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Predicting chick body mass by artificial intelligence-based models
Provedor de dados: 86
Autores: Ferraz,Patricia Ferreira Ponciano; Yanagi Junior,Tadayuki; Hernández Julio,Yamid Fabián; Castro,Jaqueline de Oliveira; Gates,Richard Stephen; Reis,Gregory Murad; Campos,Alessandro Torres.
The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) - with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Animal welfare; Artificial neural network; Broiler; Modeling; Neuro-fuzzy network; Thermal comfort.
Ano: 2014
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2014000700559