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    Machine Learning Algorithm Selection for Predictive Maintenance in the Oil Industry
    (2024)
    Alexander Briceño-Ruiz
    ;
    López Vargas, Wilson  
    ;
    Jahel Riofrío-Vera
    ;
    Steven Paredes-Medina
    ;
    Lourdes Mejía-Ibarra
    One of the applications of artificial intelligence (AI) within the industry is machine learning (ML), a powerful tool to perform predictive maintenance on machinery, as it allows for predicting future failure scenarios through data analysis and avoiding accidents. Electric submersible pumps (ESPs) are critical in the oil industry, as they are used in the artificial lift technique for oil extraction, standing out for their extensive production volume at high depths. However, ESPs are prone to frequent failures. For this reason, this study aimed to compare the performance of three popular nondeterministic ML algorithms in Rstudio, Random Forest (RF), Extreme Gradient Boosting (XGB), and Adaptive Boosting (AB), to determine the best predictive maintenance strategies for an ESP. This paper uses an artificial and public domain dataset containing cycle reading information, machine operating variables such as voltage and pressure expressed in average and standard deviation, maintenance cycle information, and component errors. The results showed that when predicting machine failures with the test dataset, the RF algorithm presented a lower variance (2.31273e-8) and higher average accuracy (99.878%); XGB (variance 4.61843e-8 and average accuracy of 99.850%), and AB (variance 7.688043e-8 and average accuracy of 99.844%). An independent sample Welch’s t-test was performed, and a significant difference was found between the accuracy of the RF and that of the other algorithms. It is concluded that RF proved more suitable for predictive maintenance of an ESP with the dataset used.
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    Machine Learning Techniques for Academic Prediction: Comparative Analysis of Random Forest, XGBoost and Classical Techniques CaseT
    (2025)
    Edwin Alexander Aguilar Sanchez
    ;
    Marcos Chacón-Castro
    ;
    Jadán Guerrero, Janio  
    The use of machine learning techniques has transformed various sectors, including education, by allowing for more accurate prediction of students’ academic preferences. The main objective of this study is to compare the effectiveness of classical algorithms, such as Logistic Regression, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), with advanced algorithms, such as Random Forest and XGBoost, in predicting academic program choices in students at a technical university in Ecuador. The methodology used included the use of a dataset of 1,170 student responses, which were processed and analyzed using these algorithms. The techniques were evaluated using metrics such as precision, recall, and F1-score. The results obtained indicate that the advanced algorithms, particularly XGBoost, significantly outperform the classical ones in terms of precision, reaching 89.3%. Although the classical algorithms demonstrated faster execution times, their lower precision makes them less suitable for complex prediction tasks. In conclusion, advanced algorithms are presented as more effective tools for academic planning and optimization of educational resources, providing the possibility of personalizing learning and improving decision-making in educational institutions.
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