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SCI2S Publications (S. Tabik)

Number of Results: 9

Jump to Year: 2021, 2020, 2019, 2018, 2017

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2021 (2)

  • [2853] A. D. Martinez, J. del Ser, E. Villa-Rodriguez, E. Osaka, J. Poyatos, S. Tabik, D. Molina, F. Herrera. Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons,Recommendations and Challenges. Information Fusion 67 (2021) 161-194. doi: 10.1016/j.inffus.2020.10.014 PDF Icon BibTex Icon
  • [2903] A. Lamas, S. Tabik, P. Cruz, R. Montes, A. Martínez-Sevilla, T. Cruz, F. Herrera. MonuMAI: Dataset, deep learning pipeline and citizen science based app for monumental heritage taxonomy and classification. Neurocomputing 420 (2021)266-280. doi: j.neucom.2020.09.041


2020 (1)

  • [2736] A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-Robles, D. Molina, R. Benjamins, R. Chatila, F. Herrera. Explainable ArtificialIntelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58 (2020) 82-115. doi: 10.1016/j.inffus.2019.12.012 PDF Icon BibTex Icon


2019 (2)


2018 (3)

  • [2372] R. Olmos, S. Tabik, F. Herrera. Automatic Handgun Detection Alarm in Videos Using Deep Learning. Neurocomputing 275, 66-72 (2018).. doi: 10.1016/j.neucom.2017.05.012
  • [2602] A. Gómez-Ríos, S. Tabik, J. Luengo, ASM Shihavuddin, B. Krawczyk, F. Herrera. Redes Neuronales Convolucionales para una Clasificación Precisa de Imágenes de Corales. XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018), I Workshop en Deep Learning (DEEPL 2018), Octubre 23-26, 2018.
  • [2627] S. Tabik, M. Peemen, L. F. Romero. A tuning approach for iterative multiple 3d stencil pipeline on GPUs: Anisotropic Nonlinear Diffusion algorithm as case study. The Journal of Supercomputing 74:4 (2018) 1580-1608. doi: 10.1007/s11227-017-2184-6


2017 (1)

  • [2190] S. Tabik, D. Peralta, A. Herrera-Poyatos, F. Herrera. A snapshot of image pre-processing for convolutional neural networks: case study of MNIST. International Journal of Computational Intelligence Systems 10 (2017) 555-568. PDF Icon