Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks (2020)
- Authors:
- USP affiliated authors: TANNURI, EDUARDO AOUN - EP ; MAUÁ, DENIS DERATANI - IME ; COZMAN, FABIO GAGLIARDI - EP ; NETTO, CAIO FABRICIO DEBERALDINI - EP
- Unidades: EP; IME
- DOI: 10.5753/kdmile.2020.11981
- Subjects: REDES NEURAIS; ANÁLISE DE SÉRIES TEMPORAIS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: SBC
- Publisher place: Porto Alegre
- Date published: 2020
- Source:
- Título: Proceedings
- Conference titles: Symposium on Knowledge Discovery, Mining and Learning - KDMiLe
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
DEBERALDINI NETTO, Caio Fabrício et al. Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks. 2020, Anais.. Porto Alegre: SBC, 2020. Disponível em: https://doi.org/10.5753/kdmile.2020.11981. Acesso em: 15 fev. 2026. -
APA
Deberaldini Netto, C. F., Tannuri, E. A., Mauá, D. D., & Cozman, F. G. (2020). Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks. In Proceedings. Porto Alegre: SBC. doi:10.5753/kdmile.2020.11981 -
NLM
Deberaldini Netto CF, Tannuri EA, Mauá DD, Cozman FG. Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks [Internet]. Proceedings. 2020 ;[citado 2026 fev. 15 ] Available from: https://doi.org/10.5753/kdmile.2020.11981 -
Vancouver
Deberaldini Netto CF, Tannuri EA, Mauá DD, Cozman FG. Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks [Internet]. Proceedings. 2020 ;[citado 2026 fev. 15 ] Available from: https://doi.org/10.5753/kdmile.2020.11981 - Dealing with cycles in graph-based probabilistic models: the case of Logical Credal Networks
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Informações sobre o DOI: 10.5753/kdmile.2020.11981 (Fonte: oaDOI API)
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