Publications

@article{202607012,

title = {The Computational Theory of Mind: Ethical and Philosophical Implications in the Age of Artificial Intelligence},

author = {Jorge A. Ruiz-Vanoye and Ocotlán Díaz-Parra and Marco A. Vera-Jiménez and Francisco R. Trejo-Macotela},

doi = {https://doi.org/10.61467/2007.1558.2026.v17i1.1245},

issn = {2007-1558},

year  = {2026},

date = {2026-01-07},

urldate = {2026-01-07},

journal = {International Journal of Combinatorial Optimization Problems and Informatics},

volume = {17},

issue = {1},

pages = {90-100},

abstract = {The Computational Theory of Mind (CTM) posits that the human mind operates in ways analogous to a computer, processing information through symbolic representations and formal rules. With the advent of Artificial Intelligence (AI) and Large Language Models (LLMs), the scope and implications of CTM have broadened considerably. This paper examines the ethical and philosophical dilemmas associated with research on and applications of CTM, with particular attention to human enhancement, privacy, consent, moral responsibility, and free will. It also considers how CTM intersects with longstanding philosophical debates on consciousness and personal identity, while addressing challenges raised by alternative perspectives, such as embodied cognition. Rather than advancing a prescriptive stance, the paper argues for a balanced approach that seeks to leverage technological developments while safeguarding human values and identity in an increasingly AI-driven context.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{jcr202510261,

title = {Urban Water-Planning Support System Using Fuzzy Logic and Metaheuristic Algorithms Under Sustainability Criteria},

author = {Jaime Aguilar-Ortiz and Francisco R. Trejo-Macotela and Ocotlán Diaz-Parra and Jorge A. Ruiz-Vanoye and Marco Antonio Vera Jiménez and Víctor M. Zamudio-García},

doi = {https://doi.org/10.15244/pjoes/216484},

issn = {1230-1485},

year  = {2026},

date = {2026-01-05},

urldate = {2025-10-26},

journal = {Polish Journal of Environmental Studies},

keywords = {Papers in the Science Citation Index Expanded},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{DiazParra2026SmartUrbanSynergy,

title = {Smart Urban Synergy: A Systems-Based Approach to Assessing Smart and Sustainable Cities},

author = {Ocotlán Díaz-Parra and Jorge A. Ruiz-Vanoye and Juan M. Xicoténcatl-Pérez and Alejandro Fuentes-Penna and Ricardo A. Barrera-Cámara and Francisco R. Trejo-Macotela and Jaime Aguilar-Ortiz and Marco A. Vera-Jiménez},

url = {https://doi.org/10.3390/systems14010074},

doi = {10.3390/systems14010074},

issn = {2079-8954},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {Systems},

volume = {14},

number = {1},

pages = {74},

abstract = {Smart cities aim to integrate technological, infrastructural, and socio-environmental systems in order to improve urban sustainability and quality of life. To qualify as both smart and sustainable, a city is generally expected to pursue self-sufficiency through the adoption of sustainable practices in energy production, water supply, and food systems. Such cities also seek to reduce operational costs for both private operators and municipalities, while aiming to enhance the quality of life of their residents. Within this context, the relevance of a web-based application becomes particularly apparent. An application equipped with predefined indicators can provide a structured and measurable framework for assessing the current status of a city or town in relation to smart and sustainable development. This framework allows for the evaluation of the extent to which a city aligns with established criteria associated with smart and sustainable urban models. This paper introduces a Python-based web application, developed using Python version 3.10, designed to assess or support the self-assessment of a city’s alignment with identified smart and sustainable development indicators. This study does not claim empirical validation or benchmarking performance; the proposed system is presented as a proof-of-concept framework. The work does not propose new smart city indicators. Rather, it presents an integrative system that seeks to operationalise existing smart and sustainable city indicators within a unified and modular web-based assessment framework, designed to support cross-domain evaluation and citizen-accessible self-assessment.},

keywords = {Papers in the Science Citation Index Expanded},

pubstate = {published},

tppubtype = {article}

}

@article{jcr2510162,

title = {Early Lung Cancer Detection via AI-Enhanced CT Image Processing Software},

author = {Joel Silos-Sánchez and Jorge A. Ruiz-Vanoye and Francisco R. Trejo-Macotela and Marco A. Márquez-Vera and Ocotlán Diaz-Parra and Josué R. Martínez-Mireles and Miguel A. Ruiz-Jaimes and Marco A. Vera-Jiménez},

doi = {https://doi.org/10.3390/diagnostics15212691},

issn = { 2075-4418},

year  = {2025},

date = {2025-10-26},

urldate = {2025-10-26},

journal = {Diagnostics},

volume = {15},

issue = {21},

pages = {2691},

keywords = {Papers in the Science Citation Index Expanded},

pubstate = {published},

tppubtype = {article}

}

@article{jcr2510264,

title = {Multiclass Classification of Neurological and Psychiatric Conditions Using Synthetic Neuroinformatics Biomarkers and EEG Band Simulation},

author = {Jorge A. Ruiz-Vanoye and Ocotlán Diaz-Parra and Francisco R. Trejo-Macotela and Eric Simancas-Acevedo and Juan M. Xicoténcatl-Pérez and Marco A. Vera-Jiménez and José M. Liceaga-Ortiz-De-La-Peña},

doi = {https://doi.org/10.61467/2007.1558.2025.v16i4.1228},

issn = {2007-1558},

year  = {2025},

date = {2025-10-26},

urldate = {2025-10-26},

journal = {International Journal of Combinatorial Optimization Problems and Informatics},

volume = {16},

issue = {4},

pages = {96–111},

abstract = {This study presents a machine learning framework for multiclass classification of neurological and psychiatric disorders using synthetic neuroinformatics biomarkers and EEG spectral simulations. Synthetic data modeled cognitive-motor features (visual delay, motor gain, expectancy weight, memory capacity, EEG bands), while real EEG data from 50 PhysioNet recordings validated performance. An XGBoost model optimized through grid search (24 combinations, five-fold validation) achieved 97.8% accuracy and 0.978 F1-score. Results showed excellent classification of neurotypical, ADHD, ASD, dementia, depression, GAD, Parkinson’s, psychosis, and Tourette’s, demonstrating strong potential for neuropsychiatric diagnosis support.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{jcr091020251,

title = {Rethinking cognitive recalibration: Integrating AI into the Management of Affordances across the lifestages},

author = {Jorge A. Ruiz-Vanoye and Ocotlán Díaz-Parra and Francisco R. Trejo-Macotela and Jaime Aguilar-Ortiz},

doi = {https://doi.org/10.1017/S0140525X25103361},

issn = {0140-525X},

year  = {2025},

date = {2025-10-09},

urldate = {2025-10-09},

journal = {Behavioral and Brain Sciences},

abstract = {This paper integrates artificial intelligence into affordance management to quantify and

simulate perceived opportunities and threats across the life course. Using computer vision,

reinforcement learning, and digital twins, we outline methods to capture objective social

indicators, model adaptive strategies, and test counterfactual scenarios. These approaches

advance evolutionary and developmental psychology from descriptive to predictive frameworks, enhancing the study of cognitive recalibration and enriching the empirical and

theoretical understanding of human adaptation.},

keywords = {Papers in the Science Citation Index Expanded},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{jcr091020252,

title = {Sustainable Transportation Optimisation of Waste Electrical and Electronic Equipment Using AI-Based Evolutionary Algorithms},

author = {Jorge A. Ruiz-Vanoye and Ocotlán Díaz-Parra and Francisco R. Trejo-Macotela and José M. Liceaga-Ortiz-De-La-Peña and Myrna Lezama-León and Evangelina Lezama-León and Jaime Aguilar-Ortiz and Alejandro Fuentes-Penna},

doi = {https://doi.org/10.3390/su17188389},

year  = {2025},

date = {2025-10-09},

urldate = {2025-10-09},

journal = {Sustainability},

volume = {17},

issue = {18},

pages = {8389},

abstract = {Waste Electrical and Electronic Equipment (WEEE) management is a critical global challenge. This study proposes a model for the WEEE Transportation Problem using advanced evolutionary algorithms such as the Genetic Algorithm (GA), the Offspring-Selected Genetic Algorithm (OSGA), the Evolution Strategy (ES), and the Offspring-Selected Evolution Strategy (OSES). These algorithms, which are part of the field of Artificial Intelligence (AI), are applied to optimise transportation routes, minimising time and costs, and promoting sustainability by reducing the carbon footprint. Test instances and solutions are presented to demonstrate the feasibility of the model and the effectiveness of the proposed algorithms. Rather than providing technical detail, the focus is placed on the novelty of applying these algorithms to the WEEE Transportation Problem in Mexico, particularly for minimising operational cost. While reductions in carbon emissions are discussed as a natural consequence of cost optimisation, a formal dual-objective formulation is beyond the present scope and is identified as a direction for future work.},

keywords = {Papers in the Science Citation Index Expanded},

pubstate = {published},

tppubtype = {article}

}

@article{jcr2510263,

title = {Detection of Silent Water Leaks in Household Using Artificial Intelligence Methods},

author = {Uriel A. Ramírez-Hernández and Francisco R. Trejo-Macotela and Daniel Robles-Camarillo and Jorge A. Ruiz-Vanoye and Eric Simancas-Acevedo and Rocío Ortega-Palacios and Julio C. Ramos-Fernández},

doi = {10.13053/CyS-29-3-5801},

issn = {2007-9737},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

journal = {Computación y Sistemas},

volume = {29},

issue = {3},

pages = {1833–1843},

abstract = {Water losses in distribution systems constitute a significant global challenge, undermining water resource sustainability, increasing operational costs, and threatening the water security of millions. In Latin America, up to 40% of treated water is reportedly lost due to leaks, ruptures, and defective connections (Xylem, 2025). At the household level, silent leaks—particularly in toilet flushing systems—can waste over 37,850 litres annually per dwelling (US EPA, 2024). Various international studies have addressed early leak detection using intelligent systems. In Europe, wireless sensor networks and machine learning models such as Random Forest, Support Vector Machines, and neural networks have been deployed for anomaly detection in urban networks. Asian research has demonstrated detection accuracies exceeding 97% through convolutional neural networks trained on acoustic and vibrational signals, enhanced by contrastive learning to address data scarcity. Hybrid approaches combining hydraulic modelling with AI have been applied in the Middle East and China, whereas logic-based and anomaly detection algorithms have been integrated into real-time platforms in Australia and Canada. Sensor placement optimisation via graph partitioning has further improved coverage efficiency. Despite their effectiveness, these solutions often require substantial investment and advanced infrastructure, limiting their applicability in resource-constrained environments. This study proposes a cost-effective, perceptron-based model for detecting silent leaks in household toilets, integrated within an Internet of Things (IoT) framework. The system employs a Hall-effect flow sensor to capture high-resolution filling-time and pulse-count data, processed through supervised learning to discriminate between normal consumption and leakage. Experimental results under real-use conditions achieved 98% classification accuracy, demonstrating both technical feasibility and operational suitability. This approach offers a practical, computationally efficient solution for domestic contexts in Latin America, enabling real-time monitoring and immediate user alerts, thus supporting water conservation efforts through accessible intelligent detection.},

keywords = {CONAHCYT},

pubstate = {published},

tppubtype = {article}

}

@article{jcr56202512,

title = {Optimising Water Management in Urban Ecosystems: A Holistic Model for the Sustainable Integration of Drinking Water, Rainwater and Wastewater Systems},

author = {Ocotlán Diaz-Parra and Jaime Aguilar-Ortiz and Jorge A. Ruiz-Vanoye and Francisco R. Trejo-Macotela and María B. Bernábe-Loranca},

doi = {https://doi.org/10.15244/pjoes/207035},

issn = {1230-1485},

year  = {2025},

date = {2025-07-21},

urldate = {2025-06-05},

journal = {Polish Journal of Environmental Studies},

abstract = {This paper presents a mathematical model for optimising the allocation of drinking, rain and recycled water in smart cities. Employing a genetic algorithm across various scenarios, the model achieves a marked improvement in urban water-use efficiency. The findings demonstrate the approach’s potential to inform sustainable water-management strategies, thereby advancing environmental conservation and resource sustainability in modern urban environments.},

keywords = {Papers in the Science Citation Index Expanded},

pubstate = {published},

tppubtype = {article}

}

@article{esci4720251,

title = {Modelling and Comparison of Machine-Learning Algorithms for Energy Consumption Prediction in Smart Buildings},

author = {Luis Arturo Ortiz-Suarez and Fernando Perez-Tellez and Jorge A. Ruiz-Vanoye and Francisco Rafael Trejo-Macotela and Eric Simancas-Acevedo and Jazmín Rodríguez Flores and Ocotlán Diaz-Parra and José M. Liceaga-Ortiz-De-La-Peña},

doi = {10.13053/CyS-29-2-5663},

issn = {1405-5546},

year  = {2025},

date = {2025-07-01},

journal = {Computación y Sistemas},

volume = {29},

issue = {2},

pages = {883–891},

abstract = {One-third of global energy demand is attributed to consumption in buildings, with HVAC and lighting systems as the primary contributors. This study presents the development and comparison of several machine-learning algorithms for predicting energy consumption in a building simulated using EnergyPlus and following the Team Data Science Process (TDSP) methodology. Feature-selection techniques (feature selection and feature importance) were applied to identify the most influential variables. Five predictive models were trained: MLP, SVR, XGBoost, Random Forest and Keras Regressor. Results demonstrate that the MLP model achieved the highest accuracy, while XGBoost showed greater stability. Additionally, traditional statistical models (ARIMA and SARIMAX) were compared to machine-learning models for multi-horizon prediction.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{esci4720252,

title = {Application of GANs to Augment the Mammography Repository},

author = {Yaneth Reyes-Hernández and Fernando Perez-Tellez and Jorge A. Ruiz-Vanoye and Jazmín Rodríguez-Flores and Eric Simancas-Acevedo and Ocotlán Diaz-Parra and Jaime Aguilar-Ortiz and Francisco R. Trejo-Macotela},

doi = { 10.13053/CyS-29-2-5664},

issn = {1405-5546},

year  = {2025},

date = {2025-07-01},

journal = {Computación y Sistemas},

volume = {29},

issue = {2},

pages = {893–900},

abstract = {Generative adversarial networks (GANs) offer an innovative approach to synthetic image generation. They have significantly impacted the creation of images that would otherwise be difficult to obtain. In this study, we examine several GAN architectures to determine whether they can generate synthetic mammography images to enrich an existing repository, thereby improving AI training for breast-cancer detection and supporting research into this disease with a more diverse dataset.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{esci4720253,

title = {Development of an Autonomous Module for Weed Management in Agriculture},

author = {Rubén O. Hernández-Terrazas and Juan M. Xicoténcatl-Pérez and Julio C. Ramos-Fernández and Jorge A. Ruíz-Vanoye and José M. Liceaga-Ortiz-De-La-Peña},

doi = {10.13053/CyS-29-2-5662},

issn = {1405-5546},

year  = {2025},

date = {2025-07-01},

journal = {Computación y Sistemas},

volume = {29},

issue = {2},

pages = {917–923},

abstract = {In the state of Hidalgo, Mexico, agriculture faces critical challenges due to climate change, such as resource scarcity, while also striving to ensure food security. One of the most pressing issues in corn cultivation is inadequate weed management, which reduces productivity and increases production costs. This article proposes an autonomous system for weed detection and removal in corn fields, integrating artificial intelligence algorithms, real-time computer vision, and a robotic arm equipped with a laser and herbicide sprayer. Following a systematic review, the YOLOv8m model was selected for its real-time detection capabilities and its balance between accuracy and efficiency. The proposed system employs machine learning algorithms in Python, achieving over 80% accuracy in distinguishing corn plants from weeds. Preliminary results demonstrate precise weed control, reduced environmental impact, and feasibility of field integration with minimal human intervention. This project marks a significant advancement toward more efficient and sustainable precision agriculture. As future work, the hybrid tool will apply selective treatments based on the size and location of the weeds, further minimizing herbicide use.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{22720251,

title = {Prediction of PM10, SO2, NO2, O3, and CO Concentrations in Guadalajara Using ARIMA and Open Data with Python},

author = {Julio C. Salas-López and Juvencio S. Zarazúa-Silva and Jorge A. Ruiz-Vanoye and Eric Simancas-Acevedo and Julio C. Salgado-Ramírez and Ocotlán Díaz-Parra},

doi = {https://doi.org/10.61467/2007.1558.2025.v16i3.1114},

issn = {2007-1558},

year  = {2025},

date = {2025-07-01},

journal = {International Journal of Combinatorial Optimization Problems and Informatics},

volume = {16},

issue = {3},

pages = {23-35},

abstract = {Air quality in Guadalajara has deteriorated in recent years, becoming a serious health concern for the local population. In response, this project seeks to mitigate the impact of pollution by developing a prediction platform based on ARIMA models implemented in Python.



The system will analyse historical pollutant levels—including PM₂.₅, PM₁₀, SO₂, NO₂, O₃ and CO—enabling the anticipation of high-pollution episodes. Armed with this information, both citizens and authorities will be able to take timely preventative measures.



Given the growing interest in air quality and its implications for health, this tool will furnish valuable data for informed decision-making. Moreover, it will facilitate trend analysis and permit short-term forecasts, helping to identify potential pollution episodes before they occur.},

keywords = {CONAHCYT, ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{ESCI22720252,

title = {Software Development for Brain Glioma Detection Using Magnetic Resonance Imaging and Deep Learning Techniques},

author = {Luis E. Gaspar-Vargas and Karla A. Torres-Calva and Jorge A. Ruiz-Vanoye and

Ocotlán Díaz Parra and Eric Simancas-Acevedo and Julio C. Salgado-Ramirez},

doi = {https://doi.org/10.61467/2007.1558.2025.v16i3.1132},

issn = {2007-1558},

year  = {2025},

date = {2025-07-01},

journal = {International Journal of Combinatorial Optimization Problems and Informatics},

volume = {16},

issue = {3},

pages = {1-10},

abstract = {The detection of brain gliomas is a crucial clinical challenge that requires early, accurate diagnostic methods to improve patient outcomes. This work presents the development of a deep learning-based system for glioma detection, employing an ensemble of ResNet18, VGG16, and DenseNet121 models trained with MRI images. The preprocessing involved dataset curation, image normalisation, and mask generation through K-means clustering. The trained model was integrated into a web application, allowing users to upload images and receive immediate diagnostic feedback. Experimental results demonstrate promising accuracy rates and reliable segmentation performance. This research highlights the potential of artificial intelligence (AI) to augment traditional medical imaging techniques and assist clinical diagnosis.},

keywords = {CONAHCYT, ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{25625,

title = {Integrated Biomimetics: Natural Innovations for Urban Design, Smart Technologies and Human Health},

author = {Ocotlan Diaz-Parra and Francisco R. Trejo-Macotela and Jorge A. Ruiz-Vanoye and Jaime Aguilar-Ortiz and Miguel A. Ruiz-Jaimes and Yadira Toledo-Navarro and Alejandro Fuentes-Penna and Ricardo A. Barrera-Cámara and Julio C. Salgado-Ramírez},

doi = {https://doi.org/10.3390/app15137323},

issn = {2076-3417},

year  = {2025},

date = {2025-06-29},

urldate = {2025-06-25},

journal = {Applied Sciences},

volume = {15},

issue = {13},

pages = {7323},

abstract = {Biomimetics has emerged as a transformative interdisciplinary approach that harnesses nature’s evolutionary strategies to develop sustainable solutions across diverse fields. This study explores its integrative role in shaping smart cities, advancing artificial intelligence and robotics, innovating biomedical applications, and enhancing computational design tools. By analyzing the evolution of biomimetic principles and their technological impact, this work highlights how nature-inspired solutions contribute to energy efficiency, adaptive urban planning, bioengineered materials, and intelligent systems. Furthermore, the paper discusses future perspectives on Biomimetics -driven innovations, emphasizing their potential to foster resilience, efficiency, and sustainability in rapidly evolving technological landscapes. Particular attention is given to neuromorphic hardware, a biologically inspired computing paradigm that mimics neural processing through spike-based communication and analogue architectures. Key components such as memristors and neuromorphic processors enable adaptive, low-power, task-specific computation, with wide-ranging applications in robotics, AI, healthcare, and renewable energy systems. Furthermore, the paper analyses how self-organising cities, conceptualised as complex adaptive systems, embody biomimetic traits such as resilience, decentralised optimisation, and autonomous resource management.},

keywords = {Papers in the Science Citation Index Expanded},

pubstate = {published},

tppubtype = {article}

}

@article{jcr56202513s,

title = {Generating medical images of breast cancer using generative artificial intelligence},

author = {Reyes-Hernández and Jorge A. Ruiz-Vanoye and Jazmín Rodriguez Flores},

doi = {http://dx.doi.org/10.61467/2007.1558.2025.v16i1.1058},

issn = {2007-1558},

year  = {2025},

date = {2025-06-07},

urldate = {2025-06-07},

journal = {International Journal of Combinatorial Optimization Problems and Informatics},

volume = {16},

issue = {1},

pages = {266-274},

abstract = {In this research work we review the fact that although there are different sets of databases with medical images of mammograms of breast cancer, these data are not really sufficient for the training of artificial intelligence systems to find signs of breast cancer in the images, so we propose the creation of new images that help to complement the data sets allowing better training in the systems that help the diagnosis of this disease.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{jcr5620251,

title = {Synthetic Love in the Digital Age: AI-Mediated Emotional Investment and Gendered Romantic Dependence},

author = {Jorge A. Ruiz-Vanoye and Ocotlán Díaz-Parra and Francisco R. Trejo-Macotela and Ricardo A. Barrera-Cámara and Alejandro Fuentes-Penna},

doi = {doi:10.1017/S0140525X25102409},

issn = {0140-525X},

year  = {2025},

date = {2025-06-05},

urldate = {2025-06-05},

journal = {Behavioral and Brain Sciences},

abstract = {Digital technology has transformed romantic relationships, influencing men’s and

women’s emotional investment differently. Drawing on ‘Romantic Relationships Matter

More to Men than to Women’, this commentary examines how AI-driven dating, social

and virtual platforms reshape partner search and mourning. It analyses synthetic lovefrom digital mediation to AI companions-arguing that these technologies may reinforce

male emotional dependency and heighten post-break-up vulnerability.},

keywords = {Papers in the Science Citation Index Expanded},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{jcr56202513,

title = {Advancing Smart Energy: A Review for Algorithms Enhancing Power Grid Reliability and Efficiency Through Advanced Quality of Energy Services},

author = {José M. Liceaga-Ortiz-De-La-Peña and Jorge A. Ruiz-Vanoye and Juan M. Xicoténcatl-Pérez and Ocotlán Díaz-Parra and Alejandro Fuentes-Penna and Ricardo A. Barrera-Cámara and Daniel Robles-Camarillo and Marco A. Márquez-Vera and Francisco R. Trejo-Macotela and Luis A. Ortiz-Suárez},

doi = {https://doi.org/10.3390/en18123094},

issn = {1996-1073},

year  = {2025},

date = {2025-06-05},

urldate = {2025-06-05},

journal = {Energies},

volume = {18},

issue = {12},

pages = {3094},

abstract = {The transformation of traditional energy systems into smart energy systems has ushered in an era of efficiency, sustainability and technological growth. In this paper, we propose a new definition for “Quality of Energy Service” that focuses on ensuring optimal power-supply quality, encompassing factors such as availability, speed (i.e., the time to restore or adjust supply following interruptions or load changes) and reliability of sup-ply. We explore the integration of advanced algorithms specifically tailored to enhance the Quality of Energy Services. By concentrating on key aspects—reliability, availability and operational efficiency—the study reviews how various algorithmic approaches, from machine learning models to classical optimisation techniques, can significantly improve power grid management. These algorithms are evaluated for their potential to optimise load distribution, predict system failures and manage real-time adjustments in power supply, thereby ensuring higher service quality and grid stability. The findings aim to provide actionable insights for policymakers, engineers and industry stakeholders seeking to advance smart grid technologies and meet global energy standards. Fur-thermore, we present a case study to demonstrate how these models can be integrated to optimise grid management, forecast energy demand and enhance operational efficiency. We employ multiple machine learning models—including Random Forest, XGBoost version 1.6.1 and Long Short-Term Memory (LSTM) networks—to predict future energy demand. These models are then combined within an ensemble learning framework to improve both the accuracy and robustness of the forecasts. Our ensemble framework not only predicts energy consumption but also optimises battery storage utilisation, ensuring continuous energy availability and reducing reliance on external energy sources. The proposed stacking ensemble achieved a forecasting accuracy of 99.06%, with a Mean Absolute Percentage Error (MAPE) of 0.9364% and a Coefficient of Determination (R2) of 0.998345, highlighting its superior performance compared to each individual base model.},

keywords = {Papers in the Science Citation Index Expanded},

pubstate = {published},

tppubtype = {article}

}

@article{jcr8620256,

title = {Artificial Intelligence and Human Well-Being: A Review of Applications and Effects on Life Satisfaction through Synthetic Happiness},

author = {Jorge A. Ruiz-Vanoye and Alejandro Fuentes-Penna and Ricardo A. Barrera-Cámara

and Ocotlán Díaz-Parra and Francisco R. Trejo-Macotela and Luis José Gómez-Pérez

and Jaime Aguilar-Ortiz and Miguel Á. Ruiz-Jaimes and Yadira Toledo-Navarro and 

Carlos R. Domínguez Mayorga},

doi = {https://doi.org/10.61467/2007.1558.2025.v16i1.932},

issn = {2007-1558},

year  = {2025},

date = {2025-06-04},

urldate = {2025-06-04},

journal = {International Journal of Combinatorial Optimization Problems and Informatics},

volume = {16},

issue = {1},

pages = {14–37},

abstract = {This paper examines the role of Artificial Intelligence in enhancing human well-being across domains such as healthcare, mental health, and education. A key contribution is the introduction of synthetic happiness—a form of well-being facilitated or enhanced by AI rather than naturally occurring processes. By reviewing current advancements, the study highlights AI’s positive impact on life satisfaction while addressing ethical concerns and potential drawbacks. The paper explores AI’s role in personalised healthcare, mental health support, and adaptive education, demonstrating how it fosters environments conducive to happiness. It further analyses synthetic happiness as a novel perspective on AI-driven well-being, discussing its benefits and risks, including reduced human interaction and over-reliance on artificial systems. While AI presents transformative opportunities to enhance happiness, it must complement rather than replace genuine human experiences. This paper provides a foundation for understanding AI’s role in well-being and offers insights for future research and applications.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{jcr8620252,

title = {Comparison between FRQI and NEQR quantum algorithms applied in digital image processing},

author = {Joel Silos-Sanchez and Jazmin Rodríguez-Flores and Josué Martínez-Mireles and Marco García-Márquez and Arturo Austria-Cornejo and Jorge A. Ruiz-Vanoye},

doi = {https://doi.org/10.61467/2007.1558.2025.v16i1.898},

issn = {2007-1558},

year  = {2025},

date = {2025-06-03},

journal = {International Journal of Combinatorial Optimization Problems and Informatics},

volume = {16},

issue = {1},

pages = {213–225},

abstract = {Quantum image processing represents a transformative approach to visual data analysis, leveraging the principles of quantum computing to overcome classical limitations. This work explores two prominent quantum image encoding methods: FRQI (Flexible Representation of Quantum Images) and NEQR (Novel Enhanced Quantum Representation). FRQI excels in qubit efficiency, making it suitable for hardware implementation, while NEQR offers superior precision in pixel intensity representation, ideal for complex image processing tasks. We detail the implementation of these algorithms, including preprocessing, quantum circuit design, and simulation, using platforms like Qiskit. The study highlights the potential of quantum image processing in fields such as medicine, industry, and environmental monitoring, while addressing challenges like qubit limitations and noise sensitivity. This research contributes to advancing quantum computing applications, paving the way for innovative and sustainable technological solutions.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{jcr8620251,

title = {Design and implementation of a wireless network with mechanisms that do not violate security to meet the demand of higher education institutions},

author = {Miguel Ángel Ruiz-Jaimes and Jorge A. Ruiz-Vanoye and Juan José Flores-Sedano and Yadira Toledo-Navarro},

doi = {https://doi.org/10.61467/2007.1558.2025.v16i1.419},

issn = {2007-1558},

year  = {2025},

date = {2025-06-02},

urldate = {2025-06-02},

journal = {International Journal of Combinatorial Optimization Problems and Informatics},

volume = {16},

issue = {2},

pages = {123–129},

abstract = {At the Autonomous University the current wireless network infrastructure is insufficient to meet the growing demand for access, causing failures and intermittencies in the service. Faced with this problem, a thesis proposal has been developed to implement a modern wireless network with high user density, centralized management and improved security schemes. The proposed methodology for the implementation of a high-density wireless network in a higher education institution. In conclusion, the project made it possible to comply with the hypothesis proposed since the implementation of a robust wireless network, with adequate levels of security and profile management, facilitated the ubiquitous access of online academic resources by the student and teaching community of the university. This translates into support for educational quality.},

keywords = {CONAHCYT, ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{jcr8620253,

title = {Weeding Robots Current Jobs and Perspectives},

author = {Rubén Oswaldo Hernández-Terrazas and Juan Xicoténcatl-Pérez and Jorge A. Ruiz-Vanoye},

doi = {https://doi.org/10.61467/2007.1558.2025.v16i1.951},

issn = {2007-1558},

year  = {2025},

date = {2025-06-02},

journal = {International Journal of Combinatorial Optimization Problems and Informatics},

volume = {16},

issue = {1},

pages = {202–207},

abstract = {Precision agriculture has revolutionised the way farmers manage their crops, maximising efficiency and minimising environmental impact. This thesis addresses the design and implementation of a device that identifies the corn plant and with the help of artificial intelligence implement a robotic arm that delivers herbicides and in some cases precision lasers.



 



We will explore the design and control of a precision farming device, from basic principles to the most advanced technologies currently in use. We will analyse the sensorisation tools, control systems and optimisation techniques employed to ensure a more sustainable and profitable agriculture through: precision identification of maize plants and the use of a robotic mechanical arm equipped with two nozzles, one to deliver herbicide and the other to deliver lasers to the weeds surrounding the maize plants. The proposal aims for the expected results to reduce herbicide consumption by 30-50%, validating the feasibility of this scalable and sustainable solution to improve efficiency in agricultural infrastructures.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{jcr8620254,

title = {Smart Energy Efficiency: Energy Management System in Smart Buildings based on Voting Ensemble and battery bank},

author = {José M. Liceaga-Ortiz-de-la-Peña and Juan M. Xicoténcatl-Pérez and Jorge A. Ruiz-Vanoye},

doi = {https://doi.org/10.61467/2007.1558.2025.v16i1.934},

issn = {2007-1558},

year  = {2025},

date = {2025-06-02},

urldate = {2025-06-02},

journal = {International Journal of Combinatorial Optimization Problems and Informatics},

volume = {16},

issue = {1},

pages = {208–212},

abstract = {Energy management in smart buildings requires strategies to improve power consumption, ensuring reliability and cost reduction. This thesis addresses the solution of the problem by implementing a predictive model based on an artificial intelligence technique working in conjunction with a proposed battery bank in order to better store energy and mitigate the high peaks of electricity demand in different periods as well as in atypical scenarios.



 



The research addresses two aspects: accuracy in consumption prediction, and energy management, the latter including energy storage efficiency and mitigating high peak power consumption. The proposal aims for the expected results to reduce electricity consumption by 3% to 7%, validating the viability of this scalable and sustainable solution to improve energy efficiency in smart infrastructures.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{jcr8620255,

title = {Optimizing Energy Consumption in Smart Buildings by Using Machine Learning Algorithms},

author = {Luis Arturo Ortiz-Suarez and Jorge A. Ruiz Vanoye and Francisco R. Trejo-Macotela},

doi = {https://doi.org/10.61467/2007.1558.2025.v16i1.1057},

issn = {2007-1558},

year  = {2025},

date = {2025-06-02},

urldate = {2025-06-02},

journal = {International Journal of Combinatorial Optimization Problems and Informatics},

volume = {16},

issue = {1},

pages = {258–265},

abstract = {In this article, an approach for optimizing energy consumption in smart buildings using machine learning algorithms is presented. Utilizing TDSP, data on climatic conditions, occupancy, and energy consumption obtained from EnergyPlus software are integrated. Feature selection and feature importance techniques, as well as statistical analyses, are implemented to select variables that are used to train machine learning models such as MLP neural networks, support vector machines, random forest, and XGBRegressor for predicting energy consumption, with accuracy evaluated using RMSE. It was demonstrated that models based on neural networks offer better accuracy, thereby enabling measures to achieve energy optimization.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}