as the seventh largest vehicle producer and the fourth largest exporter worldwide. The sector has evolved through the incorporation of advanced materials—such as aluminum, high-strength polymers, and carbon fiber—that allow for reduced vehicle weight and improved energy efficiency. Likewise, automation and the use of artificial intelligence have optimized assembly lines, raising quality and reducing costs. The transition to electric vehicles is progressing with significant production growth, although challenges remain, such as limited charging infrastructure, high battery costs, and a continued preference for internal combustion engines. In the trade arena, tariffs imposed by the United States have impacted exports, forcing the industry to adjust its production and diversify markets, while simultaneously strengthening sustainability and flexible manufacturing strategies.
creator: Francisco Fabián Tobías Macías
creator: Juan Antonio Serrato Frausto
creator: Rogelio Galván Ramos
creator: Carlos Hernández Santos
creator: Roxana García Andrade
creator: Herman Fernando Castro Camargo
creator: Otoniel Arreaga Garza
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 249
ending page: 260
doi: 10.61467/2007.1558.2026.v17i3.1322

A Hybrid Bio-Inspired Roach Infestation Algorithm with Interval Type-2 Fuzzy Adapter for Large-Scale Optimization: An extensive Evaluation
type: item
description: The Swarm-based metaheuristic algorithms have been widely adopted for solving large-scale optimization problems due to their flexibility and population-based search mechanisms. Within this family, Roach Infestation Optimization (RIO) leverages collective interactions and hunger-related dynamics to promote global exploration. Despite these advantages, its search behavior is highly sensitive to parameter settings, especially in high-dimensional spaces, where an improper balance between exploration and exploitation can reduce convergence efficiency. This study presents an adaptive strategy based on Interval Type-2 Fuzzy Logic (IT2FL) to regulate the key RIO parameters, C_maxand C_o, according to the evolutionary state of the search process. By explicitly modeling uncertainty in the parameter adaptation mechanism, the proposed framework enables smoother and more robust adjustments compared to static configurations and Type-1 fuzzy approaches. The proposed method is evaluated on large-scale benchmark optimization problems with dimensionalities ranging from 50 to 500. Its performance is systematically compared to the original RIO algorithm, a Type-1 fuzzy-enhanced RIO variant, Cuckoo Search, and Particle Swarm Optimization (PSO). Experimental results demonstrate consistent improvements in solution quality, convergence stability, and robustness across different problem instances. Furthermore, the statistical significance of the observed improvements is validated through statistical analysis based on Z-tests, confirming the effectiveness of the proposed IT2FL adaptation in high-dimensional optimization scenarios. Spanish-language metadata / Metadatos en español Título en español: Un algoritmo híbrido bioinspirado para la infestación de cucarachas con un adaptador difuso de tipo 2 por intervalos para la optimización a gran escala: una evaluación exhaustiva Resumen: Los algoritmos metaheurísticos basados en enjambres se han adoptado ampliamente para resolver problemas de opti…
creator: Enrique Lizárraga
creator: Fevrier Valdez
creator: Oscar Castillo
creator: Patricia Melin
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 261
ending page: 286
doi: 10.61467/2007.1558.2026.v17i3.1377

Spectral analysis of distorted and autoencoder-reconstructed MNIST images in a kernel-induced feature space
type: item
description: In this paper, the unsupervised learning spectral methods Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) were explored. PCA captured the maximum variance of MNIST images, including their distorted and autoencoder-reconstructed versions, and KPCA, that captured the maximum variance and nonlinear structures of the feature space where the same images were mapped. Accordingly, for KPCA the kernel functions: a polynomial of degree 5, a radial basis function (RBF), and a cosine kernel, were evaluated. A correlation analysis between the distortions level, the digit inclination and the anisotropic of pixel values of the same images and the PCA-derived components from KPCA was calculated. The distortion level and anisotropy parameters appear to have strong nonlinear correlation with PCA-derived components, especially for the polynomial and the RBF kernels. Conversely, for cosine kernel weaker correlations were observed. Moreover, the digit inclination parameter appears unrelated to the PCA-derived components for all three kernels. Spanish-language metadata / Metadatos en español Título en español: Análisis espectral de imágenes MNIST distorsionadas y reconstruidas mediante autoencoders en un espacio de características inducido por kernel Resumen: En este artículo se analizaron los métodos espectrales de aprendizaje no supervisado: el Análisis de Componentes Principales (PCA) y el Análisis de Componentes Principales con Kernel (KPCA). El PCA capturó la máxima varianza de las imágenes MNIST, incluidas sus versiones distorsionadas y las reconstruidas mediante autoencodificadores, mientras que el KPCA capturó la máxima varianza y las estructuras no lineales del espacio de características en el que se mapearon las mismas imágenes. En consecuencia, para el KPCA se evaluaron las siguientes funciones del núcleo: un polinomio de grado 5, una función de base radial (RBF) y un núcleo coseno. Se calculó un análisis de correlación entre el nivel de dist…
creator: Rafael Castañeda Díaz
creator: Daniela López-Betancur
creator: Carlos Guerrero-Méndez
creator: Efrén González-Ramírez
creator: Flossi Puma-Ttito
creator: V.H. Carrera-Escobedo
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 287
ending page: 301
doi: 10.61467/2007.1558.2026.v17i3.1457

EMG Hand-Gesture Classification Using Time, Frequency and Time–Frequency Domain Features with Machine-Learning Techniques
type: item
description: This paper investigates single-channel surface electromyography for seven hand gestures using a compact feature-based machine-learning pipeline. EMG from twelve participants was segmented into contraction-level windows and described by time-, frequency- and time–frequency-domain features, complemented with simple feature-engineering transformations. Support vector machines with linear, RBF and polynomial kernels and a multilayer perceptron were evaluated. First, models were trained on the full feature set, obtaining a no-PCA baseline where linear and polynomial SVM achieved accuracies slightly above ninety-two percent and the neural network reached about eighty-nine percent. Then, Principal Component Analysis was applied, retaining two, five, nine and twelve components. Very low dimensionality degraded all models, whereas retaining nine to twelve components slightly improved RBF SVM and clearly benefited the neural network, which reached almost ninety-four percent accuracy. Spanish-language metadata / Metadatos en español Título en español: Clasificación de gestos de la mano mediante EMG utilizando características de los dominios del tiempo, la frecuencia y el tiempo-frecuencia con técnicas de aprendizaje automático Resumen: Este artículo analiza la electromiografía de superficie monocanal para siete gestos de la mano mediante un proceso compacto de aprendizaje automático basado en características. Se segmentó el EMG de doce participantes en ventanas a nivel de contracción y se describió mediante características en los dominios del tiempo, la frecuencia y el tiempo-frecuencia, complementadas con transformaciones sencillas de ingeniería de características. Se evaluaron máquinas de vectores de soporte con núcleos lineales, RBF y polinómicos, así como un perceptrón multicapa. En primer lugar, los modelos se entrenaron con el conjunto completo de características, obteniéndose una referencia sin PCA en la que las SVM lineales y polinómicas alcanzaron precisiones ligerame…
creator: José Manuel López Villagómez
creator: Ruth Ivonne Mata-Chávez
creator: Juan Manuel López-Hernández
creator: Jorge Manuel Sánchez-Barrios
creator: Miguel Alfonso López-Alonso
creator: José Manuel Moreno-Loera
creator: Alberto Nicolas-Moreno
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 302
ending page: 316
doi: 10.61467/2007.1558.2026.v17i3.1251

Generation of a Multispectral Dataset Based on Satellite Images for Poverty Prediction at Municipal Level in Mexico
type: item
description: Poverty in Mexico is measured biennially through socioeconomic surveys identifying people with income deficiencies and six social dimensions. This approach faces significant limitations: high costs, operational difficulties in regions with violence, and restricted capacity for timely policy responses, as evidenced during COVID-19. Emerging methods based on satellite imagery and remote sensing expand poverty analysis scope and efficiency. However, implementation faces challenges from scarce primary data, particularly satellite imagery and field measurements essential for predictive model training. This article develops a multispectral database for Mexico designed to facilitate training and validation of advanced machine learning algorithms for poverty prediction. This contribution provides a methodological tool for designing robust and scalable approaches while reducing reliance on traditional socio-economic survey methods. Spanish-language metadata / Metadatos en español Título en español: Creación de un conjunto de datos multiespectrales a partir de imágenes satelitales para la predicción de la pobreza a nivel municipal en México Resumen: En México, la pobreza se mide cada dos años mediante encuestas socioeconómicas que identifican a las personas con ingresos insuficientes y tienen en cuenta seis dimensiones sociales. Este enfoque presenta importantes limitaciones: altos costos, dificultades operativas en regiones afectadas por la violencia y una capacidad limitada para dar respuestas políticas oportunas, como quedó demostrado durante la pandemia de COVID-19. Los métodos emergentes basados en imágenes satelitales y teledetección amplían el alcance y la eficacia del análisis de la pobreza. Sin embargo, su implementación se enfrenta a dificultades derivadas de la escasez de datos primarios, en particular de imágenes satelitales y mediciones sobre el terreno, que son esenciales para el entrenamiento de los modelos predictivos. Este artículo presenta una base de datos …
creator: Armida Castillo González
creator: Vitervo Lopez Caballero
creator: Alan Hernández-Solano
creator: Lucia Morales Morales
rights: Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics https://creativecommons.org/licenses/by-nc-nd/4.0/
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date: 2026-06-12
publication date: 2026-06-12
volume: 17
number: 3
starting page: 317
ending page: 328
doi: 10.61467/2007.1558.2026.v17i3.1283