Silva, João Pedro da; Aguiar, Erikson Júlio de 
; Spadon, Gabriel ; Traina, Agma Juci Machado ; Rodrigues Junior, José Fernando
Fonte: Proceedings. Nome do evento: IEEE International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, SENSORIAMENTO REMOTO, INDICADORES SOCIOECONÔMICOS, SAÚDE PÚBLICA, BRASIL
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ABNT
SILVA, João Pedro da et al. AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data. 2025, Anais.. Los Alamitos: IEEE, 2025. Disponível em: https://doi.org/10.1109/CBMS65348.2025.00180. Acesso em: 09 nov. 2025.APA
Silva, J. P. da, Aguiar, E. J. de, Spadon, G., Traina, A. J. M., & Rodrigues Junior, J. F. (2025). AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS65348.2025.00180NLM
Silva JP da, Aguiar EJ de, Spadon G, Traina AJM, Rodrigues Junior JF. AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data [Internet]. Proceedings. 2025 ;[citado 2025 nov. 09 ] Available from: https://doi.org/10.1109/CBMS65348.2025.00180Vancouver
Silva JP da, Aguiar EJ de, Spadon G, Traina AJM, Rodrigues Junior JF. AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data [Internet]. Proceedings. 2025 ;[citado 2025 nov. 09 ] Available from: https://doi.org/10.1109/CBMS65348.2025.00180
