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Artigo de periodico
A river flooding detection system based on deep learning and computer vision (2022)
Fernandes Junior, Francisco Erivaldo ; Nonato, Luis Gustavo ; Ueyama, Jó
Fonte: Multimedia Tools and Applications. Unidade: ICMC
Assuntos: VISÃO COMPUTACIONAL, ENCHENTES URBANAS, SEMÂNTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERNANDES JUNIOR, Francisco Erivaldo e NONATO, Luis Gustavo e UEYAMA, Jó. A river flooding detection system based on deep learning and computer vision. Multimedia Tools and Applications, v. 81, p. 40231-40251, 2022Tradução . . Disponível em: https://doi.org/10.1007/s11042-022-12813-3. Acesso em: 27 nov. 2025.
APA
Fernandes Junior, F. E., Nonato, L. G., & Ueyama, J. (2022). A river flooding detection system based on deep learning and computer vision. Multimedia Tools and Applications, 81, 40231-40251. doi:10.1007/s11042-022-12813-3
NLM
Fernandes Junior FE, Nonato LG, Ueyama J. A river flooding detection system based on deep learning and computer vision [Internet]. Multimedia Tools and Applications. 2022 ; 81 40231-40251.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/s11042-022-12813-3
Vancouver
Fernandes Junior FE, Nonato LG, Ueyama J. A river flooding detection system based on deep learning and computer vision [Internet]. Multimedia Tools and Applications. 2022 ; 81 40231-40251.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/s11042-022-12813-3
Artigo de periodico
Pruning deep convolutional neural networks architectures with evolution strategy (2021)
Fernandes Junior, Francisco Erivaldo ; Yen, Gary G
Fonte: Information Sciences. Unidade: ICMC
Assuntos: REDES NEURAIS, ALGORITMOS GENÉTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERNANDES JUNIOR, Francisco Erivaldo e YEN, Gary G. Pruning deep convolutional neural networks architectures with evolution strategy. Information Sciences, v. 552, p. 29-47, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2020.11.009. Acesso em: 27 nov. 2025.
APA
Fernandes Junior, F. E., & Yen, G. G. (2021). Pruning deep convolutional neural networks architectures with evolution strategy. Information Sciences, 552, 29-47. doi:10.1016/j.ins.2020.11.009
NLM
Fernandes Junior FE, Yen GG. Pruning deep convolutional neural networks architectures with evolution strategy [Internet]. Information Sciences. 2021 ; 552 29-47.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.ins.2020.11.009
Vancouver
Fernandes Junior FE, Yen GG. Pruning deep convolutional neural networks architectures with evolution strategy [Internet]. Information Sciences. 2021 ; 552 29-47.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.ins.2020.11.009
Artigo de periodico
Pruning of generative adversarial neural networks for medical imaging diagnostics with evolution strategy (2021)
Fernandes Junior, Francisco Erivaldo ; Yen, Gary G
Fonte: Information Sciences. Unidade: ICMC
Assuntos: REDES NEURAIS, ALGORITMOS GENÉTICOS, DIAGNÓSTICO POR IMAGEM
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERNANDES JUNIOR, Francisco Erivaldo e YEN, Gary G. Pruning of generative adversarial neural networks for medical imaging diagnostics with evolution strategy. Information Sciences, v. 558, p. 91-102, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2020.12.086. Acesso em: 27 nov. 2025.
APA
Fernandes Junior, F. E., & Yen, G. G. (2021). Pruning of generative adversarial neural networks for medical imaging diagnostics with evolution strategy. Information Sciences, 558, 91-102. doi:10.1016/j.ins.2020.12.086
NLM
Fernandes Junior FE, Yen GG. Pruning of generative adversarial neural networks for medical imaging diagnostics with evolution strategy [Internet]. Information Sciences. 2021 ; 558 91-102.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.ins.2020.12.086
Vancouver
Fernandes Junior FE, Yen GG. Pruning of generative adversarial neural networks for medical imaging diagnostics with evolution strategy [Internet]. Information Sciences. 2021 ; 558 91-102.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.ins.2020.12.086

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