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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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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 maio 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 maio 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 2024 maio 09 ] Available from: https://doi.org/10.1109/TNSE.2023.3331655
Artigo de periodico
TOE:: a grid-tagging discontinuous ner model enhanced by embedding tag/word relations and more fine-grained tags (2023)
Liu, Jiang; Ji, Donghong; Li, Jingye; Xie, Dongdong; Teng, Chong; Zhao, Liang; Li, Fei
Source: IEEE/ACM Transactions on Audio, Speech, and Language Processing. Unidade: FFCLRP
Subjects: DESIGNS (CONFIGURAÇÕES COMBINATÓRIAS), ARQUITETURA E ORGANIZAÇÃO DE COMPUTADORES, PROCESSAMENTO DE LINGUAGEM NATURAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LIU, Jiang et al. TOE:: a grid-tagging discontinuous ner model enhanced by embedding tag/word relations and more fine-grained tags. IEEE/ACM Transactions on Audio, Speech, and Language Processing, v. 31, p. 177-187, 2023Tradução . . Disponível em: https://doi.org/10.1109/TASLP.2022.3221009. Acesso em: 09 maio 2024.
APA
Liu, J., Ji, D., Li, J., Xie, D., Teng, C., Zhao, L., & Li, F. (2023). TOE:: a grid-tagging discontinuous ner model enhanced by embedding tag/word relations and more fine-grained tags. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 177-187. doi:10.1109/TASLP.2022.3221009
NLM
Liu J, Ji D, Li J, Xie D, Teng C, Zhao L, Li F. TOE:: a grid-tagging discontinuous ner model enhanced by embedding tag/word relations and more fine-grained tags [Internet]. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2023 ; 31 177-187.[citado 2024 maio 09 ] Available from: https://doi.org/10.1109/TASLP.2022.3221009
Vancouver
Liu J, Ji D, Li J, Xie D, Teng C, Zhao L, Li F. TOE:: a grid-tagging discontinuous ner model enhanced by embedding tag/word relations and more fine-grained tags [Internet]. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2023 ; 31 177-187.[citado 2024 maio 09 ] Available from: https://doi.org/10.1109/TASLP.2022.3221009
Artigo de periodico
Clustered and deep echo state networks for signal noise reduction (2022)
Oliveira Junior, Laercio de; Stelzer, Florian; Liang, Zhao
Source: Machine Learning. Unidade: FFCLRP
Subjects: REDES COMPLEXAS, REDES NEURAIS, SISTEMAS DINÂMICOS
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ABNT
OLIVEIRA JUNIOR, Laercio de e STELZER, Florian e LIANG, Zhao. Clustered and deep echo state networks for signal noise reduction. Machine Learning, v. 111, n. 8, p. 2885-2904, 2022Tradução . . Disponível em: https://doi.org/10.1007/s10994-022-06135-6. Acesso em: 09 maio 2024.
APA
Oliveira Junior, L. de, Stelzer, F., & Liang, Z. (2022). Clustered and deep echo state networks for signal noise reduction. Machine Learning, 111( 8), 2885-2904. doi:10.1007/s10994-022-06135-6
NLM
Oliveira Junior L de, Stelzer F, Liang Z. Clustered and deep echo state networks for signal noise reduction [Internet]. Machine Learning. 2022 ; 111( 8): 2885-2904.[citado 2024 maio 09 ] Available from: https://doi.org/10.1007/s10994-022-06135-6
Vancouver
Oliveira Junior L de, Stelzer F, Liang Z. Clustered and deep echo state networks for signal noise reduction [Internet]. Machine Learning. 2022 ; 111( 8): 2885-2904.[citado 2024 maio 09 ] Available from: https://doi.org/10.1007/s10994-022-06135-6
Trabalho de evento-anais periodico
Classification of dispersed patterns of radiographic images with COVID-19 by core-periphery network modeling (2021)
Yan, Jianglong; Liu, Weiguang; Zhu, Yu-tao; Li, Gen; Zheng, Qiusheng; Liang, Zhao
Source: Complex Networks & Their Applications X. Conference titles: International Conference on Complex Networks and Their Applications. Unidade: FFCLRP
Subjects: COVID-19, RAIOS X, IMAGEM DIGITAL, DADOS CATEGORIZADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
YAN, Jianglong et al. Classification of dispersed patterns of radiographic images with COVID-19 by core-periphery network modeling. Complex Networks & Their Applications X. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-93409-5_4. Acesso em: 09 maio 2024. , 2021
APA
Yan, J., Liu, W., Zhu, Y. -tao, Li, G., Zheng, Q., & Liang, Z. (2021). Classification of dispersed patterns of radiographic images with COVID-19 by core-periphery network modeling. Complex Networks & Their Applications X. Cham: Springer. doi:10.1007/978-3-030-93409-5_4
NLM
Yan J, Liu W, Zhu Y-tao, Li G, Zheng Q, Liang Z. Classification of dispersed patterns of radiographic images with COVID-19 by core-periphery network modeling [Internet]. Complex Networks & Their Applications X. 2021 ; 1 40-49.[citado 2024 maio 09 ] Available from: https://doi.org/10.1007/978-3-030-93409-5_4
Vancouver
Yan J, Liu W, Zhu Y-tao, Li G, Zheng Q, Liang Z. Classification of dispersed patterns of radiographic images with COVID-19 by core-periphery network modeling [Internet]. Complex Networks & Their Applications X. 2021 ; 1 40-49.[citado 2024 maio 09 ] Available from: https://doi.org/10.1007/978-3-030-93409-5_4
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
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: 09 maio 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 maio 09 ] 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 maio 09 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00154-5
Artigo de periodico
A review and comparative analysis of coarsening algorithms on bipartite networks (2021)
Valejo, Alan Demetrius Baria ; Santos, Wellington de Oliveira dos; Naldi, Murilo Coelho ; Liang, Zhao
Source: The European Physical Journal Special Topics. Unidade: FFCLRP
Subjects: ALGORITMOS, APRENDIZADO COMPUTACIONAL, REDES COMPLEXAS
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ABNT
VALEJO, Alan Demetrius Baria et al. A review and comparative analysis of coarsening algorithms on bipartite networks. The European Physical Journal Special Topics, v. 230, n. 14-15, p. 2801 - 2811, 2021Tradução . . Disponível em: https://doi.org/10.1140/epjs/s11734-021-00159-0. Acesso em: 09 maio 2024.
APA
Valejo, A. D. B., Santos, W. de O. dos, Naldi, M. C., & Liang, Z. (2021). A review and comparative analysis of coarsening algorithms on bipartite networks. The European Physical Journal Special Topics, 230( 14-15), 2801 - 2811. doi:10.1140/epjs/s11734-021-00159-0
NLM
Valejo ADB, Santos W de O dos, Naldi MC, Liang Z. A review and comparative analysis of coarsening algorithms on bipartite networks [Internet]. The European Physical Journal Special Topics. 2021 ; 230( 14-15): 2801 - 2811.[citado 2024 maio 09 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00159-0
Vancouver
Valejo ADB, Santos W de O dos, Naldi MC, Liang Z. A review and comparative analysis of coarsening algorithms on bipartite networks [Internet]. The European Physical Journal Special Topics. 2021 ; 230( 14-15): 2801 - 2811.[citado 2024 maio 09 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00159-0
Artigo de periodico
Detecting early signs of insufficiency in COVID-19 patients from CBC tests through a supervised learning approach (2021)
Colliri, Tiago; Minakawa, Marcia Michie; Liang, Zhao
Source: Lecture Notes in Computer Science. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: FFCLRP, FSP
Subjects: REDES COMPLEXAS, COVID-19, PROGNÓSTICO, BIOMARCADORES, CONTAGEM DE CÉLULAS SANGUÍNEAS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COLLIRI, Tiago e MINAKAWA, Marcia Michie e LIANG, Zhao. Detecting early signs of insufficiency in COVID-19 patients from CBC tests through a supervised learning approach. Lecture Notes in Computer Science, v. 13074, p. 42-57, 2021Tradução . . Disponível em: https://doi.org/10.1007/978-3-030-91699-2_4. Acesso em: 09 maio 2024.
APA
Colliri, T., Minakawa, M. M., & Liang, Z. (2021). Detecting early signs of insufficiency in COVID-19 patients from CBC tests through a supervised learning approach. Lecture Notes in Computer Science, 13074, 42-57. doi:10.1007/978-3-030-91699-2_4
NLM
Colliri T, Minakawa MM, Liang Z. Detecting early signs of insufficiency in COVID-19 patients from CBC tests through a supervised learning approach [Internet]. Lecture Notes in Computer Science. 2021 ; 13074 42-57.[citado 2024 maio 09 ] Available from: https://doi.org/10.1007/978-3-030-91699-2_4
Vancouver
Colliri T, Minakawa MM, Liang Z. Detecting early signs of insufficiency in COVID-19 patients from CBC tests through a supervised learning approach [Internet]. Lecture Notes in Computer Science. 2021 ; 13074 42-57.[citado 2024 maio 09 ] Available from: https://doi.org/10.1007/978-3-030-91699-2_4
Artigo de periodico
Coarsening algorithm via semi-synchronous label propagation for bipartite networks (2021)
Valejo, Alan Demétrius Baria; Althoff, Paulo Eduardo; Faleiros, Thiago de Paulo; Chuerubim, Maria Lígia; Yan, Jianglong; Liu, Weiguang; Liang, Zhao
Source: Lecture Notes in Computer Science. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: FFCLRP
Subjects: REDES COMPLEXAS, ANÁLISE MULTINÍVEL, ESTATÍSTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VALEJO, Alan Demétrius Baria et al. Coarsening algorithm via semi-synchronous label propagation for bipartite networks. Lecture Notes in Computer Science, v. 13073, p. 437-452, 2021Tradução . . Disponível em: https://doi.org/10.1007/978-3-030-91702-9_29. Acesso em: 09 maio 2024.
APA
Valejo, A. D. B., Althoff, P. E., Faleiros, T. de P., Chuerubim, M. L., Yan, J., Liu, W., & Liang, Z. (2021). Coarsening algorithm via semi-synchronous label propagation for bipartite networks. Lecture Notes in Computer Science, 13073, 437-452. doi:10.1007/978-3-030-91702-9_29
NLM
Valejo ADB, Althoff PE, Faleiros T de P, Chuerubim ML, Yan J, Liu W, Liang Z. Coarsening algorithm via semi-synchronous label propagation for bipartite networks [Internet]. Lecture Notes in Computer Science. 2021 ; 13073 437-452.[citado 2024 maio 09 ] Available from: https://doi.org/10.1007/978-3-030-91702-9_29
Vancouver
Valejo ADB, Althoff PE, Faleiros T de P, Chuerubim ML, Yan J, Liu W, Liang Z. Coarsening algorithm via semi-synchronous label propagation for bipartite networks [Internet]. Lecture Notes in Computer Science. 2021 ; 13073 437-452.[citado 2024 maio 09 ] Available from: https://doi.org/10.1007/978-3-030-91702-9_29
Artigo de periodico
Anomaly detection in Brazilian Federal Government purchase cards through unsupervised learning techniques (2021)
Nunes, Breno; Colliri, Tiago; Lauretto, Marcelo de Souza ; Liu, Weiguang; Liang, Zhao
Source: Lecture Notes in Computer Science. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: EACH, FFCLRP
Subjects: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL, CARTÃO MAGNÉTICO, PADRÕES DE SOFTWARE
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ABNT
NUNES, Breno et al. Anomaly detection in Brazilian Federal Government purchase cards through unsupervised learning techniques. Lecture Notes in Computer Science, v. 13074, p. 19-32, 2021Tradução . . Disponível em: https://doi.org/10.1007/978-3-030-91699-2_2. Acesso em: 09 maio 2024.
APA
Nunes, B., Colliri, T., Lauretto, M. de S., Liu, W., & Liang, Z. (2021). Anomaly detection in Brazilian Federal Government purchase cards through unsupervised learning techniques. Lecture Notes in Computer Science, 13074, 19-32. doi:10.1007/978-3-030-91699-2_2
NLM
Nunes B, Colliri T, Lauretto M de S, Liu W, Liang Z. Anomaly detection in Brazilian Federal Government purchase cards through unsupervised learning techniques [Internet]. Lecture Notes in Computer Science. 2021 ; 13074 19-32.[citado 2024 maio 09 ] Available from: https://doi.org/10.1007/978-3-030-91699-2_2
Vancouver
Nunes B, Colliri T, Lauretto M de S, Liu W, Liang Z. Anomaly detection in Brazilian Federal Government purchase cards through unsupervised learning techniques [Internet]. Lecture Notes in Computer Science. 2021 ; 13074 19-32.[citado 2024 maio 09 ] Available from: https://doi.org/10.1007/978-3-030-91699-2_2
Trabalho de evento-anais periodico
Analysis of radiographic images of patients with COVID-19 using fractal dimension and complex network-based high-level classification (2021)
Liu, Weiguang; Yan, Jianglong; Zhu, Yu-tao; Pereira, Everson José de Freitas; Li, Gen; Zheng, Qiusheng; Liang, Zhao
Source: Studies in Computational Intelligence. Conference titles: International Conference on Complex Networks and Their Applications. Unidade: FFCLRP
Subjects: COVID-19, REDES COMPLEXAS, RADIOGRAFIA, DIMENSÃO, RAIOS X, IMAGEM DIGITAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LIU, Weiguang et al. Analysis of radiographic images of patients with COVID-19 using fractal dimension and complex network-based high-level classification. Studies in Computational Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-93409-5_2. Acesso em: 09 maio 2024. , 2021
APA
Liu, W., Yan, J., Zhu, Y. -tao, Pereira, E. J. de F., Li, G., Zheng, Q., & Liang, Z. (2021). Analysis of radiographic images of patients with COVID-19 using fractal dimension and complex network-based high-level classification. Studies in Computational Intelligence. Cham: Springer. doi:10.1007/978-3-030-93409-5_2
NLM
Liu W, Yan J, Zhu Y-tao, Pereira EJ de F, Li G, Zheng Q, Liang Z. Analysis of radiographic images of patients with COVID-19 using fractal dimension and complex network-based high-level classification [Internet]. Studies in Computational Intelligence. 2021 ; 1015 16-26.[citado 2024 maio 09 ] Available from: https://doi.org/10.1007/978-3-030-93409-5_2
Vancouver
Liu W, Yan J, Zhu Y-tao, Pereira EJ de F, Li G, Zheng Q, Liang Z. Analysis of radiographic images of patients with COVID-19 using fractal dimension and complex network-based high-level classification [Internet]. Studies in Computational Intelligence. 2021 ; 1015 16-26.[citado 2024 maio 09 ] Available from: https://doi.org/10.1007/978-3-030-93409-5_2

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