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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15833
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dc.contributor.authorEchegaray Calderon, Omar-
dc.contributor.authorBarrios Aranibar, Dennis-
dc.date.accessioned2019-01-29T22:19:53Z-
dc.date.available2019-01-29T22:19:53Z-
dc.date.issued2016-
dc.identifier.isbn9781467384186es_PE
dc.identifier.urihttp://repositorio.ucsp.edu.pe/handle/UCSP/15833-
dc.description.abstractIn this research, we propose to use a Genetic Algorithm with an Artificial Neural Network as fitness function in order to solve one of the most important problems in predicting academic success in higher education environments. Which is to find what are the factors that affect the students' academic performance. Also, using the same Artificial Neural Network as a predictor. To solve the problem, each individual of the genetic algorithm represents a group of factors, which will be evaluated with the fitness function seeking to obtain the optimal individual (group of factors) to predict academic performance. Then, with the same Artificial Neural Network we will classify students' academic grades in order to predict their semester final grades. With this technique, it was possible to reduce the initial amount of 39 factors (founded in the literature) to only 8. The prediction accuracy is 84.86%. © 2015 IEEE.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.language.isoenges_PE
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84969724235&doi=10.1109%2fLA-CCI.2015.7435976&partnerID=40&md5=5b7b5784e2450cbd46584f6056fdc254es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectAlgorithmses_PE
dc.subjectArtificial intelligencees_PE
dc.subjectClassification (of information)es_PE
dc.subjectForecastinges_PE
dc.subjectGenetic algorithmses_PE
dc.subjectNeural networkses_PE
dc.subjectStudentses_PE
dc.subjectAcademic performancees_PE
dc.subjectFitness functionses_PE
dc.subjectHigher educationes_PE
dc.subjectOptimal selectiones_PE
dc.subjectPrediction accuracyes_PE
dc.subjectEducationes_PE
dc.titleOptimal selection of factors using Genetic Algorithms and Neural Networks for the prediction of students' academices_PE
dc.typeinfo:eu-repo/semantics/conferenceObjectes_PE
dc.identifier.doi10.1109/LA-CCI.2015.7435976es_PE
Appears in Collections:Artículos de investigación

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