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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
02/12/2019 |
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Data da última atualização: |
03/12/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CHIARELLO, F.; STEINER, M. T. A.; OLIVEIRA, E. B. de; ARCE, J. E.; FERREIRA, J. C. |
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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. |
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Título: |
Artificial neural networks applied in forest biometrics and modeling: state of the art (January/2007 to July/2018). |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Cerne, v. 25 n. 2, p. 140-155, Apr./June 2019. |
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DOI: |
10.1590/01047760201925022626 |
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Idioma: |
Inglês |
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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. |
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Palavras-Chave: |
Bibliometric Review; Forest Engineering Problems; Inteligência artificial; Multilayer Perceptron; Revisão Bibliométrica; Revisão sistemática. |
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Thesaurus Nal: |
Artificial intelligence; Systematic review. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/205952/1/2019-Edilson-Cerne-Artificial.pdf
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Marc: |
LEADER 02246naa a2200277 a 4500
001 2115699
005 2019-12-03
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1590/01047760201925022626$2DOI
100 1 $aCHIARELLO, F.
245 $aArtificial neural networks applied in forest biometrics and modeling$bstate of the art (January/2007 to July/2018).$h[electronic resource]
260 $c2019
520 $aArtificial 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.
650 $aArtificial intelligence
650 $aSystematic review
653 $aBibliometric Review
653 $aForest Engineering Problems
653 $aInteligência artificial
653 $aMultilayer Perceptron
653 $aRevisão Bibliométrica
653 $aRevisão sistemática
700 1 $aSTEINER, M. T. A.
700 1 $aOLIVEIRA, E. B. de
700 1 $aARCE, J. E.
700 1 $aFERREIRA, J. C.
773 $tCerne$gv. 25 n. 2, p. 140-155, Apr./June 2019.
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Embrapa Florestas (CNPF) |
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