Inovação na predição dos valores energéticos de alimentos para aves: um estudo bibliométrico sobre as redes bayesianas

Abstract

The Thomson Reuters Web of Science is a database that makes it possible to identify patterns and trends in scientific publications, thus allowing a broad understanding of publications in the area of interest. An area that has attracted attention for the statistical community is the Bayesian networks, due to the fact that they present probabilistically promising models of machine learning. The purpose of this was to identify and describe the main categories of Web of Science that include research on Bayesian networks, believing in their potential for the poultry industry, more specifically in the prediction of energy values of feedstuffs. To carry out this research, data were collected from the Thomson Reuters Web of Science database from 1945 to 2018. Through the search it is possible to answer several questions of interest, among them, whether there are publications from Bayesian networks mainly in the animal sciences, more specifically in the formulation of feedstuffs for broilers. The results found confirmed that this area of knowledge is still very recent. The first publications were in 1990 and the main publications are concentrated in computer science, and no research has been found to predict the metabolizable energy of feedstuffs for broilers using this methodology.