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Statistical and ML Analysis to Determine the Factors That Influence Student Dropout Rates in Information Technology Programs
OtherAbstract: This research, conducted within the framework of the project “Dropout in Higher Education—Early WarnPalabras claves:Decision Trees, logistic regression, Machine Learning, Student dropout, Supervised learningAutores:Aída Noemí Bedón, Diego Salazar-Armijos, Héctor Mauricio Revelo-Herrera, Holger Alfredo Zapata-Mayorga, Nelson Fernando Vinueza-Escobar, Paul Diaz-ZuñigaFuentes:scopusPredicting the impact of adding metakaolin on the flexural strength of concrete using ML classification techniques – a comparative study
ArticleAbstract: The structural design standards, particularly in concrete technology, heavily rely on the mechanicalPalabras claves:cement, concrete, ensemble classification regression, flexural strength, Machine Learning, MetakaolinAutores:Ebid A.M., Fredy Barahona, Hugo Rolando Sanchez Quispe, Luis Velastegui, Nancy Velasco, Onyelowe K.C., Shadi Hanandeh, Trust God A. JohnFuentes:scopusWeb Application Based on Artificial Intelligence for the Control of Healthy Habits in People with Unbalanced Diet in Lima
ArticleAbstract: This study presents the development of a web application designed to promote healthy habits using arPalabras claves:Artificial intelligence, exercise routine assistance, food recommendation, Healthcare, Machine Learning, Web Application, weight controlAutores:Alejandra Oñate-Andino, David Mauricio, Gerardo Josue Huerta-Macedo, Julissa Karol Ponte-Isminio, Pedro Castañeda, Sandra Wong-DurandFuentes:scopusOptimizing Waste Foundry Sand in Concrete Considering Strength Properties for Sustainable Green Structures
ArticleAbstract: Incorporating waste foundry sand (WFS) into concrete is a sustainable approach to enhance green consPalabras claves:Concrete strength, Machine Learning, Sustainable Green Structures, Waste Foundry SandAutores:Ana María Bucheli Campaña, Byron Gabriel Vaca Vallejo, Kerly Mishell Vaca Vallejo, Mery Mendoza Castillo, Nestor UlloaFuentes:scopusPredicting Urban Traffic Congestion with VANET Data
ArticleAbstract: The purpose of this study lies in developing a comparison of neural network-based models for vehiculPalabras claves:congestion prediction, Machine Learning, neural networks, Traffic management, urban mobilityAutores:Ángel Patricio Flores Orozco, Geovanny Silva, Jaime Paul Sayago Heredia, Juan Erazo, Pamela Buñay-Guisñan, Pedro Aguilar-Encarnacion, Wilson ChangoFuentes:scopusInfluence of alkali molarity on compressive strength of high-strength geopolymer concrete using machine learning techniques based on curing regimes and temperature
ArticleAbstract: The compressive strength behavior of high-strength geopolymer concrete (HSGPC) has been studied in tPalabras claves:alkali molarity, Compressive strength, curing temperature and time, geopolymerization, high-strength geopolymer concrete, Machine LearningAutores:Aleis Ivan Adrade Vally, Carlos Santiago Curay Yaulema, Ebid A.M., Maia Gabriela Zuiga Rodguez, Nestor Ulloa, Onyelowe K.C., Onyia M.E.Fuentes:scopusEvaluation of Efficiency in Modeling Mental Illnesses. Case Study: Ecuador
ArticleAbstract: Mental illnesses represent a globally prevalent disease whose mitigation requires the development ofPalabras claves:algorithms, ECUADOR, Efficiency evaluation, Machine Learning, mental illnesses, models, PREDICTION, random forestAutores:Cristian Inca, Evelyn Inca, Franklin Coronel, José Luis Tinajero, Joseph GuerraFuentes:scopusRandom Forest modeling of bipolar affective disorder in Ecuador
ArticleAbstract: Bipolar affective disorder is a mental disorder characterized by depressive and manic or hypomanic ePalabras claves:algorithms, Decision Tree Gradient Boosting, Machine Learning, random forestAutores:Andrea Del Rocío Mejía Rubio, Cristhian Ismael Gómez Gaona, Cristian Inca, Jesús Rodríguez, Jimmy Yaguana Torres, José Rubén León Pérez, Laura Esther Muñoz Escobar, Marco Hjalmar Velasco-Arellano, Zilma Diago AlfesFuentes:scopusModeling the Compressive Strength of Metakaolin-Based Self-Healing Geopolymer Concrete Using Machine Learning Models
ArticleAbstract: Metakaolin-based self-healing geopolymer concrete treated with Bacillus bacteria represents a signifPalabras claves:Bacillus Bacteria, Compressive strength, Green Concrete, Machine Learning, Metakaolin, Self-healing Geopolymer ConcreteAutores:Diego Mayorga, Ember G. Zumba Novay, María Albuja, Nestor UlloaFuentes:scopusPrediction and validation of compressive strength of metakaolin-based mortars using machine learning
ArticleAbstract: Metakaolin (MK)-based cement mortar plays a crucial role in the development of sustainable concretePalabras claves:cement, Compressive strength, Machine Learning, Metakaolin, Mortars, Sustainable structureAutores:Byron Gabriel Vaca Vallejo, Félix García, Kerly Vaca-Vallejo, Miguel Pérez, Naranjo E., Nestor Ulloa, Rómulo RiveraFuentes:scopus