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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Yulieth Carriazo-Regino
Universidad de Córdoba
Computer Science & IT Electrical & Electronics Engineering Engineering

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Interpretable artificial intelligence system for personalized cognitive stimulation Rubén Baena-Navarro; Yulieth Carriazo-Regino; Mario Macea-Anaya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp164-176

Abstract

The growing need to preserve cognitive health in aging populations has intensified interest in adaptive digital interventions that provide personalized and interpretable support. This study presents a web-based cognitive stimulation system for older adults integrating a multilayer perceptron (MLP) classifier, expert-derived symbolic rules, and explainable artificial intelligence (XAI) techniques, including Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME). The platform was evaluated through a 24-week intervention involving 150 participants aged 65 years and older, combining baseline cognitive profiling, rule-guided recommendation logic, and neural prediction to support individualized task allocation. Compared with a control group, participants in the intervention arm showed statistically significant improvements in cognitive outcomes (p <0.05), with measurable gains in memory- and attention-related tasks. The explainability component enabled examination of model behavior at the level of individual features through feature attribution analysis and symbolic consistency checks, supporting interpretation beyond aggregate performance metrics. Unlike approaches dependent on high-end extended reality (XR) infrastructures or game centered interaction, the system was implemented to operate under low connectivity conditions and was tested with participants from diverse educational backgrounds. This hybrid configuration provides an interpretable basis for cognitive support initiatives adaptable to community settings contexts.
Gamma and ultraviolet radiation radiation analysis: an internet of things-based dosimetric study Rubén Baena-Navarro; Luis Alcala-Varilla; Francisco Torres-Hoyos; Yulieth Carriazo-Regino; Tobías Parodi-Camaño
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7344

Abstract

This study presents the implementation of an internet of things (IoT)-based device for the accurate and continuous measurement of gamma and ultraviolet (UV) radiation in a rural area of Sincelejo, Colombia. The device, calibrated with an error margin below 5%, allowed for the reliable collection of data during the year 2022. An average effective dose rate of gamma radiation of (0.998±0.037) mSv/year was recorded, a value that approaches the recommended limit. Additionally, the inverse square law of radiation was confirmed, observing a decrease in radiation with an increase in altitude. Concurrently, a constant risk of high to extremely high UV radiation exposure was detected throughout the year. These findings emphasize the need for constant monitoring and the implementation of UV protection measures in the region. The integration of IoT in environmental dosimetry has proven to be an invaluable tool for detailed tracking of radiation levels, significantly contributing to the understanding of radiation in rural areas. The exploration of more advanced sensors and data analysis tools in future research is recommended to further improve the accuracy and utility of these devices.
Adaptive AI-driven framework for digital mental health interventions in low-resource universities Rubén Baena-Navarro; Yulieth Carriazo-Regino; Richard Crawford-Vidal; Alexander Fernández-Arango; Francisco Barreiro-Pinto
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10143

Abstract

Mental health problems affect nearly half of university students worldwide, with around 20% reporting depressive symptoms and over 40% showing signs of anxiety. This burden is particularly acute in low-resource universities, where limited infrastructure and minimal investment in mental health restrict access to effective care. To address this gap, this study applies a projective research approach, defined as the design of evidence-based solutions without immediate empirical implementation. A systematic review of 402 scientific articles was carried out across major databases, from which 15 met strict inclusion criteria. The analysis identified recurrent barriers such as unstable internet connectivity, devices with less than 2 GB RAM, and the absence of regulatory frameworks governing AI in education. Based on these findings, an adaptive intervention model was proposed, integrating artificial intelligence (AI), machine learning (ML), and deep learning (DL) to deliver personalized psychological support directly on local devices, without requiring permanent connectivity. The proposed system demonstrated potential to reduce anxiety and depression scores by 15–25% in controlled studies and achieved prediction accuracies above 80% for stress and loneliness detection. This framework provides a scalable foundation for universities in developing contexts, contributing to equity in access to digital mental health services.
Challenges in implementing free software in small and medium-sized enterprises in the city of Montería: a case study Rubén Baena-Navarro; Juan Vergara-Villadiego; Yulieth Carriazo-Regino; Richard Crawford-Vidal; Francisco Barreiro-Pinto
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6710

Abstract

This study investigates challenges and opportunities in adopting free and open-source software (FOSS) in small and medium-sized enterprises (SMEs) in Monteria, Colombia. The research reveals that around 77.5% of SMEs prefer free software, yet surprisingly, 80% are unaware of the benefits of open-source licenses, with nearly 45% not adopting them due to lack of knowledge. Implementing FOSS in SMEs offers legal and economic advantages, including reduced software acquisition costs, compliance with data protection and privacy regulations, and fostering innovation. However, adoption barriers persist, necessitating further research for enhancing implementation in Colombian SMEs. Notably, Colombia's ethical framework for AI serves as a guide for ethical AI and open-source software deployment, aligned with sustainable development goals. This study highlights free software usage prevalence in Monteria's SMEs and critical factors hindering full adoption. Addressing challenges and leveraging potential benefits can improve efficiency, regulatory compliance, and contribute to sustainable development. Continued research in this field can promote broader and stronger implementation of FOSS in Colombian SMEs.
Co-Authors Alexander Fernández-Arango Francisco Barreiro-Pinto Francisco Torres-Hoyos Juan Vergara-Villadiego Luis Alcala-Varilla Mario Macea-Anaya Richard Crawford-Vidal Rubén Baena-Navarro Tobías Parodi-Camaño