ReP USP - Resultado da busca

Artigo de periodico
A stochastic bayesian artificial intelligence framework to assess climatological water balance under missing variables for evapotranspiration estimates (2023)
Ribeiro, Vitor P; Desuó Neto, Luiz; Marques, Patricia A. A; Achcar, Jorge Alberto ; Junqueira, Adriano M; Chinatto Junior, Adilson W; Junqueira, Cynthia C. M; Maciel, Carlos Dias ; Balestieri, José Antonio P
Source: Agronomy. Unidades: EESC, FMRP, ESALQ
Subjects: BALANÇO HÍDRICO, EVAPOTRANSPIRAÇÃO, INFERÊNCIA BAYESIANA, INTELIGÊNCIA ARTIFICIAL, IRRIGAÇÃO, MODELOS PARA PROCESSOS ESTOCÁSTICOS, SISTEMAS DE APOIO À DECISÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RIBEIRO, Vitor P et al. A stochastic bayesian artificial intelligence framework to assess climatological water balance under missing variables for evapotranspiration estimates. Agronomy, v. 13, p. 1-28, 2023Tradução . . Disponível em: https://dx.doi.org/10.3390/agronomy13122970. Acesso em: 09 nov. 2024.
APA
Ribeiro, V. P., Desuó Neto, L., Marques, P. A. A., Achcar, J. A., Junqueira, A. M., Chinatto Junior, A. W., et al. (2023). A stochastic bayesian artificial intelligence framework to assess climatological water balance under missing variables for evapotranspiration estimates. Agronomy, 13, 1-28. doi:10.3390/agronomy13122970
NLM
Ribeiro VP, Desuó Neto L, Marques PAA, Achcar JA, Junqueira AM, Chinatto Junior AW, Junqueira CCM, Maciel CD, Balestieri JAP. A stochastic bayesian artificial intelligence framework to assess climatological water balance under missing variables for evapotranspiration estimates [Internet]. Agronomy. 2023 ; 13 1-28.[citado 2024 nov. 09 ] Available from: https://dx.doi.org/10.3390/agronomy13122970
Vancouver
Ribeiro VP, Desuó Neto L, Marques PAA, Achcar JA, Junqueira AM, Chinatto Junior AW, Junqueira CCM, Maciel CD, Balestieri JAP. A stochastic bayesian artificial intelligence framework to assess climatological water balance under missing variables for evapotranspiration estimates [Internet]. Agronomy. 2023 ; 13 1-28.[citado 2024 nov. 09 ] Available from: https://dx.doi.org/10.3390/agronomy13122970
Artigo de periodico
Bayesian multi-targets strategy to track Apis mellifera movements at colony level (2022)
Oliveira Júnior, Jordão Natal de; Santos, Jônatas Cruz; Jumbo, Luis O. Viteri; Almeida, Carlos H. S.; Toledo, Pedro F. S.; Rezende, Sarah M.; Haddi, Khalid; Santana, Weyder C.; Bessani, Michel; Achcar, Jorge Alberto ; Oliveira, Eugenio E.; Maciel, Carlos Dias
Source: Insects. Unidades: FMRP, EESC
Subjects: ENTROPIA, ABELHAS, PESTICIDAS, COLÔNIAS (BIOLOGIA), ALGORITMOS, INFERÊNCIA BAYESIANA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVEIRA JÚNIOR, Jordão Natal de et al. Bayesian multi-targets strategy to track Apis mellifera movements at colony level. Insects, v. 13, n. 2, p. 1-12, 2022Tradução . . Disponível em: https://doi.org/10.3390/insects13020181. Acesso em: 09 nov. 2024.
APA
Oliveira Júnior, J. N. de, Santos, J. C., Jumbo, L. O. V., Almeida, C. H. S., Toledo, P. F. S., Rezende, S. M., et al. (2022). Bayesian multi-targets strategy to track Apis mellifera movements at colony level. Insects, 13( 2), 1-12. doi:10.3390/insects13020181
NLM
Oliveira Júnior JN de, Santos JC, Jumbo LOV, Almeida CHS, Toledo PFS, Rezende SM, Haddi K, Santana WC, Bessani M, Achcar JA, Oliveira EE, Maciel CD. Bayesian multi-targets strategy to track Apis mellifera movements at colony level [Internet]. Insects. 2022 ; 13( 2): 1-12.[citado 2024 nov. 09 ] Available from: https://doi.org/10.3390/insects13020181
Vancouver
Oliveira Júnior JN de, Santos JC, Jumbo LOV, Almeida CHS, Toledo PFS, Rezende SM, Haddi K, Santana WC, Bessani M, Achcar JA, Oliveira EE, Maciel CD. Bayesian multi-targets strategy to track Apis mellifera movements at colony level [Internet]. Insects. 2022 ; 13( 2): 1-12.[citado 2024 nov. 09 ] Available from: https://doi.org/10.3390/insects13020181
Trabalho de evento
A statistical analysis and modeling of repair data from a brazilian power distribution system (2016)
Bessani, Michel; Fanucchi, Rodrigo Zempulski; Achcar, Jorge Alberto ; Maciel, Carlos Dias
Source: Proceedings. Conference titles: International Conference on Harmonics and Quality of Power - ICHQP. Unidades: EESC, FMRP
Subjects: DISTRIBUIÇÃO DE ENERGIA ELÉTRICA, MODELAGEM DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BESSANI, Michel et al. A statistical analysis and modeling of repair data from a brazilian power distribution system. 2016, Anais.. Danvers, MA: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2016. Disponível em: http://dx.doi.org/10.1109/ICHQP.2016.7783446. Acesso em: 09 nov. 2024.
APA
Bessani, M., Fanucchi, R. Z., Achcar, J. A., & Maciel, C. D. (2016). A statistical analysis and modeling of repair data from a brazilian power distribution system. In Proceedings. Danvers, MA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/ICHQP.2016.7783446
NLM
Bessani M, Fanucchi RZ, Achcar JA, Maciel CD. A statistical analysis and modeling of repair data from a brazilian power distribution system [Internet]. Proceedings. 2016 ;[citado 2024 nov. 09 ] Available from: http://dx.doi.org/10.1109/ICHQP.2016.7783446
Vancouver
Bessani M, Fanucchi RZ, Achcar JA, Maciel CD. A statistical analysis and modeling of repair data from a brazilian power distribution system [Internet]. Proceedings. 2016 ;[citado 2024 nov. 09 ] Available from: http://dx.doi.org/10.1109/ICHQP.2016.7783446
Trabalho de evento
Bayesian versus neural network analysis of algae data population: a new method to predict and analyse cause and effect (2013)
Lee, Jen John; Achcar, Jorge Alberto ; Barros, Emilio Augusto Coelho; Maciel, Carlos Dias
Conference titles: International Joint Conference on Computational Intelligence - IJCCI. Unidades: FMRP, EESC
Subjects: REDES NEURAIS, ALGAS, INFERÊNCIA BAYESIANA, APRENDIZAGEM
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LEE, Jen John et al. Bayesian versus neural network analysis of algae data population: a new method to predict and analyse cause and effect. 2013, Anais.. Vilamoura: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, 2013. Disponível em: https://repositorio.usp.br/directbitstream/853283aa-5729-49e2-ad7b-195b55d2042f/002417397.pdf. Acesso em: 09 nov. 2024.
APA
Lee, J. J., Achcar, J. A., Barros, E. A. C., & Maciel, C. D. (2013). Bayesian versus neural network analysis of algae data population: a new method to predict and analyse cause and effect. In . Vilamoura: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. Recuperado de https://repositorio.usp.br/directbitstream/853283aa-5729-49e2-ad7b-195b55d2042f/002417397.pdf
NLM
Lee JJ, Achcar JA, Barros EAC, Maciel CD. Bayesian versus neural network analysis of algae data population: a new method to predict and analyse cause and effect [Internet]. 2013 ;[citado 2024 nov. 09 ] Available from: https://repositorio.usp.br/directbitstream/853283aa-5729-49e2-ad7b-195b55d2042f/002417397.pdf
Vancouver
Lee JJ, Achcar JA, Barros EAC, Maciel CD. Bayesian versus neural network analysis of algae data population: a new method to predict and analyse cause and effect [Internet]. 2013 ;[citado 2024 nov. 09 ] Available from: https://repositorio.usp.br/directbitstream/853283aa-5729-49e2-ad7b-195b55d2042f/002417397.pdf

USP Schools

ReP

Filtros

Authors

USP affiliated authors

Subjects

Funding Agencies

Indexado em

Non normalized affiliation