Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorBianconi, André-
Autor(es): dc.creatorvon Zuben, Cláudio J.-
Autor(es): dc.creatorde Serapião, Adriane B.S.-
Autor(es): dc.creatorGovone, José S.-
Data de aceite: dc.date.accessioned2025-08-21T22:20:08Z-
Data de disponibilização: dc.date.available2025-08-21T22:20:08Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2010-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1673/031.010.5801-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/226016-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/226016-
Descrição: dc.descriptionBionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R2) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionDepartamento de Botânica Instituto de Biociências - Unesp - São Paulo State University, Cep 13506-900, Avenida 24-A-
Descrição: dc.descriptionDepartamento de Zoologia IB Unesp-
Descrição: dc.descriptionDepartamento de Estatística MatemáticaAplicada e Computação DEMAC IGCE Unesp-
Descrição: dc.descriptionDepartamento de Botânica Instituto de Biociências - Unesp - São Paulo State University, Cep 13506-900, Avenida 24-A-
Descrição: dc.descriptionDepartamento de Zoologia IB Unesp-
Descrição: dc.descriptionDepartamento de Estatística MatemáticaAplicada e Computação DEMAC IGCE Unesp-
Idioma: dc.languageen-
Relação: dc.relationJournal of Insect Science-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectInsect bionomics-
Palavras-chave: dc.subjectLarval density-
Palavras-chave: dc.subjectLife-history-
Palavras-chave: dc.subjectMass rearing-
Título: dc.titleArtificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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