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Trabalho de evento-anais periodico
Simulating complexity measures on imbalanced datasets (2020)
Barella, Victor Hugo ; Garcia, Luís Paulo Faina ; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, ALGORITMOS ÚTEIS E ESPECÍFICOS, RECONHECIMENTO DE PADRÕES, MATEMÁTICA DA COMPUTAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BARELLA, Victor Hugo e GARCIA, Luís Paulo Faina e CARVALHO, André Carlos Ponce de Leon Ferreira de. Simulating complexity measures on imbalanced datasets. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_34. Acesso em: 04 nov. 2025. , 2020
APA
Barella, V. H., Garcia, L. P. F., & Carvalho, A. C. P. de L. F. de. (2020). Simulating complexity measures on imbalanced datasets. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_34
NLM
Barella VH, Garcia LPF, Carvalho ACP de LF de. Simulating complexity measures on imbalanced datasets [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 498-512.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_34
Vancouver
Barella VH, Garcia LPF, Carvalho ACP de LF de. Simulating complexity measures on imbalanced datasets [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 498-512.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_34
Trabalho de evento-anais periodico
Identifying fine-grained opinion and classifying polarity on coronavirus pandemic (2020)
Vargas, Francielle Alves ; Santos, Rodolfo Sanches Saraiva dos; Rocha, Pedro Regattieri
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: PROCESSAMENTO DE LINGUAGEM NATURAL, MINERAÇÃO DE DADOS, MÍDIAS SOCIAIS, PORTUGUÊS DO BRASIL, SURTOS DE DOENÇAS, CORONAVIRUS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VARGAS, Francielle Alves e SANTOS, Rodolfo Sanches Saraiva dos e ROCHA, Pedro Regattieri. Identifying fine-grained opinion and classifying polarity on coronavirus pandemic. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61377-8_35. Acesso em: 04 nov. 2025. , 2020
APA
Vargas, F. A., Santos, R. S. S. dos, & Rocha, P. R. (2020). Identifying fine-grained opinion and classifying polarity on coronavirus pandemic. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61377-8_35
NLM
Vargas FA, Santos RSS dos, Rocha PR. Identifying fine-grained opinion and classifying polarity on coronavirus pandemic [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12319 511-520.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61377-8_35
Vancouver
Vargas FA, Santos RSS dos, Rocha PR. Identifying fine-grained opinion and classifying polarity on coronavirus pandemic [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12319 511-520.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61377-8_35
Trabalho de evento-anais periodico
Particle competition for unbalanced community detection in complex networks (2020)
Martins, Luan Vinicius Carvalho; Liang, Zhao
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: FFCLRP, ICMC
Assunto: REDES COMPLEXAS
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ABNT
MARTINS, Luan Vinicius Carvalho e LIANG, Zhao. Particle competition for unbalanced community detection in complex networks. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_22. Acesso em: 04 nov. 2025. , 2020
APA
Martins, L. V. C., & Liang, Z. (2020). Particle competition for unbalanced community detection in complex networks. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_22
NLM
Martins LVC, Liang Z. Particle competition for unbalanced community detection in complex networks [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 322-336.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_22
Vancouver
Martins LVC, Liang Z. Particle competition for unbalanced community detection in complex networks [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 322-336.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_22
Trabalho de evento-anais periodico
Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection (2020)
Santos, Victor M. G. dos; Mello, Rodrigo Fernandes de ; Nogueira, Tatiane ; Rios, Ricardo A
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: MINERAÇÃO DE DADOS, ANÁLISE DE SÉRIES TEMPORAIS, MÍDIAS SOCIAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Victor M. G. dos et al. Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_44. Acesso em: 04 nov. 2025. , 2020
APA
Santos, V. M. G. dos, Mello, R. F. de, Nogueira, T., & Rios, R. A. (2020). Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_44
NLM
Santos VMG dos, Mello RF de, Nogueira T, Rios RA. Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 650-664.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_44
Vancouver
Santos VMG dos, Mello RF de, Nogueira T, Rios RA. Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 650-664.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_44
Trabalho de evento-anais periodico
An improved local search genetic algorithm with multi-crossover for job shop scheduling problem (2020)
Viana, Monique Simplicio ; Morandin Junior, Orides ; Contreras, Rodrigo Colnago
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: International Conference on Artificial Intelligence and Soft Computing - ICAISC. Unidade: ICMC
Assuntos: ALGORITMOS GENÉTICOS, OTIMIZAÇÃO COMBINATÓRIA, HEURÍSTICA
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ABNT
VIANA, Monique Simplicio e MORANDIN JUNIOR, Orides e CONTRERAS, Rodrigo Colnago. An improved local search genetic algorithm with multi-crossover for job shop scheduling problem. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61401-0_43. Acesso em: 04 nov. 2025. , 2020
APA
Viana, M. S., Morandin Junior, O., & Contreras, R. C. (2020). An improved local search genetic algorithm with multi-crossover for job shop scheduling problem. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61401-0_43
NLM
Viana MS, Morandin Junior O, Contreras RC. An improved local search genetic algorithm with multi-crossover for job shop scheduling problem [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12415 464-479.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61401-0_43
Vancouver
Viana MS, Morandin Junior O, Contreras RC. An improved local search genetic algorithm with multi-crossover for job shop scheduling problem [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12415 464-479.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61401-0_43
Trabalho de evento-anais periodico
Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network (2020)
Colliri, Tiago Santos ; Delbem, Alexandre Cláudio Botazzo ; Liang, Zhao
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: ICMC, FFCLRP
Assuntos: ANÁLISE DE REGRESSÃO E DE CORRELAÇÃO, COVID-19, REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COLLIRI, Tiago Santos e DELBEM, Alexandre Cláudio Botazzo e LIANG, Zhao. Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_27. Acesso em: 04 nov. 2025. , 2020
APA
Colliri, T. S., Delbem, A. C. B., & Liang, Z. (2020). Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_27
NLM
Colliri TS, Delbem ACB, Liang Z. Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 397-411.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_27
Vancouver
Colliri TS, Delbem ACB, Liang Z. Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 397-411.[citado 2025 nov. 04 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_27

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