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Artigo de periodico
A genetic algorithm based on dual hesitant fuzzy preference relations for consensus group decision making (2022)
Calache, Lucas Daniel Del Rosso; Camargo, Victor Claudio Bento de ; Osiro, Lauro; Carpinetti, Luiz Cesar Ribeiro
Source: Applied Soft Computing Journal. Unidade: EESC
Subjects: TOMADA DE DECISÃO, ALGORITMOS GENÉTICOS, ENGENHARIA DE PRODUÇÃO
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ABNT
CALACHE, Lucas Daniel Del Rosso et al. A genetic algorithm based on dual hesitant fuzzy preference relations for consensus group decision making. Applied Soft Computing Journal, v. 121, p. 1-18, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2022.108778. Acesso em: 06 nov. 2025.
APA
Calache, L. D. D. R., Camargo, V. C. B. de, Osiro, L., & Carpinetti, L. C. R. (2022). A genetic algorithm based on dual hesitant fuzzy preference relations for consensus group decision making. Applied Soft Computing Journal, 121, 1-18. doi:10.1016/j.asoc.2022.108778
NLM
Calache LDDR, Camargo VCB de, Osiro L, Carpinetti LCR. A genetic algorithm based on dual hesitant fuzzy preference relations for consensus group decision making [Internet]. Applied Soft Computing Journal. 2022 ; 121 1-18.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2022.108778
Vancouver
Calache LDDR, Camargo VCB de, Osiro L, Carpinetti LCR. A genetic algorithm based on dual hesitant fuzzy preference relations for consensus group decision making [Internet]. Applied Soft Computing Journal. 2022 ; 121 1-18.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2022.108778
Artigo de periodico
Class-specific early exit design methodology for convolutional neural networks (2021)
Bonato, Vanderlei ; Bouganis, Christos-Savvas
Source: Applied Soft Computing Journal. Unidade: ICMC
Subjects: HARDWARE, INFERÊNCIA, CONSUMO DE ENERGIA ELÉTRICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BONATO, Vanderlei e BOUGANIS, Christos-Savvas. Class-specific early exit design methodology for convolutional neural networks. Applied Soft Computing Journal, v. 107, p. 1-12, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2021.107316. Acesso em: 06 nov. 2025.
APA
Bonato, V., & Bouganis, C. -S. (2021). Class-specific early exit design methodology for convolutional neural networks. Applied Soft Computing Journal, 107, 1-12. doi:10.1016/j.asoc.2021.107316
NLM
Bonato V, Bouganis C-S. Class-specific early exit design methodology for convolutional neural networks [Internet]. Applied Soft Computing Journal. 2021 ; 107 1-12.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2021.107316
Vancouver
Bonato V, Bouganis C-S. Class-specific early exit design methodology for convolutional neural networks [Internet]. Applied Soft Computing Journal. 2021 ; 107 1-12.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2021.107316
Artigo de periodico
Data-driven symbolic ensemble models for wind speed forecasting through evolutionary algorithms (2020)
Dufek, Amanda S; Augusto, Douglas A; Silva Dias, Pedro Leite da ; Barbosa, Helio J.C
Source: Applied Soft Computing Journal. Unidade: IAG
Subjects: PREVISÃO DO TEMPO, VELOCIDADE, VENTO
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ABNT
DUFEK, Amanda S et al. Data-driven symbolic ensemble models for wind speed forecasting through evolutionary algorithms. Applied Soft Computing Journal, v. 87, p. 1-12, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2019.105976. Acesso em: 06 nov. 2025.
APA
Dufek, A. S., Augusto, D. A., Silva Dias, P. L. da, & Barbosa, H. J. C. (2020). Data-driven symbolic ensemble models for wind speed forecasting through evolutionary algorithms. Applied Soft Computing Journal, 87, 1-12. doi:10.1016/j.asoc.2019.105976
NLM
Dufek AS, Augusto DA, Silva Dias PL da, Barbosa HJC. Data-driven symbolic ensemble models for wind speed forecasting through evolutionary algorithms [Internet]. Applied Soft Computing Journal. 2020 ; 87 1-12.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2019.105976
Vancouver
Dufek AS, Augusto DA, Silva Dias PL da, Barbosa HJC. Data-driven symbolic ensemble models for wind speed forecasting through evolutionary algorithms [Internet]. Applied Soft Computing Journal. 2020 ; 87 1-12.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2019.105976
Artigo de periodico
Two effective methods for the irregular knapsack problem (2020)
Queiroz, Layane Rodrigues de Souza ; Andretta, Marina
Source: Applied Soft Computing Journal. Unidade: ICMC
Subjects: ALGORITMOS GENÉTICOS, ANÁLISE DE DESEMPENHO
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ABNT
QUEIROZ, Layane Rodrigues de Souza e ANDRETTA, Marina. Two effective methods for the irregular knapsack problem. Applied Soft Computing Journal, v. 95, p. 1-16, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2020.106485. Acesso em: 06 nov. 2025.
APA
Queiroz, L. R. de S., & Andretta, M. (2020). Two effective methods for the irregular knapsack problem. Applied Soft Computing Journal, 95, 1-16. doi:10.1016/j.asoc.2020.106485
NLM
Queiroz LR de S, Andretta M. Two effective methods for the irregular knapsack problem [Internet]. Applied Soft Computing Journal. 2020 ; 95 1-16.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2020.106485
Vancouver
Queiroz LR de S, Andretta M. Two effective methods for the irregular knapsack problem [Internet]. Applied Soft Computing Journal. 2020 ; 95 1-16.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2020.106485
Artigo de periodico
DSTARS: a multi-target deep structure for tracking asynchronous regressor (2020)
Mastelini, Saulo Martiello ; Santana, Everton José ; Cerri, Ricardo ; Barbon Júnior, Sylvio
Source: Applied Soft Computing Journal. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE REGRESSÃO E DE CORRELAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MASTELINI, Saulo Martiello et al. DSTARS: a multi-target deep structure for tracking asynchronous regressor. Applied Soft Computing Journal, v. 91, p. 1-27, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2020.106215. Acesso em: 06 nov. 2025.
APA
Mastelini, S. M., Santana, E. J., Cerri, R., & Barbon Júnior, S. (2020). DSTARS: a multi-target deep structure for tracking asynchronous regressor. Applied Soft Computing Journal, 91, 1-27. doi:10.1016/j.asoc.2020.106215
NLM
Mastelini SM, Santana EJ, Cerri R, Barbon Júnior S. DSTARS: a multi-target deep structure for tracking asynchronous regressor [Internet]. Applied Soft Computing Journal. 2020 ; 91 1-27.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2020.106215
Vancouver
Mastelini SM, Santana EJ, Cerri R, Barbon Júnior S. DSTARS: a multi-target deep structure for tracking asynchronous regressor [Internet]. Applied Soft Computing Journal. 2020 ; 91 1-27.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2020.106215
Artigo de periodico
Islanding detection of distributed generation by using multi-gene genetic programming based classifier (2019)
Pedrino, Emerson Carlos; Yamada, Thiago; Lunardi, Thiago Reginato; Vieira Júnior, José Carlos de Melo
Source: Applied Soft Computing Journal. Unidade: EESC
Subjects: GERAÇÃO DE ENERGIA ELÉTRICA, PROGRAMAÇÃO GENÉTICA, APRENDIZADO COMPUTACIONAL, PROTEÇÃO DE SISTEMAS ELÉTRICOS, ENGENHARIA ELÉTRICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PEDRINO, Emerson Carlos et al. Islanding detection of distributed generation by using multi-gene genetic programming based classifier. Applied Soft Computing Journal, v. 74, n. Ja 2019, p. 206-215, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2018.10.016. Acesso em: 06 nov. 2025.
APA
Pedrino, E. C., Yamada, T., Lunardi, T. R., & Vieira Júnior, J. C. de M. (2019). Islanding detection of distributed generation by using multi-gene genetic programming based classifier. Applied Soft Computing Journal, 74( Ja 2019), 206-215. doi:10.1016/j.asoc.2018.10.016
NLM
Pedrino EC, Yamada T, Lunardi TR, Vieira Júnior JC de M. Islanding detection of distributed generation by using multi-gene genetic programming based classifier [Internet]. Applied Soft Computing Journal. 2019 ; 74( Ja 2019): 206-215.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2018.10.016
Vancouver
Pedrino EC, Yamada T, Lunardi TR, Vieira Júnior JC de M. Islanding detection of distributed generation by using multi-gene genetic programming based classifier [Internet]. Applied Soft Computing Journal. 2019 ; 74( Ja 2019): 206-215.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2018.10.016
Artigo de periodico
Correcting geometric deviations of CNC machine-tools: an approach with artificial neural networks (2015)
Leite, Wanderson de Oliveira; Rubio, Juan Carlos Campos ; Duduch, Jaime Gilberto ; Almeida, Paulo Eduardo Maciel de
Source: Applied Soft Computing Journal. Unidade: EESC
Subjects: REDES NEURAIS, MÁQUINAS-FERRAMENTA, ENGENHARIA MECÂNICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LEITE, Wanderson de Oliveira et al. Correcting geometric deviations of CNC machine-tools: an approach with artificial neural networks. Applied Soft Computing Journal, v. 36, p. 114-124, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2015.07.014. Acesso em: 06 nov. 2025.
APA
Leite, W. de O., Rubio, J. C. C., Duduch, J. G., & Almeida, P. E. M. de. (2015). Correcting geometric deviations of CNC machine-tools: an approach with artificial neural networks. Applied Soft Computing Journal, 36, 114-124. doi:10.1016/j.asoc.2015.07.014
NLM
Leite W de O, Rubio JCC, Duduch JG, Almeida PEM de. Correcting geometric deviations of CNC machine-tools: an approach with artificial neural networks [Internet]. Applied Soft Computing Journal. 2015 ; 36 114-124.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2015.07.014
Vancouver
Leite W de O, Rubio JCC, Duduch JG, Almeida PEM de. Correcting geometric deviations of CNC machine-tools: an approach with artificial neural networks [Internet]. Applied Soft Computing Journal. 2015 ; 36 114-124.[citado 2025 nov. 06 ] Available from: https://doi.org/10.1016/j.asoc.2015.07.014

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