Prediction of Research Project Execution using Data Augmentation and Deep Learning

Vista sencilla de ítem
dc.contributor.authorFlores Garcia, Anibal
dc.contributor.authorTito Chura, Hugo Euler
dc.contributor.authorZea Rospigliosi, Lissethe
dc.date.accessioned2024-03-06T22:15:52Z
dc.date.available2024-03-06T22:15:52Z
dc.date.issued2023-05-05
dc.description.abstractThis paper presents the results of seven deep learning models for prediction of research project execution in graduates from a public university in Peru. The deep learning models implemented are non-hybrid: Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks (CNN) and, hybrid: CNN+GRU, CNN+ LSTM and LSTM+GRU. Since most of the dataset prediction features are of the nominal type (true false), this paper proposes a simple novel data augmentation technique for this type of features. Taking as inspiration the input data type of a neural network, the proposal data augmentation technique considers nominal features as numeric, and obtain random values close to them to generate synthetic records. The results show that most of deep learning models with data augmentation significantly outperform models without data augmentation in terms of accuracy, precision, f1-score and specificity, being the main improvements of 17.39%, 66.67%, 25.00% and 25.00% respectively.
dc.formatapplication/pdf
dc.identifier.citationAnibal Flores, Hugo Tito-Chura, & Lissethe Zea-Rospigliosi. (2023). Prediction of Research Project Execution using Data Augmentation and Deep Learning. Inteligencia Artificial, 26(71), 46–58. https://doi.org/10.4114/intartif.vol26iss71pp46-58
dc.identifier.doihttps://doi.org/10.4114/intartif.vol26iss71pp46-58
dc.identifier.urihttps://repositorio.unam.edu.pe/handle/UNAM/532
dc.language.isoeng
dc.relationPredicción de ejecución de proyectos de investigación aplicando técnicas de inteligencia artificial
dc.relation.ispartofInternational Journal of Artificial Intelligence
dc.relation.urihttps://doi.org/10.4114/intartif.vol26iss71pp46-58
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.uriCopyright (c) 2023 Iberamia & The Authors
dc.sourceRepositorio Institucional - UNAM
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.00
dc.titlePrediction of Research Project Execution using Data Augmentation and Deep Learning
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
Archivos
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
1.71 KB
Formato:
Unknown data format
Descripción:
Descargar
Colecciones
Artículos Científicos publicados en SCOPUS