Charte Ojeda, F., Rivera Rivas, A. J., Medina, J., & Espinilla, M. (2020). El ecosistema de aprendizaje del estudiante universitario en la post-pandemia. Metodologías y herramientas. Enseñanza y Aprendizaje de Ingeniería de Computadores. https://doi.org/10.30827/Digibug.64779
Antonio Jesús Rivera Rivas
Nombre
Antonio Jesús
Apellidos
Rivera Rivas
2020
2019
Viedma, D. T., Rivera Rivas, A. J., Charte Ojeda, F., & del Jesus Díaz, M. J. (2019). A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy. 270-280. https://doi.org/10.1007/978-3-030-20521-8_23 (Original work published 2019)
Martínez, F., Charte Ojeda, F., Rivera Rivas, A. J., & Frías Bustamante, M. del P. (2019). Automatic Time Series Forecasting with GRNN: A Comparison with Other Models. 198-209. https://doi.org/10.1007/978-3-030-20521-8_17 (Original work published 2019)
Charte Ojeda, F., Rivera Rivas, A. J., Martínez, F., & del Jesus Díaz, M. J. (2019). Automating Autoencoder Architecture Configuration: An Evolutionary Approach. 339-349. https://doi.org/10.1007/978-3-030-19591-5_35 (Original work published 2019)
Charte Ojeda, F., Rivera Rivas, A. J., del Jesus Díaz, M. J., & Herrera Triguero, F. (2019). Dealing with difficult minority labels in imbalanced mutilabel data sets. Neurocomputing, 326, 39-53. https://doi.org/10.1016/j.neucom.2016.08.158
Charte Ojeda, F., Vico, A., Pérez Godoy, M. D., & Rivera Rivas, A. J. (2019). predtoolsTS: R package for streamlining time series forecasting. Progress in Artificial Intelligence, 8, 505-510. https://doi.org/10.1007/s13748-019-00193-z (Original work published 2019)
Charte Ojeda, F., Rivera Rivas, A. J., del Jesus Díaz, M. J., & Herrera Triguero, F. (2019). REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization. Neurocomputing, 326, 110-122. https://doi.org/10.1016/j.neucom.2017.01.118
Martínez, F., Frías Bustamante, M. del P., Charte Ojeda, F., & Rivera Rivas, A. J. (2019). Time Series Forecasting with KNN in R: the tsfknn Package. The R Journal, 11, 229-242. https://doi.org/10.32614/RJ-2019-004 (Original work published 2019)
2018
Pulgar Rubio, F. J., Rivera Rivas, A. J., Charte Ojeda, F., & del Jesus Díaz, M. J. (2018). Análisis del impacto de datos desbalanceados en el rendimiento predictivo de redes neuronales convolucionales. 1213-1218. Granada (Spain). (Original work published)
Martínez, F., Frías Bustamante, M. del P., Pérez Godoy, M. D., & Rivera Rivas, A. J. (2018). Dealing with seasonality by narrowing the training set in time series forecasting with kNN. Expert Systems with Applications, 103, 38-48. https://doi.org/10.1016/j.eswa.2018.03.005