Publications

@article{jcr202606231,

title = {Recruitment Is Not Neutral: Consent-Bound AI and the Ethics of Pre-Consent Inference in Clinical Trials},

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

doi = {https://doi.org/10.1080/15265161.2026.2690924},

issn = {1536-0075},

year  = {2026},

date = {2026-06-23},

urldate = {2026-06-23},

journal = {The American Journal of Bioethics },

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

pubstate = {forthcoming},

tppubtype = {article}

}

@article{jcr2026062311,

title = {Wavelet-Based Evolving Fuzzy System for Online Fault Diagnosis: Application to the Tennessee Eastman Process},

author = {Marco Antonio Márquez-Vera and Jorge A. Ruiz-Vanoye and Carlos Antonio Márquez-Vera and Alfian Ma’arif and Edith Mendoza-Ramírez},

doi = {https://doi.org/10.3390/a19060485},

issn = {1999-4893},

year  = {2026},

date = {2026-06-17},

journal = {Algorithms},

volume = {19},

issue = {6},

pages = {485},

abstract = {Evolving fuzzy systems (EFS) offer an incremental learning, making them promising for fault diagnosis (FD) in industrial processes, where unknown faults and changing operation conditions are common. The evolving fuzzy structure enables incremental rule adaptation while maintaining interpretability and reduced computational complexity compared with deep learning approaches. However, the performance of EFS depends heavily on the preprocessing of input data. This study evaluates eight preprocessing strategies for EFS applied to the Tennessee Eastman benchmark process. A one-vs-rest EFS architecture was implemented for ten representative faults (IDV1, IDV2, IDV4, IDV5, IDV6, IDV7, IDV8, IDV10, IDV13 and IDV14) in order to make a comparison with other FD techniques. This approach uses seven variables selected by using the least angle regression. Preprocessing methods were applied to highlight fault signatures. Using the Daubechies-4 in the preprocessing achieved the best overall F1-score (73.68%) with a sensitivity of 97.37%, outperforming the no-preprocessing baseline (F1 = 70.67%). Per-fault analysis showed high performance for faults IDV6, IDV7, and IDV14, while IDV1, IDV2, IDV5, and IDV8 exhibited high sensitivity but lower specificity. These findings indicate that wavelet preprocessing significantly enhances EFS for FD, and that the choice of wavelet should be guided by application priorities: Daubechies-4 is recommended for maximum detection and fewer false alarms. The obtained results demonstrate that wavelet preprocessing substantially improves classification robustness and fault discrimination compared with the non-preprocessed baseline.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{jcr2026062312,

title = {Topological and Self-Structured Approaches to Supervised Anomaly Detection in Econometrics},

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

doi = {https://doi.org/10.61467/2007.1558.2026.v17i3.857},

issn = {2007-1558},

year  = {2026},

date = {2026-06-15},

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

volume = {17},

issue = {3},

pages = {1–36},

abstract = {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.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{jcr2026062313,

title = {Emotional AI in the Workplace: Systematic Review of Effects on Employee Well-Being, Productivity, and Organizational Performance},

author = {Jorge A. Ruiz-Vanoye and Jaime Aguilar-Ortiz and Ocotlán Diaz-Parra and Ricardo A. Barrera-Cámara and Alejandro Fuentes-Penna and Miguel Á. Ruiz-Jaimes and Yadira Toledo-Navarro and Marco A. Vera-Jiménez and Alicia Ortiz-Montes and Marco Tulio Valdez-Acosta},

doi = {https://doi.org/10.61467/2007.1558.2026.v17i3.1398},

issn = {2007-1558},

year  = {2026},

date = {2026-06-15},

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

volume = {17},

issue = {3},

pages = {146–165},

abstract = {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.},

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  = {2026},

date = {2026-06-02},

urldate = {2026-06-02},

journal = {Behavioral and Brain Sciences},

volume = {49},

pages = {e78},

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 = {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 = {https://doi.org/10.1017/S0140525X25102409},

issn = {0140-525X},

year  = {2026},

date = {2026-06-01},

urldate = {2025-06-05},

journal = {Behavioral and Brain Sciences},

volume = {49},

pages = {e123},

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 = {published},

tppubtype = {article}

}

@article{jcr2026062314,

title = {AI and synthetic happiness in esports athletes and recreational gamers: Between code, competition, and well-being},

author = {Jorge A. Ruiz-Vanoye and Francisco R. Trejo-Macotela and Ocotlán Diaz-Parra and Jaime Aguilar-Ortiz and Miguel A. Ruiz-Jaimes and Yadira Toledo-Navarro and Alejandro Fuentes-Penna and Ricardo A. Barrera-Cámara and Marco A. Vera-Jiménez},

doi = {https://doi.org/10.1016/j.chbah.2026.100309},

year  = {2026},

date = {2026-04-24},

journal = {Computers in Human Behavior: Artificial Humans},

volume = {8},

pages = {100309},

abstract = {rtificial Intelligence (AI) is increasingly embedded in esports, shaping training, performance, and player experience. While much attention has been given to technical applications, less is known about how AI-mediated systems affect psychological well-being. This Perspective Article introduces the concept of synthetic happiness, defined as a technology-mediated form of subjective well-being that emerges through adaptive feedback, motivational regulation, and cognitive reappraisal in digital environments. We propose two conceptual models to situate synthetic happiness within established psychological theories: the Synthetic Happiness Pyramid and the AI-Happiness Loop. The Pyramid extends Maslow's hierarchy by incorporating AI-driven adaptation as a determinant of resilience, motivation, and flourishing in esports contexts. The Loop illustrates the dynamic cycle through which biometric and behavioural monitoring inform adaptive interventions, sustaining flow, supporting Self-Determination Theory's needs of autonomy, competence, and relatedness, and mitigating burnout risk. Beyond theory, we highlight ethical concerns related to biometric data privacy, cognitive autonomy, and youth protection, emphasizing the need for transparent and responsible design. By integrating sport psychology, motivation science, and AI ethics, this article outlines a research agenda for empirically testing synthetic happiness models and developing frameworks that ensure AI promotes—not undermines—long-term well-being in competitive digital sport.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jr542026,

title = {Vision-Based Robotic System for Selective Weed Detection and Control in Precision Agriculture},

author = {Rubén O. Hernández-Terrazas and Juan M. Xicoténcatl-Pérez and Julio C. Ramos-Fernández and Marco A. Márquez-Vera and José G. Benítez-Morales and Eucario G. Pérez-Pérez and Jorge A. Ruiz-Vanoye and Ocotlán Diaz-Parra and Francisco R. Trejo-Macotela and Alejandro Fuentes-Penna},

doi = {https://doi.org/10.3390/agriculture16070810},

issn = {2077-0472},

year  = {2026},

date = {2026-04-05},

urldate = {2026-04-05},

journal = {Agriculture},

volume = {19},

issue = {7},

pages = {810},

abstract = {Precision agriculture is a key technology for addressing challenges such as increasing food demand, labour shortages, and the environmental impact of intensive agrochemical use. In this context, selective weed management remains a critical issue due to its direct effect on crop productivity and sustainability. This article presents a simulation-based framework for the design and evaluation of an agricultural robotic module for the detection, classification, and selective intervention of weeds. The proposed system integrates convolutional neural networks and the kinematic model of a 2DOF robot manipulator with 5 links for weed classification and treatment. The system is evaluated in a virtual environment, where camera calibration, perception accuracy, and the performance of the kinematic model are analysed. Quantitative results include detection accuracy, localization error, and intervention success rate under simulated field conditions. The results demonstrate selective weed management and the feasibility of simulation for developing weed control systems, while also identifying the main challenges for real-world deployment.},

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

pubstate = {published},

tppubtype = {article}

}

@article{jcr31120253,

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 = {2026-03-02},

urldate = {2026-01-06},

journal = {Polish Journal of Environmental Studies},

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

pubstate = {published},

tppubtype = {article}

}

@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{Márquez-Vera_Calderón-Suárez_Gutiérrez-Moreno_Díaz-Parra_Márquez-Vera_2026,

title = {Electronics and Programming for Bioprocess Control in Biotechnology Engineering: Accessible Solutions for Industry 4.0},

author = {Marco Antonio Márquez-Vera and Ricardo Calderón-Suárez and Evelin Gutiérrez-Moreno and Ocotlán Díaz-Parra and Carlos Antonio Márquez-Vera},

url = {https://ijcopi.org/ojs/article/view/1200},

doi = {10.61467/2007.1558.2026.v17i1.1200},

year  = {2026},

date = {2026-01-01},

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

volume = {17},

number = {1},

pages = {20–33},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Márquez-Vera_Ma’arif_Díaz-Parra_Yakoub_Ramos-Fernández_2026,

title = {Towards Interpretable Inverse Model Control: The Role of Grey-Box Models},

author = {Marco Antonio Márquez-Vera and Alfian-Ma’arif and Ocotlán Díaz-Parra and Zaineb Yakoub and Julio César Ramos-Fernández},

url = {https://ijcopi.org/ojs/article/view/1244},

doi = {10.61467/2007.1558.2026.v17i1.1244},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

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

volume = {17},

number = {1},

pages = {117–131},

keywords = {ESCI},

pubstate = {published},

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{jcr31120251,

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 = {2025-10-25},

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{jcr31120252,

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 = {2025-10-25},

journal = {Diagnostics},

volume = {15},

issue = {21},

pages = {2691},

abstract = {Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality worldwide among both men and women. Early and accurate detection is essential to improve patient outcomes. This study explores the use of artificial intelligence (AI)-based software for the diagnosis of lung cancer through the analysis of medical images in DICOM format, aiming to enhance image visualization, preprocessing, and diagnostic precision in chest computed tomography (CT) scans. Methods: The proposed system processes DICOM medical images converted to standard formats (JPG or PNG) for preprocessing and analysis. An ensemble of classical machine learning algorithms—including Random Forest, Gradient Boosting, Support Vector Machine, and K-Nearest Neighbors—was implemented to classify pulmonary images and predict the likelihood of malignancy. Image normalization, denoising, segmentation, and feature extraction were performed to improve model reliability and reproducibility. Results: The AI-enhanced system demonstrated substantial improvements in diagnostic accuracy and robustness compared with individual classifiers. The ensemble model achieved a classification accuracy exceeding 90%, highlighting its effectiveness in identifying malignant and non-malignant lung nodules. Conclusions: The findings indicate that AI-assisted CT image processing can significantly contribute to the early detection of lung cancer. The proposed methodology enhances diagnostic confidence, supports clinical decision-making, and represents a viable step toward integrating AI into radiological workflows for early cancer screening.},

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

pubstate = {published},

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},

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{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 Beatríz 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},

urldate = {2025-07-01},

journal = {Computación y Sistemas},

volume = {29},

issue = {2},

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},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{22720251,

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},

urldate = {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{22720252,

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 = {25-35},

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{JCR25625,

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 = {2025-06-25},

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 analysing 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, this paper discusses future perspectives on biomimetics-driven innovations, emphasising 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, this 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{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},

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{jcr8620251,

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},

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}

}