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Artigo de periodico
Is a poster a strong signal of film quality?: evaluating the predictive power of visual elements using deep learning (2025)
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, v. 84, p. 26877–26905, 2025Tradução . . Disponível em: https://doi.org/10.1007/s11042-024-20174-2. Acesso em: 08 out. 2025.
APA
Souza, T. L. D. e, Ranieri, C. M., Panangadan, A., Ueyama, J., & Nishijima, M. (2025). Is a poster a strong signal of film quality?: evaluating the predictive power of visual elements using deep learning. Multimedia Tools and Applications, 84, 26877–26905. 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. 2025 ; 84 26877–26905.[citado 2025 out. 08 ] 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. 2025 ; 84 26877–26905.[citado 2025 out. 08 ] Available from: https://doi.org/10.1007/s11042-024-20174-2
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: 08 out. 2025.
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 2025 out. 08 ] 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 2025 out. 08 ] Available from: https://doi.org/10.1007/s10489-024-05466-2
Artigo de periodico
Primary healthcare effects of a well-designed anti-corruption program (2022)
Nishijima, Marislei ; Ellis, Randall P.; Cati, Regina C.
Fonte: World Development Perspectives. Unidade: IRI
Assuntos: ATENÇÃO PRIMÁRIA À SAÚDE, SISTEMA JUDICIÁRIO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
NISHIJIMA, Marislei e ELLIS, Randall P. e CATI, Regina C. Primary healthcare effects of a well-designed anti-corruption program. World Development Perspectives, v. 25, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.wdp.2022.100399. Acesso em: 08 out. 2025.
APA
Nishijima, M., Ellis, R. P., & Cati, R. C. (2022). Primary healthcare effects of a well-designed anti-corruption program. World Development Perspectives, 25. doi:10.1016/j.wdp.2022.100399
NLM
Nishijima M, Ellis RP, Cati RC. Primary healthcare effects of a well-designed anti-corruption program [Internet]. World Development Perspectives. 2022 ; 25[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.wdp.2022.100399
Vancouver
Nishijima M, Ellis RP, Cati RC. Primary healthcare effects of a well-designed anti-corruption program [Internet]. World Development Perspectives. 2022 ; 25[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.wdp.2022.100399
Artigo de periodico
Is Rotten Tomatoes killing the movie industry? A regression discontinuity approach (2021)
Nishijima, Marislei ; Rodrigues Júnior, Mauro; Souza, Thais Luiza Donega e
Fonte: Applied Economics Letters. Unidades: IRI, FEA, EACH
Assuntos: INDÚSTRIA CINEMATOGRÁFICA, FILMES, FILMES, CINEMA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
NISHIJIMA, Marislei e RODRIGUES JÚNIOR, Mauro e SOUZA, Thais Luiza Donega e. Is Rotten Tomatoes killing the movie industry? A regression discontinuity approach. Applied Economics Letters, 2021Tradução . . Disponível em: https://doi.org/10.1080/13504851.2021.1918324. Acesso em: 08 out. 2025.
APA
Nishijima, M., Rodrigues Júnior, M., & Souza, T. L. D. e. (2021). Is Rotten Tomatoes killing the movie industry? A regression discontinuity approach. Applied Economics Letters. doi:10.1080/13504851.2021.1918324
NLM
Nishijima M, Rodrigues Júnior M, Souza TLD e. Is Rotten Tomatoes killing the movie industry? A regression discontinuity approach [Internet]. Applied Economics Letters. 2021 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1080/13504851.2021.1918324
Vancouver
Nishijima M, Rodrigues Júnior M, Souza TLD e. Is Rotten Tomatoes killing the movie industry? A regression discontinuity approach [Internet]. Applied Economics Letters. 2021 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1080/13504851.2021.1918324

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