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Filtros : "machine-learning" Limpar

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  • Artigo de periodico

    Computational classification of animals for a highway detection system (2021)

    Sato, Denis ; Zanella, Adroaldo José ; Costa, Ernane José Xavier

    Source: Brazilian Journal of Veterinary Research and Animal Science. Unidades: FMVZ, FZEA

    Subjects: ACIDENTES DE TRÂNSITO, RODOVIAS, VISÃO COMPUTACIONAL, APRENDIZADO COMPUTACIONAL, CAPIVARAS, EQUÍDEOS

    Versão PublicadaAcesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      SATO, Denis e ZANELLA, Adroaldo José e COSTA, Ernane José Xavier. Computational classification of animals for a highway detection system. Brazilian Journal of Veterinary Research and Animal Science, v. 58, n. esp, p. 1-10, 2021Tradução . . Disponível em: https://doi.org/10.11606/issn.1678-4456.bjvras.2021.174951. Acesso em: 03 jan. 2026.

    • APA

      Sato, D., Zanella, A. J., & Costa, E. J. X. (2021). Computational classification of animals for a highway detection system. Brazilian Journal of Veterinary Research and Animal Science, 58( esp), 1-10. doi:10.11606/issn.1678-4456.bjvras.2021.174951

    • NLM

      Sato D, Zanella AJ, Costa EJX. Computational classification of animals for a highway detection system [Internet]. Brazilian Journal of Veterinary Research and Animal Science. 2021 ; 58( esp): 1-10.[citado 2026 jan. 03 ] Available from: https://doi.org/10.11606/issn.1678-4456.bjvras.2021.174951

    • Vancouver

      Sato D, Zanella AJ, Costa EJX. Computational classification of animals for a highway detection system [Internet]. Brazilian Journal of Veterinary Research and Animal Science. 2021 ; 58( esp): 1-10.[citado 2026 jan. 03 ] Available from: https://doi.org/10.11606/issn.1678-4456.bjvras.2021.174951

  • Artigo de periodico

    Potential confounders in the analysis of Brazilian adolescent’s health: a combination of machine learning and graph theory (2020)

    Oku, Amanda Yumi Ambriola ; Morais, Guilherme Augusto Zimeo ; Bueno, Ana Paula Arantes ; Fujita, André ; Sato, João Ricardo

    Source: International Journal of Environmental Research and Public Health. Unidade: IME

    Subjects: ADOLESCENTES, TEORIA DOS GRAFOS

    Versão PublicadaAcesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      OKU, Amanda Yumi Ambriola et al. Potential confounders in the analysis of Brazilian adolescent’s health: a combination of machine learning and graph theory. International Journal of Environmental Research and Public Health, v. 17, n. 1, p. 1-10, 2020Tradução . . Disponível em: https://doi.org/10.3390/ijerph17010090. Acesso em: 03 jan. 2026.

    • APA

      Oku, A. Y. A., Morais, G. A. Z., Bueno, A. P. A., Fujita, A., & Sato, J. R. (2020). Potential confounders in the analysis of Brazilian adolescent’s health: a combination of machine learning and graph theory. International Journal of Environmental Research and Public Health, 17( 1), 1-10. doi:10.3390/ijerph17010090

    • NLM

      Oku AYA, Morais GAZ, Bueno APA, Fujita A, Sato JR. Potential confounders in the analysis of Brazilian adolescent’s health: a combination of machine learning and graph theory [Internet]. International Journal of Environmental Research and Public Health. 2020 ; 17( 1): 1-10.[citado 2026 jan. 03 ] Available from: https://doi.org/10.3390/ijerph17010090

    • Vancouver

      Oku AYA, Morais GAZ, Bueno APA, Fujita A, Sato JR. Potential confounders in the analysis of Brazilian adolescent’s health: a combination of machine learning and graph theory [Internet]. International Journal of Environmental Research and Public Health. 2020 ; 17( 1): 1-10.[citado 2026 jan. 03 ] Available from: https://doi.org/10.3390/ijerph17010090
  • 1

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