Figueroa Barraza, Joaquín Eduardo 
; López Droguett, Enrique ; Martins, Marcelo Ramos
Assuntos: REDES NEURAIS, ALGORITMOS PARA IMAGENS, APRENDIZADO COMPUTACIONAL, PROGNÓSTICO, DIAGNÓSTICO
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FIGUEROA BARRAZA, Joaquín Eduardo e LÓPEZ DROGUETT, Enrique e MARTINS, Marcelo Ramos. Towards interpretable deep learning: a feature selection framework for prognostics and health management using deep neural networks. Sensors, v. 21, n. 17, p. 1-30, 2021Tradução . . Disponível em: https://doi.org/10.3390/s21175888. Acesso em: 15 set. 2024.APA
Figueroa Barraza, J. E., López Droguett, E., & Martins, M. R. (2021). Towards interpretable deep learning: a feature selection framework for prognostics and health management using deep neural networks. Sensors, 21( 17), 1-30. doi:10.3390/s21175888NLM
Figueroa Barraza JE, López Droguett E, Martins MR. Towards interpretable deep learning: a feature selection framework for prognostics and health management using deep neural networks [Internet]. Sensors. 2021 ; 21( 17): 1-30.[citado 2024 set. 15 ] Available from: https://doi.org/10.3390/s21175888Vancouver
Figueroa Barraza JE, López Droguett E, Martins MR. Towards interpretable deep learning: a feature selection framework for prognostics and health management using deep neural networks [Internet]. Sensors. 2021 ; 21( 17): 1-30.[citado 2024 set. 15 ] Available from: https://doi.org/10.3390/s21175888
