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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Andrade Arenas, Laberiano
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Chatbot with ChatGPT technology for mental wellbeing and emotional management Andrade Arenas, Laberiano; Yactayo-Arias, Cesar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2635-2644

Abstract

There is a growing concern among the world's population about mental health in work, academics, and other contexts where stress, anxiety, and depression are common problems that negatively impact mental health. This study evaluates a chatbot powered by ChatGPT, offering a novel perspective on emotional intervention and mental well-being. It highlights the urgency of this approach in a context where mental health is critical, providing value by combining advanced technologies with emotional management. A multi-faceted approach was implemented to evaluate both usability and technical performance. The usability of the chatbot was evaluated by users using the System Usability Scale (SUS), while the technical performance was evaluated by experts. The active participation of 15 users provided a detailed perspective, resulting in an average usability of 83, reflecting a positive experience in interacting with the system. At the same time, five experts, through technical metrics, assigned an average technical performance of 4.28, indicating solid operational effectiveness. In conclusion, although more research is needed to customize and optimize chatbots over the long term, this approach holds promise for addressing mental health issues in a variety of settings and represents the integration of artificial intelligence to the benefit of those seeking help managing emotional disorders.
Seismic trend analysis: a data mining approach for pattern prediction Andrade Arenas, Laberiano; Yactayo-Arias, Cesar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2623-2634

Abstract

In the global context, seismic movements represent a constant for the population due to geophysical variability and other factors that make them possible, carrying with them the risk of losing innocent lives. The main purpose of our research is to apply data mining techniques to prevent seismic events of any magnitude to anticipate and mitigate future events. In the development of the research, we applied knowledge discovery database methodology. The clustering analysis results revealed the following: cluster 0 encompassed 45 items, with average magnitude of 0.230, representing 15.5% of the total events. Cluster 1 comprised 56 items with average magnitude of 0.156, equivalent to 19.2% of the total. Cluster 2, the largest, consisted of 94 items with average magnitude of 0.156, constituting 32.3% of the total seismic events. Cluster 3 was composed of 54 items, with average magnitude of 0.155, representing 18.3% of the total. Lastly, cluster 4 included 42 items, with average magnitude of 0.155, representing 14.5% of the total. In conclusion, cluster 3 emerged as the most significant, with 94 events and average magnitude of 0.141, equivalent to 32.3% of the total seismic events. This discovery underscores the need to utilize data mining techniques for earthquake prediction, enabling proactive measures against potential events, which are frequent in various geographic areas.
System dynamics modeling for predicting the impact of tutoring on student retention in the school of engineering Andrade Arenas, Laberiano; Giraldo Retuerto, Margarita; Yactayo-Arias, Cesar
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.v13i5.7562

Abstract

Student retention is a persistent problem in many educational institutions, and we seek to address this issue through the implementation of tutoring programs. To achieve this objective, system dynamics (SD) modeling is proposed as a method. This analytical tool allows simulating and predicting the behavior of a complex system over time, considering the interactions between its components. The main objective of this research is to perform SD modeling to improve student retention through tutoring. It seeks to design more effective and personalized tutoring programs, adapted to the specific needs and challenges of the institution's students. The results obtained show that, in the period between 2022 and 2026, research degrees will be encouraged, reaching 50% participation. This increase is considered a positive indicator that encourages universities to become research protagonists. In conclusion, SD modeling makes it possible to forecast and strategically plan the expected results in terms of student retention. This method provides tools to more effectively address the problem of retention, ensuring the academic success of students and promoting the participation of universities in research.
Co-Authors Giraldo Retuerto, Margarita Yactayo-Arias, Cesar