ReP USP - Resultado da busca

Filtros : "Neural Computing and Applications" "Indexado no Current Contents/Engineering" Removido: "MACIEL, LEANDRO DOS SANTOS" Limpar

Filtros

Autores USP
- ehlers, ricardo sandes (1)
- estrella, júlio cezar (1)
- mendiondo, eduardo mario (1)
- santana, marcos josé (1)
- santana, regina helena carlucci (1)

Ano de publicação
- 2016 (~2)
Assuntos
- programação concorrente (2)
- sistemas distribuídos (2)
- algoritmos genéticos (1)
- computação em nuvem (1)
- elasticidade (1)

Artigo de periodico
Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure (2016)
Messias, Valter Rogério; Estrella, Júlio Cezar ; Ehlers, Ricardo Sandes ; Santana, Marcos José; Santana, Regina Helena Carlucci; Reiff-Marganiec, Stephan
Fonte: Neural Computing and Applications. Unidade: ICMC
Assuntos: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE, INFERÊNCIA BAYESIANA, ESTATÍSTICA APLICADA, ALGORITMOS GENÉTICOS, MODELOS EM SÉRIES TEMPORAIS, ELASTICIDADE, COMPUTAÇÃO EM NUVEM
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MESSIAS, Valter Rogério et al. Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure. Neural Computing and Applications, v. No 2016, n. 8, p. 2383-2406, 2016Tradução . . Disponível em: https://doi.org/10.1007/s00521-015-2133-3. Acesso em: 28 nov. 2025.
APA
Messias, V. R., Estrella, J. C., Ehlers, R. S., Santana, M. J., Santana, R. H. C., & Reiff-Marganiec, S. (2016). Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure. Neural Computing and Applications, No 2016( 8), 2383-2406. doi:10.1007/s00521-015-2133-3
NLM
Messias VR, Estrella JC, Ehlers RS, Santana MJ, Santana RHC, Reiff-Marganiec S. Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure [Internet]. Neural Computing and Applications. 2016 ; No 2016( 8): 2383-2406.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-015-2133-3
Vancouver
Messias VR, Estrella JC, Ehlers RS, Santana MJ, Santana RHC, Reiff-Marganiec S. Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure [Internet]. Neural Computing and Applications. 2016 ; No 2016( 8): 2383-2406.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-015-2133-3
Artigo de periodico
Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil (2016)
Furquim, Gustavo; Pessin, Gustavo; Faiçal, Bruno S; Mendiondo, Eduardo Mario ; Ueyama, Jó
Fonte: Neural Computing and Applications. Unidades: EESC, ICMC
Assuntos: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FURQUIM, Gustavo et al. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil. Neural Computing and Applications, v. 27, p. 1129-1141, 2016Tradução . . Disponível em: https://doi.org/10.1007/s00521-015-1930-z. Acesso em: 28 nov. 2025.
APA
Furquim, G., Pessin, G., Faiçal, B. S., Mendiondo, E. M., & Ueyama, J. (2016). Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil. Neural Computing and Applications, 27, 1129-1141. doi:10.1007/s00521-015-1930-z
NLM
Furquim G, Pessin G, Faiçal BS, Mendiondo EM, Ueyama J. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil [Internet]. Neural Computing and Applications. 2016 ; 27 1129-1141.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-015-1930-z
Vancouver
Furquim G, Pessin G, Faiçal BS, Mendiondo EM, Ueyama J. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil [Internet]. Neural Computing and Applications. 2016 ; 27 1129-1141.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-015-1930-z

Biblioteca Digital de Produção Intelectual da Universidade de São Paulo 2012 - 2025