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Filtros : "Liang, Zhao" "ICMC" Removidos: "PROCESSAMENTO DE IMAGENS" "Lai, Ying-Cheng" "Biometrical Journal" Limpar

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  • Artigo de periodico

    Coarsening algorithm based on multi-label propagation for knowledge discovery in bipartite networks (2024)

    Valejo, Alan Demetrius Baria ; Althoff, Paulo Eduardo ; Faleiros, Thiago de Paulo ; Rocha Filho, Geraldo Pereira ; Yu-Tao, Zhu ; Jianglong, Yan; Weiguang, Liu; Liang, Zhao

    Source: IEEE Transactions on Network Science and Engineering. Unidades: FFCLRP, ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, DESCOBERTA DE CONHECIMENTO, ALGORITMOS ÚTEIS E ESPECÍFICOS, REDES COMPLEXAS

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      VALEJO, Alan Demetrius Baria et al. Coarsening algorithm based on multi-label propagation for knowledge discovery in bipartite networks. IEEE Transactions on Network Science and Engineering, v. 11, n. 2, p. 1799-1809, 2024Tradução . . Disponível em: https://doi.org/10.1109/TNSE.2023.3331655. Acesso em: 07 nov. 2024.

    • APA

      Valejo, A. D. B., Althoff, P. E., Faleiros, T. de P., Rocha Filho, G. P., Yu-Tao, Z., Jianglong, Y., et al. (2024). Coarsening algorithm based on multi-label propagation for knowledge discovery in bipartite networks. IEEE Transactions on Network Science and Engineering, 11( 2), 1799-1809. doi:10.1109/TNSE.2023.3331655

    • NLM

      Valejo ADB, Althoff PE, Faleiros T de P, Rocha Filho GP, Yu-Tao Z, Jianglong Y, Weiguang L, Liang Z. Coarsening algorithm based on multi-label propagation for knowledge discovery in bipartite networks [Internet]. IEEE Transactions on Network Science and Engineering. 2024 ; 11( 2): 1799-1809.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1109/TNSE.2023.3331655

    • Vancouver

      Valejo ADB, Althoff PE, Faleiros T de P, Rocha Filho GP, Yu-Tao Z, Jianglong Y, Weiguang L, Liang Z. Coarsening algorithm based on multi-label propagation for knowledge discovery in bipartite networks [Internet]. IEEE Transactions on Network Science and Engineering. 2024 ; 11( 2): 1799-1809.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1109/TNSE.2023.3331655

  • Trabalho de evento

    Modelling graph neural network by aggregating the activation maps of self-organizing map (2024)

    Martins, Luan Vinicius de Carvalho ; Donghong, Ji; Liang, Zhao

    Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC

    Subjects: REDES NEURAIS, TEORIA DOS GRAFOS

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      MARTINS, Luan Vinicius de Carvalho e DONGHONG, Ji e LIANG, Zhao. Modelling graph neural network by aggregating the activation maps of self-organizing map. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/IJCNN60899.2024.10650514. Acesso em: 07 nov. 2024.

    • APA

      Martins, L. V. de C., Donghong, J., & Liang, Z. (2024). Modelling graph neural network by aggregating the activation maps of self-organizing map. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN60899.2024.10650514

    • NLM

      Martins LV de C, Donghong J, Liang Z. Modelling graph neural network by aggregating the activation maps of self-organizing map [Internet]. Proceedings. 2024 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650514

    • Vancouver

      Martins LV de C, Donghong J, Liang Z. Modelling graph neural network by aggregating the activation maps of self-organizing map [Internet]. Proceedings. 2024 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650514

  • Trabalho de evento

    TransGNN: a transductive graph neural network with graph dynamic embedding (2023)

    Anghinoni, Leandro ; Yu-Tao, Zhu; Donghong, Ji; Liang, Zhao

    Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS

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      ANGHINONI, Leandro et al. TransGNN: a transductive graph neural network with graph dynamic embedding. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/IJCNN54540.2023.10191134. Acesso em: 07 nov. 2024.

    • APA

      Anghinoni, L., Yu-Tao, Z., Donghong, J., & Liang, Z. (2023). TransGNN: a transductive graph neural network with graph dynamic embedding. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN54540.2023.10191134

    • NLM

      Anghinoni L, Yu-Tao Z, Donghong J, Liang Z. TransGNN: a transductive graph neural network with graph dynamic embedding [Internet]. Proceedings. 2023 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191134

    • Vancouver

      Anghinoni L, Yu-Tao Z, Donghong J, Liang Z. TransGNN: a transductive graph neural network with graph dynamic embedding [Internet]. Proceedings. 2023 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191134

  • Trabalho de evento

    WSI2ML: an open-source whole slide image annotation software for machine learning applications (2023)

    Martins, Luan Vinicius de Carvalho ; Bueno, Adriana Passos ; Defelicibus, Alexandre ; Drummond, Rodrigo Duarte ; Valieris, Renan ; Yu-Tao, Zhu ; Silva, Israel Tojal da ; Liang, Zhao

    Source: Proceedings. Conference titles: Brazilian Symposium on Multimedia and the Web - WebMedia. Unidades: FFCLRP, ICMC

    Subjects: BIOINFORMÁTICA, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, PATOLOGIA CLÍNICA, TECNOLOGIAS DA SAÚDE

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      MARTINS, Luan Vinicius de Carvalho et al. WSI2ML: an open-source whole slide image annotation software for machine learning applications. 2023, Anais.. New York: ACM, 2023. Disponível em: https://doi.org/10.1145/3617023.3617038. Acesso em: 07 nov. 2024.

    • APA

      Martins, L. V. de C., Bueno, A. P., Defelicibus, A., Drummond, R. D., Valieris, R., Yu-Tao, Z., et al. (2023). WSI2ML: an open-source whole slide image annotation software for machine learning applications. In Proceedings. New York: ACM. doi:10.1145/3617023.3617038

    • NLM

      Martins LV de C, Bueno AP, Defelicibus A, Drummond RD, Valieris R, Yu-Tao Z, Silva IT da, Liang Z. WSI2ML: an open-source whole slide image annotation software for machine learning applications [Internet]. Proceedings. 2023 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1145/3617023.3617038

    • Vancouver

      Martins LV de C, Bueno AP, Defelicibus A, Drummond RD, Valieris R, Yu-Tao Z, Silva IT da, Liang Z. WSI2ML: an open-source whole slide image annotation software for machine learning applications [Internet]. Proceedings. 2023 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1145/3617023.3617038

  • Trabalho de evento

    Complex network-based data classification using minimum spanning tree metric and optimization (2023)

    Saire, Josimar Edinson Chire ; Liang, Zhao

    Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC

    Subjects: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL

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      SAIRE, Josimar Edinson Chire e LIANG, Zhao. Complex network-based data classification using minimum spanning tree metric and optimization. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/IJCNN54540.2023.10191004. Acesso em: 07 nov. 2024.

    • APA

      Saire, J. E. C., & Liang, Z. (2023). Complex network-based data classification using minimum spanning tree metric and optimization. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN54540.2023.10191004

    • NLM

      Saire JEC, Liang Z. Complex network-based data classification using minimum spanning tree metric and optimization [Internet]. Proceedings. 2023 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191004

    • Vancouver

      Saire JEC, Liang Z. Complex network-based data classification using minimum spanning tree metric and optimization [Internet]. Proceedings. 2023 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191004

  • Tese (Doutorado)

    Structure characterization of complex networks for machine learning (2023)

    Anghinoni, Leandro ; Liang, Zhao (Orientador) ; Silva, Israel T. (coorient)

    Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, REDES COMPLEXAS, REDES NEURAIS, ANÁLISE DE SÉRIES TEMPORAIS

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      ANGHINONI, Leandro. Structure characterization of complex networks for machine learning. 2023. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2023. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-13092023-143213/. Acesso em: 07 nov. 2024.

    • APA

      Anghinoni, L. (2023). Structure characterization of complex networks for machine learning (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-13092023-143213/

    • NLM

      Anghinoni L. Structure characterization of complex networks for machine learning [Internet]. 2023 ;[citado 2024 nov. 07 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-13092023-143213/

    • Vancouver

      Anghinoni L. Structure characterization of complex networks for machine learning [Internet]. 2023 ;[citado 2024 nov. 07 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-13092023-143213/

  • Tese (Doutorado)

    A Modified Echo State Network Model Using Non-Random Topology (2023)

    Arroyo, Diana Carolina Roca ; Liang, Zhao (Orientador) ; Silva, Israel T. (coorient)

    Unidade: ICMC

    Subjects: REDES NEURAIS, REDES COMPLEXAS, MINERAÇÃO DE DADOS, REGRESSÃO LINEAR

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      ARROYO, Diana Carolina Roca. A Modified Echo State Network Model Using Non-Random Topology. 2023. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2023. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-12092023-210015/. Acesso em: 07 nov. 2024.

    • APA

      Arroyo, D. C. R. (2023). A Modified Echo State Network Model Using Non-Random Topology (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-12092023-210015/

    • NLM

      Arroyo DCR. A Modified Echo State Network Model Using Non-Random Topology [Internet]. 2023 ;[citado 2024 nov. 07 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-12092023-210015/

    • Vancouver

      Arroyo DCR. A Modified Echo State Network Model Using Non-Random Topology [Internet]. 2023 ;[citado 2024 nov. 07 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-12092023-210015/

  • Tese (Doutorado)

    Classificação de Alto Nível Baseada em Redes Complexas (2023)

    Saire, Josimar Edinson Chire ; Liang, Zhao (Orientador)

    Unidade: ICMC

    Subjects: REDES COMPLEXAS, COLÔNIAS DE FORMIGAS, ALGORITMOS GENÉTICOS, APRENDIZADO COMPUTACIONAL, COVID-19

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      SAIRE, Josimar Edinson Chire. Classificação de Alto Nível Baseada em Redes Complexas. 2023. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2023. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-02022024-172326/. Acesso em: 07 nov. 2024.

    • APA

      Saire, J. E. C. (2023). Classificação de Alto Nível Baseada em Redes Complexas (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-02022024-172326/

    • NLM

      Saire JEC. Classificação de Alto Nível Baseada em Redes Complexas [Internet]. 2023 ;[citado 2024 nov. 07 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-02022024-172326/

    • Vancouver

      Saire JEC. Classificação de Alto Nível Baseada em Redes Complexas [Internet]. 2023 ;[citado 2024 nov. 07 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-02022024-172326/

  • Artigo de periodico

    Characterizing data patterns with core-periphery network modeling (2023)

    Jianglong, Yan; Anghinoni, Leandro ; Yu-Tao, Zhu; Weiguang, Liu; Gen, Li; Qiusheng, Zheng; Liang, Zhao

    Source: Journal of Computational Science. Unidades: FFCLRP, ICMC

    Subjects: REDES COMPLEXAS, RECONHECIMENTO DE IMAGEM, RADIOGRAFIA, COVID-19

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      JIANGLONG, Yan et al. Characterizing data patterns with core-periphery network modeling. Journal of Computational Science, v. 66, n. Ja 2023, p. 1-13, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.jocs.2022.101912. Acesso em: 07 nov. 2024.

    • APA

      Jianglong, Y., Anghinoni, L., Yu-Tao, Z., Weiguang, L., Gen, L., Qiusheng, Z., & Liang, Z. (2023). Characterizing data patterns with core-periphery network modeling. Journal of Computational Science, 66( Ja 2023), 1-13. doi:10.1016/j.jocs.2022.101912

    • NLM

      Jianglong Y, Anghinoni L, Yu-Tao Z, Weiguang L, Gen L, Qiusheng Z, Liang Z. Characterizing data patterns with core-periphery network modeling [Internet]. Journal of Computational Science. 2023 ; 66( Ja 2023): 1-13.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.jocs.2022.101912

    • Vancouver

      Jianglong Y, Anghinoni L, Yu-Tao Z, Weiguang L, Gen L, Qiusheng Z, Liang Z. Characterizing data patterns with core-periphery network modeling [Internet]. Journal of Computational Science. 2023 ; 66( Ja 2023): 1-13.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.jocs.2022.101912

  • Artigo de periodico

    Complex network-based classification of radiographic images for COVID-19 diagnosis (2023)

    Weiguang, Liu; Rodrigues, Rafael Delalibera ; Jianglong, Yan; Yu-Tao, Zhu; Pereira, Everson José de Freitas; Gen, Li; Qiusheng, Zheng; Liang, Zhao

    Source: PLoS ONE. Unidades: FFCLRP, ICMC

    Subjects: REDES COMPLEXAS, RECONHECIMENTO DE IMAGEM, DIAGNÓSTICO POR COMPUTADOR, TECNOLOGIAS DA SAÚDE, RADIOGRAFIA, COVID-19

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      WEIGUANG, Liu et al. Complex network-based classification of radiographic images for COVID-19 diagnosis. PLoS ONE, v. 18, n. 9, p. 1-26, 2023Tradução . . Disponível em: https://doi.org/10.1371/journal.pone.0290968. Acesso em: 07 nov. 2024.

    • APA

      Weiguang, L., Rodrigues, R. D., Jianglong, Y., Yu-Tao, Z., Pereira, E. J. de F., Gen, L., et al. (2023). Complex network-based classification of radiographic images for COVID-19 diagnosis. PLoS ONE, 18( 9), 1-26. doi:10.1371/ journal.pone.0290968

    • NLM

      Weiguang L, Rodrigues RD, Jianglong Y, Yu-Tao Z, Pereira EJ de F, Gen L, Qiusheng Z, Liang Z. Complex network-based classification of radiographic images for COVID-19 diagnosis [Internet]. PLoS ONE. 2023 ; 18( 9): 1-26.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1371/journal.pone.0290968

    • Vancouver

      Weiguang L, Rodrigues RD, Jianglong Y, Yu-Tao Z, Pereira EJ de F, Gen L, Qiusheng Z, Liang Z. Complex network-based classification of radiographic images for COVID-19 diagnosis [Internet]. PLoS ONE. 2023 ; 18( 9): 1-26.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1371/journal.pone.0290968

  • Artigo de periodico

    Multilevel coarsening for interactive visualization of large bipartite networks (2022)

    Valejo, Alan Demetrius Baria ; Fabbri, Renato ; Lopes, Alneu de Andrade ; Liang, Zhao; Oliveira, Maria Cristina Ferreira de

    Source: Frontiers in Research Metrics and Analytics. Unidades: ICMC, FFCLRP

    Subjects: REDES COMPLEXAS, TEORIA DOS GRAFOS, VISUALIZAÇÃO

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      VALEJO, Alan Demetrius Baria et al. Multilevel coarsening for interactive visualization of large bipartite networks. Frontiers in Research Metrics and Analytics, v. 7, p. 1-18, 2022Tradução . . Disponível em: https://doi.org/10.3389/frma.2022.855165. Acesso em: 07 nov. 2024.

    • APA

      Valejo, A. D. B., Fabbri, R., Lopes, A. de A., Liang, Z., & Oliveira, M. C. F. de. (2022). Multilevel coarsening for interactive visualization of large bipartite networks. Frontiers in Research Metrics and Analytics, 7, 1-18. doi:10.3389/frma.2022.855165

    • NLM

      Valejo ADB, Fabbri R, Lopes A de A, Liang Z, Oliveira MCF de. Multilevel coarsening for interactive visualization of large bipartite networks [Internet]. Frontiers in Research Metrics and Analytics. 2022 ; 7 1-18.[citado 2024 nov. 07 ] Available from: https://doi.org/10.3389/frma.2022.855165

    • Vancouver

      Valejo ADB, Fabbri R, Lopes A de A, Liang Z, Oliveira MCF de. Multilevel coarsening for interactive visualization of large bipartite networks [Internet]. Frontiers in Research Metrics and Analytics. 2022 ; 7 1-18.[citado 2024 nov. 07 ] Available from: https://doi.org/10.3389/frma.2022.855165

  • Artigo de periodico

    Network community detection via iterative edge removal in a flocking-like system (2021)

    Verri, Filipe Alves Neto ; Gueleri, Roberto Alves ; Qiusheng, Zheng; Junbao, Zhang; Liang, Zhao

    Source: European Physical Journal - Special Topics. Unidades: FFCLRP, ICMC

    Subjects: REDES COMPLEXAS, SISTEMAS DINÂMICOS, ALGORITMOS ÚTEIS E ESPECÍFICOS

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      VERRI, Filipe Alves Neto et al. Network community detection via iterative edge removal in a flocking-like system. European Physical Journal - Special Topics, v. 230, n. 14-15, p. 2843-2855, 2021Tradução . . Disponível em: https://doi.org/10.1140/epjs/s11734-021-00154-5. Acesso em: 07 nov. 2024.

    • APA

      Verri, F. A. N., Gueleri, R. A., Qiusheng, Z., Junbao, Z., & Liang, Z. (2021). Network community detection via iterative edge removal in a flocking-like system. European Physical Journal - Special Topics, 230( 14-15), 2843-2855. doi:10.1140/epjs/s11734-021-00154-5

    • NLM

      Verri FAN, Gueleri RA, Qiusheng Z, Junbao Z, Liang Z. Network community detection via iterative edge removal in a flocking-like system [Internet]. European Physical Journal - Special Topics. 2021 ; 230( 14-15): 2843-2855.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00154-5

    • Vancouver

      Verri FAN, Gueleri RA, Qiusheng Z, Junbao Z, Liang Z. Network community detection via iterative edge removal in a flocking-like system [Internet]. European Physical Journal - Special Topics. 2021 ; 230( 14-15): 2843-2855.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00154-5

  • Artigo de periodico

    Time series pattern identification by hierarchical community detection (2021)

    Anghinoni, Leandro ; Vega-Oliveros, Didier Augusto ; Silva, Thiago Christiano ; Liang, Zhao

    Source: European Physical Journal - Special Topics. Unidades: FFCLRP, ICMC

    Subjects: REDES COMPLEXAS, ANÁLISE DE SÉRIES TEMPORAIS, RECONHECIMENTO DE PADRÕES

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      ANGHINONI, Leandro et al. Time series pattern identification by hierarchical community detection. European Physical Journal - Special Topics, v. 230, n. 14-15, p. 2775-2782, 2021Tradução . . Disponível em: https://doi.org/10.1140/epjs/s11734-021-00163-4. Acesso em: 07 nov. 2024.

    • APA

      Anghinoni, L., Vega-Oliveros, D. A., Silva, T. C., & Liang, Z. (2021). Time series pattern identification by hierarchical community detection. European Physical Journal - Special Topics, 230( 14-15), 2775-2782. doi:10.1140/epjs/s11734-021-00163-4

    • NLM

      Anghinoni L, Vega-Oliveros DA, Silva TC, Liang Z. Time series pattern identification by hierarchical community detection [Internet]. European Physical Journal - Special Topics. 2021 ; 230( 14-15): 2775-2782.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00163-4

    • Vancouver

      Anghinoni L, Vega-Oliveros DA, Silva TC, Liang Z. Time series pattern identification by hierarchical community detection [Internet]. European Physical Journal - Special Topics. 2021 ; 230( 14-15): 2775-2782.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00163-4

  • Artigo de periodico

    Stock market trend detection and automatic decision-making through a network-based classification model (2021)

    Colliri, Tiago Santos ; Liang, Zhao

    Source: Natural Computing. Unidades: FFCLRP, ICMC

    Subjects: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, BOLSA DE VALORES

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      COLLIRI, Tiago Santos e LIANG, Zhao. Stock market trend detection and automatic decision-making through a network-based classification model. Natural Computing, v. 20, n. 4, p. 791-804, 2021Tradução . . Disponível em: https://doi.org/10.1007/s11047-020-09829-9. Acesso em: 07 nov. 2024.

    • APA

      Colliri, T. S., & Liang, Z. (2021). Stock market trend detection and automatic decision-making through a network-based classification model. Natural Computing, 20( 4), 791-804. doi:10.1007/s11047-020-09829-9

    • NLM

      Colliri TS, Liang Z. Stock market trend detection and automatic decision-making through a network-based classification model [Internet]. Natural Computing. 2021 ; 20( 4): 791-804.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1007/s11047-020-09829-9

    • Vancouver

      Colliri TS, Liang Z. Stock market trend detection and automatic decision-making through a network-based classification model [Internet]. Natural Computing. 2021 ; 20( 4): 791-804.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1007/s11047-020-09829-9

  • Tese (Doutorado)

    Network-based high level classification: novel models and applications (2021)

    Colliri, Tiago Santos; Liang, Zhao (Orientador)

    Unidade: ICMC

    Subjects: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL, COVID-19

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      COLLIRI, Tiago Santos. Network-based high level classification: novel models and applications. 2021. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2021. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-26032021-102400/. Acesso em: 07 nov. 2024.

    • APA

      Colliri, T. S. (2021). Network-based high level classification: novel models and applications (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-26032021-102400/

    • NLM

      Colliri TS. Network-based high level classification: novel models and applications [Internet]. 2021 ;[citado 2024 nov. 07 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-26032021-102400/

    • Vancouver

      Colliri TS. Network-based high level classification: novel models and applications [Internet]. 2021 ;[citado 2024 nov. 07 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-26032021-102400/

  • Trabalho de evento

    An optimized modularity-based high level classification model (2020)

    Colliri, Tiago Santos ; Weiguang, Liu; Liang, Zhao

    Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC

    Subjects: REDES COMPLEXAS, RECONHECIMENTO DE PADRÕES

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      COLLIRI, Tiago Santos e WEIGUANG, Liu e LIANG, Zhao. An optimized modularity-based high level classification model. 2020, Anais.. Piscataway: IEEE, 2020. Disponível em: https://doi.org/10.1109/IJCNN48605.2020.9206755. Acesso em: 07 nov. 2024.

    • APA

      Colliri, T. S., Weiguang, L., & Liang, Z. (2020). An optimized modularity-based high level classification model. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN48605.2020.9206755

    • NLM

      Colliri TS, Weiguang L, Liang Z. An optimized modularity-based high level classification model [Internet]. Proceedings. 2020 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9206755

    • Vancouver

      Colliri TS, Weiguang L, Liang Z. An optimized modularity-based high level classification model [Internet]. Proceedings. 2020 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9206755

  • Artigo de periodico

    A tourist walk approach for internal and external outlier detection (2020)

    Rodrigues, Rafael Delalibera ; Liang, Zhao ; Zheng, Qiusheng; Zhang, Junbao

    Source: Neurocomputing. Unidades: FFCLRP, ICMC

    Subjects: TURISMO, MEMÓRIA (ELETRÔNICA DIGITAL), ATRATORES

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      RODRIGUES, Rafael Delalibera et al. A tourist walk approach for internal and external outlier detection. Neurocomputing, v. 393, p. 203-213, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2018.10.113. Acesso em: 07 nov. 2024.

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      Rodrigues, R. D., Liang, Z., Zheng, Q., & Zhang, J. (2020). A tourist walk approach for internal and external outlier detection. Neurocomputing, 393, 203-213. doi:10.1016/j.neucom.2018.10.113

    • NLM

      Rodrigues RD, Liang Z, Zheng Q, Zhang J. A tourist walk approach for internal and external outlier detection [Internet]. Neurocomputing. 2020 ; 393 203-213.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.neucom.2018.10.113

    • Vancouver

      Rodrigues RD, Liang Z, Zheng Q, Zhang J. A tourist walk approach for internal and external outlier detection [Internet]. Neurocomputing. 2020 ; 393 203-213.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.neucom.2018.10.113

  • Dissertação (Mestrado)

    A Two-Stage Particle Competition Model for Unbalanced Community Detection in Complex Networks (2020)

    Martins, Luan Vinicius de Carvalho; Liang, Zhao (Orientador)

    Unidade: ICMC

    Subjects: REDES COMPLEXAS, DINÂMICA ESTOCÁSTICA, COMPUTAÇÃO BIOINSPIRADA

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      MARTINS, Luan Vinicius de Carvalho. A Two-Stage Particle Competition Model for Unbalanced Community Detection in Complex Networks. 2020. Dissertação (Mestrado) – Universidade de São Paulo, São Carlos, 2020. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-20082020-101929/. Acesso em: 07 nov. 2024.

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      Martins, L. V. de C. (2020). A Two-Stage Particle Competition Model for Unbalanced Community Detection in Complex Networks (Dissertação (Mestrado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-20082020-101929/

    • NLM

      Martins LV de C. A Two-Stage Particle Competition Model for Unbalanced Community Detection in Complex Networks [Internet]. 2020 ;[citado 2024 nov. 07 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-20082020-101929/

    • Vancouver

      Martins LV de C. A Two-Stage Particle Competition Model for Unbalanced Community Detection in Complex Networks [Internet]. 2020 ;[citado 2024 nov. 07 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-20082020-101929/

  • Trabalho de evento-anais periodico

    Particle competition for unbalanced community detection in complex networks (2020)

    Martins, Luan Vinicius Carvalho; Liang, Zhao

    Source: Lecture Notes in Artificial Intelligence. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: FFCLRP, ICMC

    Assunto: REDES COMPLEXAS

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      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: 07 nov. 2024. , 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 2024 nov. 07 ] 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 2024 nov. 07 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_22

  • Artigo de periodico

    Temporal network pattern identification by community modelling (2020)

    Gao, Xubo; Zheng, Qiusheng; Vega-Oliveros, Didier Augusto ; Anghinoni, Leandro ; Liang, Zhao

    Source: Scientific Reports. Unidades: FFCLRP, ICMC

    Subjects: MINERAÇÃO DE DADOS, ANÁLISE DE SÉRIES TEMPORAIS, RECONHECIMENTO DE PADRÕES

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      GAO, Xubo et al. Temporal network pattern identification by community modelling. Scientific Reports, v. 10, p. 1-12, 2020Tradução . . Disponível em: https://doi.org/10.1038/s41598-019-57123-1. Acesso em: 07 nov. 2024.

    • APA

      Gao, X., Zheng, Q., Vega-Oliveros, D. A., Anghinoni, L., & Liang, Z. (2020). Temporal network pattern identification by community modelling. Scientific Reports, 10, 1-12. doi:10.1038/s41598-019-57123-1

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      Gao X, Zheng Q, Vega-Oliveros DA, Anghinoni L, Liang Z. Temporal network pattern identification by community modelling [Internet]. Scientific Reports. 2020 ; 10 1-12.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1038/s41598-019-57123-1

    • Vancouver

      Gao X, Zheng Q, Vega-Oliveros DA, Anghinoni L, Liang Z. Temporal network pattern identification by community modelling [Internet]. Scientific Reports. 2020 ; 10 1-12.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1038/s41598-019-57123-1

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