https://athenea.autanabooks.com/index.php/revista <p>The <strong>Athenea Journal</strong> is published in collaboration with Venezuela and Ecuador, highlighting the multicultural values of our lands and showcasing Latin America’s scientific contributions to the world, where science becomes a universal language without borders. It is a space where the brightest minds from our nations come together to contribute to global knowledge, showing that in science there are neither limits nor barriers—only the shared desire to advance together. Athenea is not just a journal but a bridge connecting hearts and talents, proving that when we work together, borders fade away, and the future fills with infinite possibilities.</p> <p><strong>Athenea </strong>is a scientific journal oriented to Engineering Sciences. It is published by AutanaBooks SAS, with the institutional support of the Universidad Experimental "Antonio José de Sucre" (UNEXPO), vice-rectorate Puerto Ordaz, Venezuela. Its main administrative office is located in Venezuela, and its editor is PhD Franyelit Suárez.<br>The journal Athenea focuses on Engineering Sciences and aims to publish academic and scientific material of high research level and quality, produced by scientists and researchers in Latin America and the world to disseminate the work of teaching and research.</p> Autanabooks en-US Athenea Engineering sciences journal 2737-6419 https://athenea.autanabooks.com/index.php/revista/article/view/138 <p>This study develops an intelligent system based on machine learning for the early detection of academic stress in university students, focusing on the areas of Engineering and Social Sciences. Based on a quantitative and simulation-based approach, a structured dataset was built integrating academic, behavioral, and psychosocial variables. Predictive models were implemented, including Random Forest, Support Vector Machines, and XGBoost, and their performance was evaluated using multiclass and binary classification metrics. The results show that ensemble models achieved the highest levels of accuracy, exceeding 90% in the detection of stress levels. Likewise, key variables such as fatigue, concentration, pending assignments, and hours of sleep were identified. Scenario simulation demonstrated that combined interventions generate significant reductions in the probability of high stress. The study provides a predictive-explanatory approach that contributes to decision-making in educational environments oriented toward student well-being.</p> Jose Calizaya Lopez Ariosto Carita Choquecahua Luis Felipe Ticona Lecaros Gloria Isabel Monzon Alvarez Rossana Teresa Quicano Alvarez Copyright (c) 2026 Jose Calizaya Lopez, Ariosto Carita Choquecahua, Luis Felipe Ticona Lecaros, Gloria Isabel Monzon Alvarez, Rossana Teresa Quicano Alvarez https://creativecommons.org/licenses/by/4.0/deed.es 2026-05-12 2026-05-12 7 24 8 18 10.47460/athenea.v7i24.138