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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Yactayo-Arias, Cesar
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Computer Science & IT Electrical & Electronics Engineering Engineering

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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.
Advances in the diagnosis of ocular diseases: an innovative approach through an expert system Andrade-Arenas, Laberiano; Yactayo-Arias, Cesar
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7971

Abstract

In the context of ophthalmic care, where early diagnosis of eye disorders plays a crucial role in patients' quality of life, this study focused on the development and evaluation of an expert system based on SWI Prolog. The main objective of this research was to provide an effective method for the preliminary diagnosis of ocular disorders, including cataract, trachoma, uveitis, glaucoma, and presbyopia. For the evaluation of the system, a confusion matrix was implemented and accuracy, sensitivity and specificity were calculated using a sample of 30 cases, of which 20 were positive and 10 negatives. The findings revealed an outstanding accuracy of 95%, with a sensitivity and specificity of 90%. This highlights the potential of the tool as an effective means of early detection of visual problems. In conclusion, this expert system represents a significant advance in ophthalmologic diagnosis, with important implications for clinical care and patients' quality of life, although expansion and validation of the tool in further clinical studies is suggested for its wider and more successful implementation in the field of ophthalmology.
Mobile application: awareness of the population on the environmental impact Andrade-Arenas, Laberiano; Giraldo-Retuerto, Margarita; Molina-Velarde, Pedro; Yactayo-Arias, Cesar
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6131

Abstract

Nowadays, pollution keeps increasing due to social, political, economic, cultural, and environmental factors. Environmental awareness is close to zero because people prioritize personal activities. In that sense, the objective of this investigation is to raise environmental awareness in the population regarding the impact of pollution and support this through a mobile application (APP) that helps reduce pollution. The methodology used was the cascade, and through its phases, it was developed the prototype design of the mobile APP. The results obtained from this hybrid research were through a survey using ATLAS.ti 22; it concluded that environmental awareness begins at home and is taught by the parents, also it should be promoted from elementary school to high school and even in college. Moreover, in a survey, the users stated by 89% that the use of this mobile APP can help reduce the environmental impact. Also, in the validation through expert judgment, all the attributes were accepted with an average of 81%, that of functionality was the lowest, and the highest was that of consistency and integration with 83%. Finally, environmental education should be a priority policy in any country, as this will benefit its population.
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.
Expert systems in mental health: innovative approach for personalized treatment Andrade-Arenas, Laberiano; Rubio-Paucar, Inoc; Celis, Domingo Hernández; Yactayo-Arias, Cesar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp414-427

Abstract

Custom classification of mental illnesses has emerged as a challenge for mental health specialists, often minimized by patients' lack of awareness of symptoms and the importance of early intervention. Therefore, the purpose of this research is to provide a comprehensive understanding of personalized treatment, encompassing both pharmacological and non-pharmacological options, specifically tailored to mental disorders, considering factors such as the patient's age and gender, among other relevant characteristics. In this context, the Buchanan methodology has been chosen as the framework for structuring a web-based expert system. This approach covers everything from problem identification to system implementation and subsequent evaluation. The survey results, with a total of 50 responses, reveal that the category "Good" leads with 70%, closely followed by "Fair" and "Poor," both at 14%. 71.4% of responses reflect a positive evaluation, with 85.7% combining "Good" or "Fair" responses, and all categories reaching 100%. These results support the feasibility and effectiveness of implementing a web-based expert system under the Buchanan methodology. A positive response in the survey suggests that this methodology can significantly contribute to personalizing and recommending appropriate treatments, both pharmacological and non-pharmacological, thereby benefiting a broad spectrum of patients with mental disorders.
The crucial role of artificial intelligence in addressing climate change Andrade-Arenas, Laberiano; Hernández Celis, Domingo; Yactayo-Arias, Cesar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp1-11

Abstract

Addressing climate change is one of the fundamental priorities at a global level, given its significant impact on both the environment and society. This systematic literature review explores the role of artificial intelligence (AI) in addressing climate change. It identified applications, contributions to predicting extreme events, techniques used, ethical challenges, and associated biases. The rapid systematic literature review (RSL) was conducted using databases such as Scopus, Dimensions, directory of open access journals (DOAJ), and IEEE Xplore. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement was used to ensure the completeness and transparency of the analysis. 40 articles were selected that were published between 2018 and 2023 and addressed AI in climate change. The findings show that AI is being used to predict and mitigate extreme climate events, estimate the greenhouse effect, and predict temperatures. In addition, innovative techniques such as hybrid machine learning models, convolutional neural networks, artificial neural networks, support vector machines, and logistic regression. In conclusion, AI offers a promising approach to addressing climate change, with transformative potential in predicting and mitigating its effects. However, continuous ethical considerations are required to guarantee its conscientious and efficient utilization.
Diagnosis and treatment of Guillain-Barre using the prolog expert system Andrade-Arenas, Laberiano; Molina-Velarde, Pedro; Pucuhuayla-Revatta, Félix; Yactayo-Arias, Cesar

Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp333-342

Abstract

This research is mostly about Guillain-Barre syndrome (GBS), a complicated neurological condition with many subtypes that make diagnosis and treatment hard, even though medical care is always getting better. The main goal of this study is to build and test an expert system that can correctly diagnose these subtypes, with a focus on early detection and personalized treatments. The evaluation of the system was carried out using a dataset composed of 20 cases (12 positive and 8 negative). A confusion matrix was used to evaluate key metrics such as precision, sensitivity, and specificity. The findings demonstrate precision and sensitivity of 83% and specificity of 75%. These findings unambiguously demonstrate the efficacy of the system in correctly identifying positive Guillain-Barre cases while substantially reducing false negatives. In conclusion, this expert system offers a potentially useful tool to improve the accuracy of the diagnosis and treatment of Guillain-Barre patients. However, to take advantage of its full potential in clinical practice, it should be used as diagnostic support and not replace the medical staff, and it should be updated periodically to reflect new findings in medicine.
System dynamics modeling for strategic management of information technologies in universities Andrade-Arenas, Laberiano; Giraldo Retuerto, Margarita; Yactayo-Arias, Cesar
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.v14i6.10618

Abstract

This study seeks to answer the question: how can system dynamics (SD) modeling contribute to the strategic management of information technology (IT) in universities? The objective of the research is to analyze the importance of incorporating IT into university strategic management through the application of SD methodology. To this end, a model was designed that integrates variables related to resource allocation, the quality of the educational process, and the interaction between institutional actors. The methodology made it possible to simulate technological implementation scenarios and examine their effects on operational efficiency and academic performance. The results show that the strategic integration of IT promotes better resource planning, optimizes the interaction between administrative and academic processes, and contributes to raising the quality of teaching. In conclusion, the proposed model demonstrates that SD is an effective tool for anticipating and understanding the internal dynamics of universities, facilitating more efficient strategic management in today's digital context.
Co-Authors Andrade Arenas, Laberiano Andrade-Arenas, Laberiano Arenas, Laberiano Andrade Celis, Domingo Hernández Giraldo Retuerto, Margarita Giraldo-Retuerto, Margarita Hernández Celis, Domingo Jáuregui-Velarde, Raúl Molina-Velarde, Pedro Paucar, Inoc Rubio Pucuhuayla-Revatta, Félix Retuerto, Margarita Giraldo Rubio-Paucar, Inoc