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Trabalho de evento-anais periodico
Modeling EEG data into graphs for the prognostic of patients in coma using graph neural networks (2025)
Nascimento Junior, Odelmo O.; Assis, Dhara L. C.; Destro Filho, João Batista; Liang, Zhao ; Carneiro, Murillo Guimarães
Fonte: Lecture Notes in Artificial Intelligence - Intelligent Systems. Nome do evento: Brazilian Conference on Intelligent Systems, BRACIS. Unidade: FFCLRP
Assuntos: REDE NERVOSA, COMA, ELETROENCEFALOGRAFIA, MONITORAMENTO, CÉREBRO
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ABNT
NASCIMENTO JUNIOR, Odelmo O. et al. Modeling EEG data into graphs for the prognostic of patients in coma using graph neural networks. Lecture Notes in Artificial Intelligence - Intelligent Systems. Cham: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/978-3-031-79038-6_16. Acesso em: 09 nov. 2025. , 2025
APA
Nascimento Junior, O. O., Assis, D. L. C., Destro Filho, J. B., Liang, Z., & Carneiro, M. G. (2025). Modeling EEG data into graphs for the prognostic of patients in coma using graph neural networks. Lecture Notes in Artificial Intelligence - Intelligent Systems. Cham: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.1007/978-3-031-79038-6_16
NLM
Nascimento Junior OO, Assis DLC, Destro Filho JB, Liang Z, Carneiro MG. Modeling EEG data into graphs for the prognostic of patients in coma using graph neural networks [Internet]. Lecture Notes in Artificial Intelligence - Intelligent Systems. 2025 ; 15415 223-237.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1007/978-3-031-79038-6_16
Vancouver
Nascimento Junior OO, Assis DLC, Destro Filho JB, Liang Z, Carneiro MG. Modeling EEG data into graphs for the prognostic of patients in coma using graph neural networks [Internet]. Lecture Notes in Artificial Intelligence - Intelligent Systems. 2025 ; 15415 223-237.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1007/978-3-031-79038-6_16
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
Fonte: IEEE Transactions on Network Science and Engineering. Unidades: FFCLRP, ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, DESCOBERTA DE CONHECIMENTO, ALGORITMOS ÚTEIS E ESPECÍFICOS, REDES COMPLEXAS
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ABNT
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: 09 nov. 2025.
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 2025 nov. 09 ] 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 2025 nov. 09 ] 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
Fonte: Proceedings. Nome do evento: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC
Assuntos: REDES NEURAIS, TEORIA DOS GRAFOS
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ABNT
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: 09 nov. 2025.
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 2025 nov. 09 ] 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 2025 nov. 09 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650514
Artigo de periodico
TKDP: threefold knowledge-enriched deep prompt tuning for few-shot named entity recognition (2024)
Liu, Jiang; Fei, Hao; Li, Fei; Li, Jingye; Li, Bobo; Liang, Zhao ; Teng, Chong; Ji, Donghong
Fonte: IEEE Transactions on Knowledge and Data Engineering. Unidade: FFCLRP
Assuntos: CIÊNCIA DA COMPUTAÇÃO, PROCESSAMENTO DE LINGUAGEM NATURAL, LINGUAGEM DE MÁQUINA
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ABNT
LIU, Jiang et al. TKDP: threefold knowledge-enriched deep prompt tuning for few-shot named entity recognition. IEEE Transactions on Knowledge and Data Engineering, v. 36, n. 11, p. 6397-6409, 2024Tradução . . Disponível em: https://doi.org/10.1109/TKDE.2024.3389650. Acesso em: 09 nov. 2025.
APA
Liu, J., Fei, H., Li, F., Li, J., Li, B., Liang, Z., et al. (2024). TKDP: threefold knowledge-enriched deep prompt tuning for few-shot named entity recognition. IEEE Transactions on Knowledge and Data Engineering, 36( 11), 6397-6409. doi:10.1109/TKDE.2024.3389650
NLM
Liu J, Fei H, Li F, Li J, Li B, Liang Z, Teng C, Ji D. TKDP: threefold knowledge-enriched deep prompt tuning for few-shot named entity recognition [Internet]. IEEE Transactions on Knowledge and Data Engineering. 2024 ; 36( 11): 6397-6409.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1109/TKDE.2024.3389650
Vancouver
Liu J, Fei H, Li F, Li J, Li B, Liang Z, Teng C, Ji D. TKDP: threefold knowledge-enriched deep prompt tuning for few-shot named entity recognition [Internet]. IEEE Transactions on Knowledge and Data Engineering. 2024 ; 36( 11): 6397-6409.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1109/TKDE.2024.3389650
Trabalho de evento
TransGNN: a transductive graph neural network with graph dynamic embedding (2023)
Anghinoni, Leandro ; Yu-Tao, Zhu; Donghong, Ji; Liang, Zhao
Fonte: Proceedings. Nome do evento: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, REDES NEURAIS
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ABNT
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: 09 nov. 2025.
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 2025 nov. 09 ] 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 2025 nov. 09 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191134
Trabalho de evento
Complex network-based data classification using minimum spanning tree metric and optimization (2023)
Saire, Josimar Edinson Chire ; Liang, Zhao
Fonte: Proceedings. Nome do evento: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC
Assuntos: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL
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ABNT
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: 09 nov. 2025.
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 2025 nov. 09 ] 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 2025 nov. 09 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191004
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
Fonte: Proceedings. Nome do evento: Brazilian Symposium on Multimedia and the Web - WebMedia. Unidades: FFCLRP, ICMC
Assuntos: BIOINFORMÁTICA, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, PATOLOGIA CLÍNICA, TECNOLOGIAS DA SAÚDE
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ABNT
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: 09 nov. 2025.
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 2025 nov. 09 ] 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 2025 nov. 09 ] Available from: https://doi.org/10.1145/3617023.3617038
Trabalho de evento
High-level classification for EEG analysis (2023)
Carneiro, Murillo Guimarães; Ramos, Camila D.; Destro Filho, João Batista; Zhu, Yu-tao; Ji, Donghong; Liang, Zhao
Nome do evento: International Joint Conference on Neural Networks. Unidade: FFCLRP
Assuntos: REDES COMPLEXAS, SISTEMA BINÁRIO, ELETROENCEFALOGRAFIA, PROGNÓSTICO, COMA
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ABNT
CARNEIRO, Murillo Guimarães et al. High-level classification for EEG analysis. 2023, Anais.. Queensland: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, 2023. p. 1-8. Disponível em: https://doi.org/10.1109/IJCNN54540.2023.10191823. Acesso em: 09 nov. 2025.
APA
Carneiro, M. G., Ramos, C. D., Destro Filho, J. B., Zhu, Y. -tao, Ji, D., & Liang, Z. (2023). High-level classification for EEG analysis. In (p. 1-8). Queensland: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.1109/IJCNN54540.2023.10191823
NLM
Carneiro MG, Ramos CD, Destro Filho JB, Zhu Y-tao, Ji D, Liang Z. High-level classification for EEG analysis [Internet]. 2023 ; 1-8.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191823
Vancouver
Carneiro MG, Ramos CD, Destro Filho JB, Zhu Y-tao, Ji D, Liang Z. High-level classification for EEG analysis [Internet]. 2023 ; 1-8.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191823
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
Fonte: PLoS ONE. Unidades: FFCLRP, ICMC
Assuntos: REDES COMPLEXAS, RECONHECIMENTO DE IMAGEM, DIAGNÓSTICO POR COMPUTADOR, TECNOLOGIAS DA SAÚDE, RADIOGRAFIA, COVID-19
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ABNT
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: 09 nov. 2025.
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 2025 nov. 09 ] 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 2025 nov. 09 ] Available from: https://doi.org/10.1371/journal.pone.0290968
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
Fonte: Journal of Computational Science. Unidades: FFCLRP, ICMC
Assuntos: REDES COMPLEXAS, RECONHECIMENTO DE IMAGEM, RADIOGRAFIA, COVID-19
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ABNT
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: 09 nov. 2025.
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 2025 nov. 09 ] 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 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.jocs.2022.101912
Artigo de periodico
OneEE: a one-stage framework for fast overlapping and nested event extraction (2022)
Cao, Hu; Li, Jingye; Su, Fangfang; Li, Fei; Fei, Hao; Wu, Shengqiong; Li, Bobo; Liang, Zhao; Ji, Donghong
Fonte: arXiv. Unidade: FFCLRP
Assuntos: PROCESSAMENTO DE LINGUAGEM NATURAL, RECONHECIMENTO DE PADRÕES, REPRESENTAÇÃO ORDINÁRIA
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ABNT
CAO, Hu et al. OneEE: a one-stage framework for fast overlapping and nested event extraction. arXiv, p. 1-13, 2022Tradução . . Disponível em: https://doi.org/10.48550/arXiv.2209.02693. Acesso em: 09 nov. 2025.
APA
Cao, H., Li, J., Su, F., Li, F., Fei, H., Wu, S., et al. (2022). OneEE: a one-stage framework for fast overlapping and nested event extraction. arXiv, 1-13. doi:10.48550/arXiv.2209.02693
NLM
Cao H, Li J, Su F, Li F, Fei H, Wu S, Li B, Liang Z, Ji D. OneEE: a one-stage framework for fast overlapping and nested event extraction [Internet]. arXiv. 2022 ; 1-13.[citado 2025 nov. 09 ] Available from: https://doi.org/10.48550/arXiv.2209.02693
Vancouver
Cao H, Li J, Su F, Li F, Fei H, Wu S, Li B, Liang Z, Ji D. OneEE: a one-stage framework for fast overlapping and nested event extraction [Internet]. arXiv. 2022 ; 1-13.[citado 2025 nov. 09 ] Available from: https://doi.org/10.48550/arXiv.2209.02693
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
Fonte: Frontiers in Research Metrics and Analytics. Unidades: ICMC, FFCLRP
Assuntos: REDES COMPLEXAS, TEORIA DOS GRAFOS, VISUALIZAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
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: 09 nov. 2025.
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 2025 nov. 09 ] 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 2025 nov. 09 ] Available from: https://doi.org/10.3389/frma.2022.855165
Parte de monografia/livro
Predicting corruption convictions among brazilian representatives through a voting-history based network (2021)
Colliri, Tiago Santos ; Liang, Zhao
Fonte: Corruption networks : concepts and applications. Unidades: FFCLRP, ICMC
Assuntos: REDES COMPLEXAS, CORRUPÇÃO, ELEIÇÃO PARLAMENTAR, CONDENAÇÃO CRIMINAL, BRASIL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COLLIRI, Tiago Santos e LIANG, Zhao. Predicting corruption convictions among brazilian representatives through a voting-history based network. Corruption networks : concepts and applications. Tradução . Cham: Springer Nature, 2021. . Disponível em: https://doi.org/10.1007/978-3-030-81484-7_4. Acesso em: 09 nov. 2025.
APA
Colliri, T. S., & Liang, Z. (2021). Predicting corruption convictions among brazilian representatives through a voting-history based network. In Corruption networks : concepts and applications. Cham: Springer Nature. doi:10.1007/978-3-030-81484-7_4
NLM
Colliri TS, Liang Z. Predicting corruption convictions among brazilian representatives through a voting-history based network [Internet]. In: Corruption networks : concepts and applications. Cham: Springer Nature; 2021. [citado 2025 nov. 09 ] Available from: https://doi.org/10.1007/978-3-030-81484-7_4
Vancouver
Colliri TS, Liang Z. Predicting corruption convictions among brazilian representatives through a voting-history based network [Internet]. In: Corruption networks : concepts and applications. Cham: Springer Nature; 2021. [citado 2025 nov. 09 ] Available from: https://doi.org/10.1007/978-3-030-81484-7_4

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