ReP USP - Resultado da busca

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: 07 nov. 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 nov. 07 ] 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 nov. 07 ] Available from: https://doi.org/10.1007/978-3-030-93409-5_4
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: 07 nov. 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 nov. 07 ] 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 nov. 07 ] 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: 07 nov. 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 nov. 07 ] 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 nov. 07 ] 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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 07 nov. 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 nov. 07 ] 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 nov. 07 ] 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: 07 nov. 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 nov. 07 ] 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 nov. 07 ] Available from: https://doi.org/10.1007/978-3-030-93409-5_2
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 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
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
Source: Lecture Notes in Artificial Intelligence. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: ICMC, FFCLRP
Subjects: 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: 07 nov. 2024. , 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 2024 nov. 07 ] 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 2024 nov. 07 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_27
Monografia/livro
Machine learning in complex networks (2016)
Silva, Thiago Christiano ; Liang, Zhao
Unidade: FFCLRP
Subjects: APRENDIZADO COMPUTACIONAL, REDES COMPLEXAS, REDES DE COMPUTADORES, SISTEMAS HÍBRIDOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Thiago Christiano e LIANG, Zhao. Machine learning in complex networks. . Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-319-17290-3. Acesso em: 07 nov. 2024. , 2016
APA
Silva, T. C., & Liang, Z. (2016). Machine learning in complex networks. Cham: Springer. doi:10.1007/978-3-319-17290-3
NLM
Silva TC, Liang Z. Machine learning in complex networks [Internet]. 2016 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1007/978-3-319-17290-3
Vancouver
Silva TC, Liang Z. Machine learning in complex networks [Internet]. 2016 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1007/978-3-319-17290-3
Trabalho de evento-anais periodico
Particle competition in complex networks for semi-supervised classification (2009)
Breve, Fabricio; Liang, Zhao; Quiles, Marcos
Source: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecomunications Engineering - LNICST. Conference titles: International Conference on Complex Science - COMPLEX. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES COMPLEXAS, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BREVE, Fabricio e LIANG, Zhao e QUILES, Marcos. Particle competition in complex networks for semi-supervised classification. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecomunications Engineering - LNICST. Berlin: Springer. Disponível em: https://doi.org/10.1007/978-3-642-02466-5_14. Acesso em: 07 nov. 2024. , 2009
APA
Breve, F., Liang, Z., & Quiles, M. (2009). Particle competition in complex networks for semi-supervised classification. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecomunications Engineering - LNICST. Berlin: Springer. doi:10.1007/978-3-642-02466-5_14
NLM
Breve F, Liang Z, Quiles M. Particle competition in complex networks for semi-supervised classification [Internet]. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecomunications Engineering - LNICST. 2009 ; 4 163-174.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1007/978-3-642-02466-5_14
Vancouver
Breve F, Liang Z, Quiles M. Particle competition in complex networks for semi-supervised classification [Internet]. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecomunications Engineering - LNICST. 2009 ; 4 163-174.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1007/978-3-642-02466-5_14
Trabalho de evento-anais periodico
A self-organized network for data clustering (2005)
Liang, Zhao; Damiance Junior, Antonio P G; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Lectures Notes in Computer Science. Conference titles: Advances in Natural Computation: First International Conference. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LIANG, Zhao e DAMIANCE JUNIOR, Antonio P G e CARVALHO, André Carlos Ponce de Leon Ferreira de. A self-organized network for data clustering. Lectures Notes in Computer Science. Heidelberg: Springer. Disponível em: http://springer.lib.tsinghua.edu.cn/(osjebo55xpdpd3ngyau0xbqz)/app/home/contribution.asp?referrer=parent&backto=issue,158,169;journal,105,2245;linkingpublicationresults,1:105633,1. Acesso em: 07 nov. 2024. , 2005
APA
Liang, Z., Damiance Junior, A. P. G., & Carvalho, A. C. P. de L. F. de. (2005). A self-organized network for data clustering. Lectures Notes in Computer Science. Heidelberg: Springer. Recuperado de http://springer.lib.tsinghua.edu.cn/(osjebo55xpdpd3ngyau0xbqz)/app/home/contribution.asp?referrer=parent&backto=issue,158,169;journal,105,2245;linkingpublicationresults,1:105633,1
NLM
Liang Z, Damiance Junior APG, Carvalho ACP de LF de. A self-organized network for data clustering [Internet]. Lectures Notes in Computer Science. 2005 ; 3610[citado 2024 nov. 07 ] Available from: http://springer.lib.tsinghua.edu.cn/(osjebo55xpdpd3ngyau0xbqz)/app/home/contribution.asp?referrer=parent&backto=issue,158,169;journal,105,2245;linkingpublicationresults,1:105633,1
Vancouver
Liang Z, Damiance Junior APG, Carvalho ACP de LF de. A self-organized network for data clustering [Internet]. Lectures Notes in Computer Science. 2005 ; 3610[citado 2024 nov. 07 ] Available from: http://springer.lib.tsinghua.edu.cn/(osjebo55xpdpd3ngyau0xbqz)/app/home/contribution.asp?referrer=parent&backto=issue,158,169;journal,105,2245;linkingpublicationresults,1:105633,1

USP Schools

ReP

Filtros

Departament

Authors

USP affiliated authors

Subjects

Source title

Funding Agencies

Non normalized affiliation