Temporal Dengue Outbreak Prediction from Climatic Variables using Finite Element Machines for Regression

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorSri Krishna College of Engineering and Technology-
Autor(es): dc.contributorIit Madras-
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorLydia, M.-
Autor(es): dc.creatorKumar, G. Edwin Prem-
Autor(es): dc.creatorRavichandran, Akash-
Autor(es): dc.creatorMartins, Guilherme Brandao-
Autor(es): dc.creatorPassos, Leandro A.-
Autor(es): dc.creatorPapa, Joao P.-
Data de aceite: dc.date.accessioned2025-08-21T17:09:02Z-
Data de disponibilização: dc.date.available2025-08-21T17:09:02Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/IWSSIP58668.2023.10180266-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307860-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307860-
Descrição: dc.descriptionThe global burden of dengue, a mosquito-borne viral infection, has alarmingly increased in recent decades. The rise in disease occurrence is mainly attributed to changes in the climate, human ecology, globalization, and demography. In such a scenario, an accurate prediction of a dengue outbreak is essential to reduce the morbidity rate significantly. This paper employs two classes of autoregressive models for dengue forecasting and a recently proposed approach called Finite Element Machine for Regression (FEMaR). It also proposes a variant, namely FEMaR-KD, which allows the exploration of k-approximate nearest neighbors to interpolate data points based on k -neighborhood instead of the whole dataset. Such models are built considering environmental parameters, which denote one of the main determinants for infection occurrence. Finally, the proposed models' performance is assessed over two distinct datasets, considering differing spatial scales and regions. Results show that FEMaR obtained a mean absolute error up to 51% smaller than the autoregressive models considering univariate scenarios, and a root mean squared error up to 63% smaller regarding the univariate models.-
Descrição: dc.descriptionSri Krishna College of Engineering and Technology Department of Mechatronics Engineering, Tamil Nadu-
Descrição: dc.descriptionSri Krishna College of Engineering and Technology Department of Information Technology, Tamil Nadu-
Descrição: dc.descriptionIit Madras Department of Electrical Engineering-
Descrição: dc.descriptionUFSCar -Federal University of São Carlos São Carlos Department of Computing-
Descrição: dc.descriptionSão Paulo State University Bauru Department of Computing-
Descrição: dc.descriptionSão Paulo State University Bauru Department of Computing-
Idioma: dc.languageen-
Relação: dc.relationInternational Conference on Systems, Signals, and Image Processing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAutoregression-
Palavras-chave: dc.subjectDengue-
Palavras-chave: dc.subjectFinite Element Machines-
Palavras-chave: dc.subjectMachine Learning-
Palavras-chave: dc.subjectRegression-
Título: dc.titleTemporal Dengue Outbreak Prediction from Climatic Variables using Finite Element Machines for Regression-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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