Título:	Artificial neural networks applied in forest biometrics and modeling: state of the art (January/2007 to July/2018).
Autoria:	CHIARELLO, F. STEINER, M. T. A. OLIVEIRA, E. B. de ARCE, J. E. FERREIRA, J. C.
Afiliação:	Flávio Chiarello, PUC-PR; Maria Teresinha Arns Steiner, PUC-PR; EDILSON BATISTA DE OLIVEIRA, CNPF; Júlio Eduardo Arce, UFPR; Júlio César Ferreira, PUC-PR.
Ano de publicação:	2019
Referência:	Cerne, v. 25 n. 2, p. 140-155, Apr./June 2019.
Conteúdo:	Artificial Intelligence has been an important support tool in different spheres of activity, enabling knowledge aggregation, process optimization and the application of methodologies capable of solving complex real problems. Despite focusing on a wide range of successful metrics, the Artificial Neural Network (ANN) approach, a technique similar to the central nervous system, has gained notoriety and relevance with regard to the classification of standards, intrinsic parameter estimates, remote sense, data mining and other possibilities. This article aims to conduct a systematic review, involving some bibliometric aspects, to detect the application of ANNs in the field of Forest Engineering, particularly in the prognosis of the essential parameters for forest inventory, analyzing the construction of the scopes, implementation of networks (type ? classification), the software used and complementary techniques. Of the 1,140 articles collected from three research databases (Science Direct, Scopus and Web of Science), 43 articles underwent these analyses. The results show that the number of works within this scope has increased continuously, with 32% of the analyzed articles predicting the final total marketable volume, 78% making use of Multilayer Perceptron Networks (MLP, Multilayer Perceptron) and 63% from Brazilian researchers.
NAL Thesaurus:	Artificial intelligence Systematic review
Palavras-chave:	Bibliometric Review Multilayer Perceptron Forest Engineering Problems Revisão sistemática Revisão Bibliométrica Inteligência artificial
Digital Object Identifier:	10.1590/01047760201925022626
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPF)