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{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 = {2026-01-06},

urldate = {2025-11-03},

journal = {Polish Journal of Environmental Studies},

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

pubstate = {forthcoming},

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{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},

year  = {2025},

date = {2025-10-09},

journal = {Behavioral and Brain Sciences},

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},

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{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},

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}

}

@article{JCR6225,

title = {A Comprehensive Review of Quality of Aquaculture Services in Integrated Multi-Trophic Systems},

author = {Jorge A. Ruiz-Vanoye and Ocotlán Díaz-Parra and Marco Antonio Márquez-Vera and Alejandro Fuentes-Penna and Ricardo A. Barrera-Cámara and Miguel A. Ruiz-Jaimes and Yadira Toledo-Navarro and Beatriz Bernabe-Loranca and Eric Simancas-Acevedo and Francisco R. Trejo-Macotela and Marco A. Vera-Jimenez},

doi = {https://doi.org/10.3390/fishes10020054},

year  = {2025},

date = {2025-02-05},

journal = {Fishes},

volume = {10},

number = {2},

pages = {54},

abstract = {The concept of Quality of Aquaculture Services (QoAS) is inspired by the Quality of Service (QoS) principle, originally developed in the field of networks and telecommunications, where it refers to the ability to guarantee the quality, availability, and priority of service in a communications system. Adapted to the aquaculture context, QoAS is fundamental to maximising the benefits of Integrated Multi-Trophic Aquaculture (IMTA). IMTA has emerged as a sustainable approach to meet the growing global demand for aquatic food products by combining species from different trophic levels in a single system, optimising resource use, improving environmental performance, and diversifying production. However, ensuring QoAS in these complex systems requires the implementation of advanced technologies to monitor, manage, and optimise every aspect of the aquaculture process. This article presents a comprehensive review of technologies applied at IMTA, focusing on IoT-based monitoring systems, resource management algorithms, water recirculation technologies, intelligent automation, biosecurity, and data management platforms. Our review finds that IoT and automation-based solutions significantly enhance real-time monitoring, increasing operational efficiency and environmental sustainability. Key challenges identified include integration complexity, high costs, and technical expertise requirements, but the ongoing development of modular, user-friendly solutions indicates a promising trajectory. This review highlights the transformative role of technological innovation in IMTA, providing a foundation for future research and advancements in QoAS management in aquaculture.

},

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

pubstate = {published},

tppubtype = {article}

}

@article{JA25125,

title = {Symmetry in Genetic Distance Metrics: Quantifying Variability in Neurological Disorders for Personalized Treatment of Alzheimer’s and Dementia},

author = {Jorge A. Ruiz-Vanoye and Ocotlán Díaz-Parra and Marco Antonio Márquez-Vera and Ricardo A. Barrera-Cámara and Alejandro Fuentes-Penna and Eric Simancas-Acevedo and Miguel A. Ruiz-Jaimes and Juan Xicoténcatl-Pérez and Julio C . Ramos-Fernández},

doi = {https://doi.org/10.3390/sym17020172},

isbn = {2073-8994},

year  = {2025},

date = {2025-01-20},

journal = {Symmetry},

abstract = {his paper aims to adapt and apply genetic distance metrics in biomedical signal processing to improve the classification and monitoring of neurological disorders, specifically Alzheimer’s disease and frontotemporal dementia. The primary objectives are: (1) to quantify the variability in EEG signal patterns among the distinct subtypes of neurodegenerative disorders and healthy individuals, and (2) to explore the potential of a novel genetic similarity metric in establishing correlations between brain signal dynamics and clinical progression. Using a dataset of resting-state EEG recordings (eyes closed) from 88 subjects (36 with Alzheimer’s disease, 23 with frontotemporal dementia, and 29 healthy individuals), a comparative analysis of brain activity patterns was conducted. Symmetry plays a critical role in the proposed genetic similarity metric, as it captures the balanced relationships between intra- and inter-group EEG signal patterns. Our findings demonstrate that this approach significantly improves disease subtype identification and highlights the potential of the genetic similarity metric to optimize the predictive models. Furthermore, this methodology supports the development of personalized therapeutic interventions tailored to individual patient profiles, making a novel contribution to the field of neurological signal analysis and advancing the application of EEG in personalized medicine.},

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

pubstate = {published},

tppubtype = {article}

}

@article{jcr8620252,

title = {Brain Data Security and Neurosecurity: Technological advances, Ethical dilemmas, and Philosophical perspectives},

author = {Jorge A. Ruiz-Vanoye and Ocotlán Díaz-Parra and Francisco Marroquín-Gutiérrez

and Juan M. Xicoténcatl-Pérez and Ricardo A. Barrera-Cámara and Alejandro Fuentes-Penna and Eric Simancas-Acevedo and Jazmín Rodríguez-Flores and Josue R. Martínez-Mireles},

doi = {https://doi.org/10.61467/2007.1558.2024.v15i5.555},

issn = {2007-1558},

year  = {2024},

date = {2024-11-11},

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

volume = {15},

issue = {5},

pages = {17–35},

abstract = {The rapid development in neurotechnology has significantly advanced our ability to understand and manipulate brain functions. However, these advancements have raised critical concerns regarding the security and privacy of brain data. This paper aims to explore the multifaceted issues surrounding the protection of brain data, focusing on neurosecurity. We begin by reviewing the current technological landscape, focusing on methods used to secure brain data, including encryption, authentication protocols, and anonymisation techniques. Drawing parallels with established computer security practices, we highlight both the strengths and limitations of these approaches when applied to neural data. Next, we delve into the ethical dilemmas posed by brain data security. Issues such as mental privacy and informed consent are analysed. The implications of unauthorised access to brain data and the misuse of such data in various contexts, including criminal justice, employment, and military applications, are discussed in detail. Furthermore, we examine the philosophical perspectives on brain data security, particularly concerning personal identity, autonomy, and freedom of thought. We explore how the manipulation and protection of brain data intersect with longstanding debates in ethics and philosophy, proposing frameworks for addressing these challenges. By combining a technological review with an ethical and philosophical analysis, this paper aims to provide a comprehensive understanding of neurosecurity and brain data security. We conclude with recommendations for future research and policy development to ensure the ethical and responsible use of brain data, emphasising the need for robust governance frameworks that protect individual rights while fostering technological innovation.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{journal11424,

title = {Real-Time Coordination and Communication Process for the Issuance of Emergency Ambulance Alerts},

author = {Evelyn V. Tafoya-Rivera and Ocotlán Díaz-Parra and Marco A. Márquez-Vera and Jorge A. Ruiz-Vanoye and Julio C . Ramos-Fernández and José G. Benítez-Morales and Eduardo Hernández-Huerta},

doi = {https://doi.org/10.61467/2007.1558.2024.v15i4.535},

isbn = {2007-1558},

year  = {2024},

date = {2024-11-01},

urldate = {2024-11-01},

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

volume = {15},

issue = {4},

pages = {101–117},

abstract = {The lack of attention to emergency alerts in road accidents in a timely manner may be due to the lack of management of resources or the lack of communication between hospital and patrol departments. The purpose of this research is to reduce the management time to provide attention to requests on highways. An alert and resource management process is proposed. This process will allow sending the optimized information in the shortest possible time. The experimentation was worked with a set of test instances and simulated in the laboratory using mechatronic devices to show the communication, coordination and attention in real time. The results obtained were acceptable, it was possible to identify the alert to be included in the algorithm which must comply with the restrictions and subsequently calculate the optimal route to be followed by the mechatronic device to reach the request node for the attention of the distress alert.},

keywords = {CONAHCYT, ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{journal114242,

title = {Artificial Intelligences in Industrial Robots: A Framework Based on Gardner’s Multiple Intelligences},

author = {Jorge A. Ruiz-Vanoye and Ocotlán Díaz-Parra and Alejandro Fuentes-Penna and Eric Simancas-Acevedo and Ricardo A. Barrera-Cámara},

doi = {https://doi.org/10.61467/2007.1558.2024.v15i4.536},

issn = {2007-1558},

year  = {2024},

date = {2024-11-01},

urldate = {2024-11-01},

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

volume = {15},

issue = {4},

pages = {118–129},

abstract = {Industrial robots, both in manufacturing and non-manufacturing sectors, are evolving rapidly, driven by advancements in artificial intelligence (AI). This paper presents a comprehensive survey of industrial robots, framed through the lens of Howard Gardner’s theory of multiple intelligences. By categorising various AI capabilities in industrial robots—such as visual recognition, decision-making, and collaborative interaction—based on Gardner’s intelligence framework, we provide a novel taxonomy that bridges human cognitive abilities and artificial systems. The survey explores the historical development of industrial robots, the current state of AI implementation, and future trends in robotics. Additionally, we discuss the implications of these advancements for industries and their workforce, as well as the ethical considerations surrounding the growing autonomy of AI systems. This paper aims to serve as a reference point for researchers and professionals seeking to understand the intersection of cognitive science and industrial AI, highlighting the potential and challenges of integrating AI into robotic systems.},

keywords = {ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{paper130,

title = {A brief review of the History of Computational Intelligence, Bio-Inspired Computing and Scientific Computing},

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

doi = {https://doi.org/10.61467/2007.1558.2024.v15i3.524},

issn = {1558-2024},

year  = {2024},

date = {2024-10-01},

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

volume = {15},

issue = {3},

pages = {164–170},

abstract = {The use of computational tools to solve real problems of different industries has been in increased significantly. Software is used to solve problems of the air, sea, ground, pipes, and aerospace transportation planning; problems related to the science of sport, chemistry, medicine, nursing, mechatronics, robotics; business problems related to minimize costs and maximize profits, problems of management of small, medium and large companies; agro-industrial problems, environment, among many others. To solve real problems using scientific computation, Bio-inspired and computational intelligence. This paper aims to present a review of the history of Computational Intelligence Bio-Inspired Computing and scientific computing.},

keywords = {CONAHCYT, ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{PAPER134,

title = {A comparative analysis of the Classical and Machine learning Forecasting Methods for the Mexican Stock Exchange},

author = {Juan Frausto-Solis and Javier Alberto Rangel-González and Erick Estrada-Patiño and Juan Javier González-Barbosa and Erika Alarcón-Ruiz and Guadalupe Castilla-Valdez and Ocotlán Díaz-Parra



},

doi = {https://doi.org/10.61467/2007.1558.2024.v15i3.512},

issn = {1558-2024},

year  = {2024},

date = {2024-10-01},

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

volume = {15},

issue = {3},

pages = {43–58},

abstract = {There is no recent comparison in the literature on the application of classical and machine learning methods for forecasting financial assets on the Mexican Stock Exchange (BMV). These methods divide the time series into three sections: training, validation, and testing. They predict future values using the training data and are evaluated in the validation phase using error metrics; once the lowest error is obtained, the best parameters and algorithms are used to predict values using the test section. This paper aims to find the most accurate regression algorithm to make predictions in the financial time series of the BMV. The regression methods compared include linear regression, neural networks, decision trees, and support vector regression. The study uses historical BMV asset price data to compare the accuracy of each of these algorithms.},

keywords = {CONAHCYT, ESCI},

pubstate = {published},

tppubtype = {article}

}

@article{jj123b,

title = {Fuzzy Logic Prediction of Hypertensive Disorders in Pregnancy Using the Takagi–Sugeno and C-Means Algorithms},

author = {Israel Campero-Jurado and Daniel Robles-Camarillo and Jorge A. Ruiz-Vanoye and Juan M. Xicoténcatl-Pérez and Ocotlán Díaz-Parra and Julio-César Salgado-Ramírez and Francisco Marroquín-Gutiérrez and Julio Cesar Ramos-Fernández},

doi = {https://doi.org/10.3390/math12152417},

year  = {2024},

date = {2024-08-03},

urldate = {2024-08-03},

journal = {Mathematics},

volume = {12},

issue = {15},

abstract = {Hypertensive disorders in pregnancy, which include preeclampsia, eclampsia, and chronic hypertension, complicate approximately 10% of all pregnancies in the world, constituting one of the most serious causes of mortality and morbidity in gestation. To help predict the occurrence of hypertensive disorders, a study based on algorithms that help model this health problem using mathematical tools is proposed. This study proposes a fuzzy c-means (FCM) model based on the Takagi–Sugeno (T-S) type of fuzzy rule to predict hypertensive disorders in pregnancy. To test different modeling methodologies, cross-validation comparisons were made between random forest, decision tree, support vector machine, and T-S and FCM methods, which achieved 80.00%, 66.25%, 70.00%, and 90.00%, respectively. The evaluation consisted of calculating the true positive rate (TPR) over the true negative rate (TNR), with equal error rate (EER) curves achieving a percentage of 20%. The learning dataset consisted of a total of 371 pregnant women, of which 13.2% were diagnosed with a condition related to gestational hypertension. The dataset for this study was obtained from the Secretaría de Salud del Estado de Hidalgo (SSEH), México. A random sub-sampling technique was used to adjust the class distribution of the data set, and to eliminate the problem of unbalanced classes. The models were trained using a total of 98 samples. The modeling results indicate that the T-S and FCM method has a higher predictive ability than the other three models in this research.},

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

pubstate = {published},

tppubtype = {article}

}