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Artigo de periodico
Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers (2024)
Rocha Filho, Geraldo Pereira ; Meneguette, Rodolfo Ipolito ; Mendonça, Fabio Lucio Lopes de ; Enamoto, Liriam Michi ; Pessin, Gustavo ; Gonçalves, Vinicius Pereira
Source: Neural Computing and Applications. Unidade: ICMC
Subjects: EMOÇÕES, VOZ, REDES NEURAIS, RECONHECIMENTO DE VOZ
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ROCHA FILHO, Geraldo Pereira et al. Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers. Neural Computing and Applications, v. 36, p. 19939–19950, 2024Tradução . . Disponível em: https://doi.org/10.1007/s00521-024-10249-4. Acesso em: 16 jun. 2025.
APA
Rocha Filho, G. P., Meneguette, R. I., Mendonça, F. L. L. de, Enamoto, L. M., Pessin, G., & Gonçalves, V. P. (2024). Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers. Neural Computing and Applications, 36, 19939–19950. doi:10.1007/s00521-024-10249-4
NLM
Rocha Filho GP, Meneguette RI, Mendonça FLL de, Enamoto LM, Pessin G, Gonçalves VP. Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers [Internet]. Neural Computing and Applications. 2024 ; 36 19939–19950.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-024-10249-4
Vancouver
Rocha Filho GP, Meneguette RI, Mendonça FLL de, Enamoto LM, Pessin G, Gonçalves VP. Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers [Internet]. Neural Computing and Applications. 2024 ; 36 19939–19950.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-024-10249-4
Trabalho de evento-anais periodico
Effective sample size, dimensionality, and generalization in covariate shift adaptation (2023)
Polo, Felipe Maia ; Vicente, Renato
Source: Neural Computing and Applications. Conference titles: LatinX in AI at NeurIPS. Unidade: IME
Assunto: MATEMÁTICA APLICADA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
POLO, Felipe Maia e VICENTE, Renato. Effective sample size, dimensionality, and generalization in covariate shift adaptation. Neural Computing and Applications. Godalming: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/s00521-021-06615-1. Acesso em: 16 jun. 2025. , 2023
APA
Polo, F. M., & Vicente, R. (2023). Effective sample size, dimensionality, and generalization in covariate shift adaptation. Neural Computing and Applications. Godalming: Instituto de Matemática e Estatística, Universidade de São Paulo. doi:10.1007/s00521-021-06615-1
NLM
Polo FM, Vicente R. Effective sample size, dimensionality, and generalization in covariate shift adaptation [Internet]. Neural Computing and Applications. 2023 ; 35( 25): 18187-18199.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-021-06615-1
Vancouver
Polo FM, Vicente R. Effective sample size, dimensionality, and generalization in covariate shift adaptation [Internet]. Neural Computing and Applications. 2023 ; 35( 25): 18187-18199.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-021-06615-1
Artigo de periodico
OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects (2018)
Sousa, Miguel Angelo de Abreu de; Del Moral Hernandez, Emilio ; Pires, Ricardo
Source: Neural Computing and Applications. Unidade: EP
Assunto: SISTEMAS EMBUTIDOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUSA, Miguel Angelo de Abreu de e DEL MORAL HERNANDEZ, Emilio e PIRES, Ricardo. OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects. Neural Computing and Applications, v. 30, n. 12, p. 3759-3771, 2018Tradução . . Disponível em: https://doi.org/10.1007/s00521-017-2957-0. Acesso em: 16 jun. 2025.
APA
Sousa, M. A. de A. de, Del Moral Hernandez, E., & Pires, R. (2018). OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects. Neural Computing and Applications, 30( 12), 3759-3771. doi:10.1007/s00521-017-2957-0
NLM
Sousa MA de A de, Del Moral Hernandez E, Pires R. OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects [Internet]. Neural Computing and Applications. 2018 ; 30( 12): 3759-3771.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-017-2957-0
Vancouver
Sousa MA de A de, Del Moral Hernandez E, Pires R. OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects [Internet]. Neural Computing and Applications. 2018 ; 30( 12): 3759-3771.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-017-2957-0
Artigo de periodico
Classification of electromyography signals using relevance vector machines and fractal dimension (2016)
Lima, Clodoaldo Aparecido de Moraes; Coelho, André L.V; Madeo, Renata Cristina Barros; Peres, Sarajane Marques
Source: Neural Computing and Applications. Unidade: EACH
Subjects: RECONHECIMENTO DE PADRÕES, INTELIGÊNCIA ARTIFICIAL, ANÁLISE DO MOVIMENTO HUMANO, ELETROMIOGRAFIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LIMA, Clodoaldo Aparecido de Moraes et al. Classification of electromyography signals using relevance vector machines and fractal dimension. Neural Computing and Applications, v. 27, n. 3, p. 791-804, 2016Tradução . . Disponível em: https://doi.org/10.1007/s00521-015-1953-5. Acesso em: 16 jun. 2025.
APA
Lima, C. A. de M., Coelho, A. L. V., Madeo, R. C. B., & Peres, S. M. (2016). Classification of electromyography signals using relevance vector machines and fractal dimension. Neural Computing and Applications, 27( 3), 791-804. doi:10.1007/s00521-015-1953-5
NLM
Lima CA de M, Coelho ALV, Madeo RCB, Peres SM. Classification of electromyography signals using relevance vector machines and fractal dimension [Internet]. Neural Computing and Applications. 2016 ; 27( 3): 791-804.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-1953-5
Vancouver
Lima CA de M, Coelho ALV, Madeo RCB, Peres SM. Classification of electromyography signals using relevance vector machines and fractal dimension [Internet]. Neural Computing and Applications. 2016 ; 27( 3): 791-804.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-1953-5
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
Source: Neural Computing and Applications. Unidade: ICMC
Subjects: 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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] 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
Source: Neural Computing and Applications. Unidades: EESC, ICMC
Subjects: 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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-1930-z

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