AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data (2025)
- Authors:
- USP affiliated authors: TRAINA, AGMA JUCI MACHADO - ICMC ; RODRIGUES JUNIOR, JOSÉ FERNANDO - ICMC ; SILVA, JOÃO PEDRO DA - ICMC ; AGUIAR, ERIKSON JÚLIO DE - ICMC
- Unidade: ICMC
- DOI: 10.1109/CBMS65348.2025.00180
- Subjects: APRENDIZADO COMPUTACIONAL; SENSORIAMENTO REMOTO; INDICADORES SOCIOECONÔMICOS; SAÚDE PÚBLICA; BRASIL
- Keywords: AI; Remote Sensing; Public Health; Analytics
- Agências de fomento:
- Language: Inglês
- Sustainable Development Goals (GDS):
03. Good health and well-being
10. Reduced inequalities
11. Sustainable cities and communities
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Alamitos
- Date published: 2025
- Source:
- Título: Proceedings
- ISSN: 2372-9198
- Conference titles: IEEE International Symposium on Computer-Based Medical Systems - CBMS
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
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: 28 dez. 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/CBMS65348.2025.00180 -
NLM
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 dez. 28 ] Available from: https://doi.org/10.1109/CBMS65348.2025.00180 -
Vancouver
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 dez. 28 ] Available from: https://doi.org/10.1109/CBMS65348.2025.00180 - On the power of CNNs to detect slums in Brazil
- Satellite imagery predicts socioeconomic indicators: experiments in Brazil
- Utilizing satellite imagery to predict socioeconomic indicators: a study conducted in Brazil
- SentinelAdvMedical: toward adversarial attacks detection on medical image classification via Out-Of-Distribution strategies
- MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data
- Reliable and smart decision support system for emergency management based on crowdsourcing information
- H-Metric: characterizing image datasets via homogenization based on KNN-Queries
- Image retrieval employing genetic dissimilarity weighting and feature space transformation functions
- Hierarchical graph techniques applied to database visualization
- Design and evaluation case study: evaluating the kinect device in the task of natural interaction in a visualization system
Informações sobre o DOI: 10.1109/CBMS65348.2025.00180 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 |
3257463.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
