Data/hora: 12/06/2024 11:36:49

Provedor de dados: 	86	
País: 	Brazil	
Título: 	Predicting chick body mass by artificial intelligence-based models	
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.	
Data: 	2014-07-01	
Ano: 	2014	
Palavras-chave: 	Animal welfare; Artificial neural network; Broiler; Modeling; Neuro-fuzzy network; Thermal comfort.	
Resumo: 	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 after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2014000700559	
Editor: 	Embrapa Secretaria de Pesquisa e Desenvolvimento

Pesquisa Agropecuária Brasileira	
Relação: 	10.1590/S0100-204X2014000700009	
Formato: 	text/html	
Fonte: 	Pesquisa Agropecuária Brasileira  v.49 n.7 2014	
Fonte: 	info:eu-repo/semantics/openAccess