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Artigo de periodico
A deep learning workflow enhanced with optical flow fields for flood risk estimation (2024)
Ranieri, Caetano Mazzoni ; Souza, Thais Luiza Donega e ; Nishijima, Marislei ; Krishnamachari, Bhaskar ; Ueyama, Jó
Fonte: Applied Intelligence. Unidades: ICMC, IRI, EACH
Assuntos: ENCHENTES URBANAS, APRENDIZAGEM PROFUNDA, PROCESSAMENTO DE IMAGENS, VISÃO COMPUTACIONAL, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RANIERI, Caetano Mazzoni et al. A deep learning workflow enhanced with optical flow fields for flood risk estimation. Applied Intelligence, v. 54, p. 5536-5557, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10489-024-05466-2. Acesso em: 12 out. 2024.
APA
Ranieri, C. M., Souza, T. L. D. e, Nishijima, M., Krishnamachari, B., & Ueyama, J. (2024). A deep learning workflow enhanced with optical flow fields for flood risk estimation. Applied Intelligence, 54, 5536-5557. doi:10.1007/s10489-024-05466-2
NLM
Ranieri CM, Souza TLD e, Nishijima M, Krishnamachari B, Ueyama J. A deep learning workflow enhanced with optical flow fields for flood risk estimation [Internet]. Applied Intelligence. 2024 ; 54 5536-5557.[citado 2024 out. 12 ] Available from: https://doi.org/10.1007/s10489-024-05466-2
Vancouver
Ranieri CM, Souza TLD e, Nishijima M, Krishnamachari B, Ueyama J. A deep learning workflow enhanced with optical flow fields for flood risk estimation [Internet]. Applied Intelligence. 2024 ; 54 5536-5557.[citado 2024 out. 12 ] Available from: https://doi.org/10.1007/s10489-024-05466-2
Artigo de periodico
Is a poster a strong signal of film quality?: evaluating the predictive power of visual elements using deep learning (2024)
Souza, Thais Luiza Donega e ; Ranieri, Caetano Mazzoni ; Panangadan, Anand ; Ueyama, Jó ; Nishijima, Marislei
Fonte: Multimedia Tools and Applications. Unidades: ICMC, IRI, EACH
Assuntos: REDES NEURAIS, CINEMA, CARTAZ, QUALIDADE DO PRODUTO, APRENDIZAGEM PROFUNDA, DESIGN GRÁFICO, PROPAGANDA PELO CINEMA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUZA, Thais Luiza Donega e et al. Is a poster a strong signal of film quality?: evaluating the predictive power of visual elements using deep learning. Multimedia Tools and Applications, 2024Tradução . . Disponível em: https://doi.org/10.1007/s11042-024-20174-2. Acesso em: 12 out. 2024.
APA
Souza, T. L. D. e, Ranieri, C. M., Panangadan, A., Ueyama, J., & Nishijima, M. (2024). Is a poster a strong signal of film quality?: evaluating the predictive power of visual elements using deep learning. Multimedia Tools and Applications. doi:10.1007/s11042-024-20174-2
NLM
Souza TLD e, Ranieri CM, Panangadan A, Ueyama J, Nishijima M. Is a poster a strong signal of film quality?: evaluating the predictive power of visual elements using deep learning [Internet]. Multimedia Tools and Applications. 2024 ;[citado 2024 out. 12 ] Available from: https://doi.org/10.1007/s11042-024-20174-2
Vancouver
Souza TLD e, Ranieri CM, Panangadan A, Ueyama J, Nishijima M. Is a poster a strong signal of film quality?: evaluating the predictive power of visual elements using deep learning [Internet]. Multimedia Tools and Applications. 2024 ;[citado 2024 out. 12 ] Available from: https://doi.org/10.1007/s11042-024-20174-2
Artigo de periodico
Towards HPC simulations of billion-cell reservoirs by multiscale mixed methods (2022)
Jaramillo, Alfredo ; Guiraldello, Rafael Trevisanuto ; Sanchez, Stevens Paz ; Ausas, Roberto Federico ; Sousa, Fabricio Simeoni de ; Pereira, Felipe; Buscaglia, Gustavo Carlos
Fonte: Computational Geosciences. Unidades: ICMC, IRI
Assuntos: AVALIAÇÃO DE DESEMPENHO, ESCOAMENTO BIFÁSICO, BENCHMARKS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
JARAMILLO, Alfredo et al. Towards HPC simulations of billion-cell reservoirs by multiscale mixed methods. Computational Geosciences, v. 26, p. 481-501, 2022Tradução . . Disponível em: https://doi.org/10.1007/s10596-022-10131-z. Acesso em: 12 out. 2024.
APA
Jaramillo, A., Guiraldello, R. T., Sanchez, S. P., Ausas, R. F., Sousa, F. S. de, Pereira, F., & Buscaglia, G. C. (2022). Towards HPC simulations of billion-cell reservoirs by multiscale mixed methods. Computational Geosciences, 26, 481-501. doi:10.1007/s10596-022-10131-z
NLM
Jaramillo A, Guiraldello RT, Sanchez SP, Ausas RF, Sousa FS de, Pereira F, Buscaglia GC. Towards HPC simulations of billion-cell reservoirs by multiscale mixed methods [Internet]. Computational Geosciences. 2022 ; 26 481-501.[citado 2024 out. 12 ] Available from: https://doi.org/10.1007/s10596-022-10131-z
Vancouver
Jaramillo A, Guiraldello RT, Sanchez SP, Ausas RF, Sousa FS de, Pereira F, Buscaglia GC. Towards HPC simulations of billion-cell reservoirs by multiscale mixed methods [Internet]. Computational Geosciences. 2022 ; 26 481-501.[citado 2024 out. 12 ] Available from: https://doi.org/10.1007/s10596-022-10131-z

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