Puentes-Marchal, F., Pérez-Godoy, M., González, P., & del Jesus Díaz, M. J. (2021). Implementation of Data Stream Classification Neural Network Models Over Big Data Platforms. https://doi.org/10.1007/978-3-030-85099-9_22 (Original work published)
Maria José del Jesus Díaz
First name
Maria José
Last name
del Jesus Díaz
2021
García-Vico, Á. M., Carmona, C. J., González García, P., & del Jesus Díaz, M. J. (2021). A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams. Expert Systems with Applications, 183, 115419.
Pulgar Rubio, F. J., Charte Ojeda, F., Rivera Rivas, A. J., & del Jesus Díaz, M. J. (2021). ClEnDAE: A classifier based on ensembles with built-in dimensionality reduction through denoising autoencoders. Information Sciences, 565, 146-176. https://doi.org/10.1016/j.ins.2021.02.060
García-Vico, Á. M., Seker, H., Carmona, C. J., González García, P., & del Jesus Díaz, M. J. (2021). FEPDS: Una propuesta para la extracción de patrones emergentes difusos en flujos continuos de datos. Presentado en.
Puentes, F., Pérez Godoy, M. D., González García, P., & del Jesus Díaz, M. J. (2021). Implementation of Data Stream Classification Neural Network Models Over Big Data Platforms. 272-280. Springer International Publishing. https://doi.org/10.1007/978-3-030-85099-9_22
2020
Rivera Rivas, A. J., Pérez Godoy, M. D., Elizondo, D., Deka, L., & del Jesus Díaz, M. J. (2020). A Preliminary Study on Crop Classification with Unsupervised Algorithms for Time Series on Images with Olive Trees and Cereal Crops. 276-285. https://doi.org/10.1007/978-3-030-57802-2_27 (Original work published 2020)
Puentes, F., Pérez Godoy, M. D., González García, P., & del Jesus Díaz, M. J. (2020). An analysis of technological frameworks for data streams. Progress in Artificial Intelligence, 9, 239-261. https://doi.org/10.1007/s13748-020-00210-6 (Original work published 2020)
Charte, D. ", Charte Ojeda, F., del Jesus Díaz, M. J., & Herrera Triguero, F. (2020). An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges. Neurocomputing, 404, 93-107. https://doi.org/10.1016/j.neucom.2020.04.057
Pulgar Rubio, F. J., Charte Ojeda, F., Rivera Rivas, A. J., & del Jesus Díaz, M. J. (2020). Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines. Information Fusion, 54, 44-60. https://doi.org/10.1016/j.inffus.2019.07.004 (Original work published 2020)
Charte Ojeda, F., Rivera Rivas, A. J., Martínez, F., & del Jesus Díaz, M. J. (2020). EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search. Integrated Computer-Aided Engineering, 27, 211-231. https://doi.org/10.3233/ICA-200619 (Original work published 2020)