Fonte: Journal of Complex Networks. Unidades: ICMC, IFSC
Assuntos: TECNOLOGIAS DA SAÚDE, APRENDIZADO COMPUTACIONAL, REDES COMPLEXAS, RECONHECIMENTO DE IMAGEM, DIAGNÓSTICO POR COMPUTADOR, DOENÇAS VASCULARES
ABNT
PINEDA, Aruane Mello et al. Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning. Journal of Complex Networks, v. 11, n. 5, p. cnad030-1-cnad030-21, 2023Tradução . . Disponível em: https://doi.org/10.1093/comnet/cnad030. Acesso em: 06 set. 2024.APA
Pineda, A. M., Rodrigues, F. A., Alves, C. L., Möckel, M., Oliveira, T. G. L. de, & Porto, J. A. M. (2023). Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning. Journal of Complex Networks, 11( 5), cnad030-1-cnad030-21. doi:10.1093/comnet/cnad030NLM
Pineda AM, Rodrigues FA, Alves CL, Möckel M, Oliveira TGL de, Porto JAM. Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning [Internet]. Journal of Complex Networks. 2023 ; 11( 5): cnad030-1-cnad030-21.[citado 2024 set. 06 ] Available from: https://doi.org/10.1093/comnet/cnad030Vancouver
Pineda AM, Rodrigues FA, Alves CL, Möckel M, Oliveira TGL de, Porto JAM. Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning [Internet]. Journal of Complex Networks. 2023 ; 11( 5): cnad030-1-cnad030-21.[citado 2024 set. 06 ] Available from: https://doi.org/10.1093/comnet/cnad030