Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1115699
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorCHIARELLO, F.eng
dc.contributor.authorSTEINER, M. T. A.eng
dc.contributor.authorOLIVEIRA, E. B. deeng
dc.contributor.authorARCE, J. E.eng
dc.contributor.authorFERREIRA, J. C.eng
dc.date.accessioned2019-12-03T00:36:35Z-
dc.date.available2019-12-03T00:36:35Z-
dc.date.created2019-12-02
dc.date.issued2019
dc.identifier.citationCerne, v. 25 n. 2, p. 140-155, Apr./June 2019.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1115699-
dc.descriptionArtificial 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.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectBibliometric Revieweng
dc.subjectMultilayer Perceptroneng
dc.subjectForest Engineering Problemseng
dc.subjectRevisão sistemáticaeng
dc.subjectRevisão Bibliométricaeng
dc.subjectInteligência artificialeng
dc.titleArtificial neural networks applied in forest biometrics and modeling: state of the art (January/2007 to July/2018).eng
dc.typeArtigo de periódicoeng
dc.date.updated2019-12-03T11:11:11Z
dc.subject.nalthesaurusArtificial intelligenceeng
dc.subject.nalthesaurusSystematic revieweng
riaa.ainfo.id1115699eng
riaa.ainfo.lastupdate2019-12-03 -02:00:00
dc.identifier.doi10.1590/01047760201925022626eng
dc.contributor.institutionFlávio Chiarello, PUC-PR; Maria Teresinha Arns Steiner, PUC-PR; EDILSON BATISTA DE OLIVEIRA, CNPF; Júlio Eduardo Arce, UFPR; Júlio César Ferreira, PUC-PR.eng
Aparece nas coleções:Artigo em periódico indexado (CNPF)

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
2019EdilsonCerneArtificial.pdf819.93 kBAdobe PDFThumbnail
Visualizar/Abrir
Mostrar registro simples do item Visualizar estatísticas

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace