García-Vico, Ángel 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. Presented at the.
Maria José del Jesus Díaz
First name
Maria José
Last name
del Jesus Díaz
2021
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
Escobar, A. ., González García, P. ., & del Jesus Díaz, M. J. . (2021). Revisión y análisis de conceptos en Inteligencia Artificial Explicable. Una aproximación a la unificación de la terminología.
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)
2020
García-Vico, Ángel M. ., Carmona, C. J. ., González García, P. ., Seker, H. ., & del Jesus Díaz, M. J. . (2020). FEPDS: A Proposal for the Extraction of Fuzzy Emerging Patterns in Data Streams. IEEE Transactions on Fuzzy Systems, 28, 3193-3203. https://doi.org/10.1109/TFUZZ.2020.2992849 (Original work published 2020)
Carmona, C. J. ., González García, P. ., García-Vico, Ángel M. ., & del Jesus Díaz, M. J. . (2020). A Preliminary Many Objective Approach for Extracting Fuzzy Emerging Patterns. 1268, 100. https://doi.org/10.1007/978-3-030-57802-2_10
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)