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Trabalho de evento-anais periodico
A network-based visual analytics approach for performance evaluation of swarms of robots in the surveillance task (2022)
Linhares, Claudio Douglas Gouveia ; Tinoco, Claudiney Ramos ; Ponciano, Jean Roberto ; Oliveira, Gina Maira Barbosa de; Travençolo, Bruno Augusto Nassif
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: ROBÓTICA, VISÃO COMPUTACIONAL, VISUALIZAÇÃO, REDES COMPLEXAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LINHARES, Claudio Douglas Gouveia et al. A network-based visual analytics approach for performance evaluation of swarms of robots in the surveillance task. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-21686-2_5. Acesso em: 05 nov. 2025. , 2022
APA
Linhares, C. D. G., Tinoco, C. R., Ponciano, J. R., Oliveira, G. M. B. de, & Travençolo, B. A. N. (2022). A network-based visual analytics approach for performance evaluation of swarms of robots in the surveillance task. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-21686-2_5
NLM
Linhares CDG, Tinoco CR, Ponciano JR, Oliveira GMB de, Travençolo BAN. A network-based visual analytics approach for performance evaluation of swarms of robots in the surveillance task [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13653 61-76.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-21686-2_5
Vancouver
Linhares CDG, Tinoco CR, Ponciano JR, Oliveira GMB de, Travençolo BAN. A network-based visual analytics approach for performance evaluation of swarms of robots in the surveillance task [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13653 61-76.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-21686-2_5
Trabalho de evento-anais periodico
Extreme learning machine to graph convolutional networks (2022)
Gonçalves, Thales ; Rodrigues, Francisco Aparecido
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, REDES COMPLEXAS, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GONÇALVES, Thales e RODRIGUES, Francisco Aparecido. Extreme learning machine to graph convolutional networks. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-21689-3_42. Acesso em: 05 nov. 2025. , 2022
APA
Gonçalves, T., & Rodrigues, F. A. (2022). Extreme learning machine to graph convolutional networks. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-21689-3_42
NLM
Gonçalves T, Rodrigues FA. Extreme learning machine to graph convolutional networks [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13654 601-615.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-21689-3_42
Vancouver
Gonçalves T, Rodrigues FA. Extreme learning machine to graph convolutional networks [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13654 601-615.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-21689-3_42
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: 05 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. 05 ] 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. 05 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_22
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: 05 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. 05 ] 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. 05 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_27
Trabalho de evento-anais periodico
M-Flash: fast billion-scale graph computation using a bimodal block processing model (2016)
Gualdron, Hugo; Cordeiro, Robson Leonardo Ferreira ; Rodrigues Junior, José Fernando ; Chau, Duen Horng (Polo); Kahng, Minsuk; Kang, U
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD. Unidade: ICMC
Assuntos: COMPUTAÇÃO GRÁFICA, REDES COMPLEXAS, MINERAÇÃO DE DADOS, ALGORITMOS GRÁFICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GUALDRON, Hugo et al. M-Flash: fast billion-scale graph computation using a bimodal block processing model. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-319-46227-1_39. Acesso em: 05 nov. 2025. , 2016
APA
Gualdron, H., Cordeiro, R. L. F., Rodrigues Junior, J. F., Chau, D. H. (P. ), Kahng, M., & Kang, U. (2016). M-Flash: fast billion-scale graph computation using a bimodal block processing model. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-319-46227-1_39
NLM
Gualdron H, Cordeiro RLF, Rodrigues Junior JF, Chau DH (P), Kahng M, Kang U. M-Flash: fast billion-scale graph computation using a bimodal block processing model [Internet]. Lecture Notes in Artificial Intelligence. 2016 ; 9852 623-640.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-319-46227-1_39
Vancouver
Gualdron H, Cordeiro RLF, Rodrigues Junior JF, Chau DH (P), Kahng M, Kang U. M-Flash: fast billion-scale graph computation using a bimodal block processing model [Internet]. Lecture Notes in Artificial Intelligence. 2016 ; 9852 623-640.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-319-46227-1_39
Trabalho de evento-anais periodico
Uncovering overlap community structure in complex networks using particle competition (2009)
Breve, Fabricio; Liang, Zhao; Quiles, Marcos
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: International Conference on Artificial Intelligence and Computational Intelligence - AICI. Unidade: ICMC
Assuntos: REDES COMPLEXAS, MINERAÇÃO DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BREVE, Fabricio e LIANG, Zhao e QUILES, Marcos. Uncovering overlap community structure in complex networks using particle competition. Lecture Notes in Artificial Intelligence. Berlin: Springer-Verlag. Disponível em: https://doi.org/10.1007/978-3-642-05253-8_68. Acesso em: 05 nov. 2025. , 2009
APA
Breve, F., Liang, Z., & Quiles, M. (2009). Uncovering overlap community structure in complex networks using particle competition. Lecture Notes in Artificial Intelligence. Berlin: Springer-Verlag. doi:10.1007/978-3-642-05253-8_68
NLM
Breve F, Liang Z, Quiles M. Uncovering overlap community structure in complex networks using particle competition [Internet]. Lecture Notes in Artificial Intelligence. 2009 ; 5855 619-628.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-642-05253-8_68
Vancouver
Breve F, Liang Z, Quiles M. Uncovering overlap community structure in complex networks using particle competition [Internet]. Lecture Notes in Artificial Intelligence. 2009 ; 5855 619-628.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-642-05253-8_68

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