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Filtros : "Financiamento Ministry of Science and Technology of the People's Republic of China" "International Conference on Complex Networks and Their Applications" Limpar

Filtros

Idioma
- inglês (~2)
Título da fonte
- complex networks & their applications x (~1)
- studies in computational intelligence (~1)
Editora
- springer (~2)
Nome do evento
- international conference on complex networks and their applications (~2)
ODS
País de publicação
- suiça (~2)
Grupo de pesquisa
Agência de fomento
- financiamento cnpq (~2)
- financiamento ministry of science and technology of the people's republic of china (~2)
- fundação de amparo à pesquisa do estado de são paulo (fapesp) (~2)
Indexado em
Colaboração institucional
Afiliação dos autores externos normalizada
Afiliação dos autores externos não normalizada
- china branch of brics institute of future networks, shenzhen (1)
- china branch of brics institute of future networks, shenzhen (1)
- department of computing and mathematics, university of sao paulo (usp), ribeirao preto (1)
- henan key laboratory on public opinion intelligent analysis, zhongyuan university of technology, zhengzhou (1)
- henan key laboratory on public opinion intelligent analysis, zhongyuan university of technology, zhengzhou (1)

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
Fonte: Complex Networks & Their Applications X. Nome do evento: International Conference on Complex Networks and Their Applications. Unidade: FFCLRP
Assuntos: 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 nov. 2025. , 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 2025 nov. 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 2025 nov. 09 ] Available from: https://doi.org/10.1007/978-3-030-93409-5_4
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
Fonte: Studies in Computational Intelligence. Nome do evento: International Conference on Complex Networks and Their Applications. Unidade: FFCLRP
Assuntos: 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 nov. 2025. , 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 2025 nov. 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 2025 nov. 09 ] Available from: https://doi.org/10.1007/978-3-030-93409-5_2

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