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dc.contributor.authorBispo, Guilherme Dantas-
dc.contributor.authorVergara, Guilherme Fay-
dc.contributor.authorSaiki, Gabriela Mayumi-
dc.contributor.authorMartins, Patrícia Helena dos Santos-
dc.contributor.authorCoelho, Jaqueline Gutierri-
dc.contributor.authorRodrigues, Gabriel Arquelau Pimenta-
dc.contributor.authorOliveira, Matheus Noschang de-
dc.contributor.authorMosquéra, Letícia Rezende-
dc.contributor.authorGonçalves, Vinícius Pereira-
dc.contributor.authorNeumann, Clovis-
dc.contributor.authorSerrano, André Luiz Marques-
dc.date.accessioned2026-04-30T14:17:53Z-
dc.date.available2026-04-30T14:17:53Z-
dc.date.issued2024-04-
dc.identifier.citationBISPO, Guilherme Dantas et al. Automatic literature mapping selection: classification of papers on industry productivity. Applied Sciences, v. 14, n. 9, 3679, 2024. DOI: https://doi.org/10.3390/app14093679. Disponível em: https://www.mdpi.com/2076-3417/14/9/3679. Acesso em: 24 abr. 2026.pt_BR
dc.identifier.urihttp://repositorio.unb.br/handle/10482/54329-
dc.language.isoengpt_BR
dc.publisherMDPIpt_BR
dc.rightsAcesso Abertopt_BR
dc.titleAutomatic literature mapping selection : classification of papers on industry productivitypt_BR
dc.typeArtigopt_BR
dc.subject.keywordInteligênciapt_BR
dc.subject.keywordInovaçãopt_BR
dc.subject.keywordProdutividade industrialpt_BR
dc.subject.keywordSustentabilidadept_BR
dc.subject.keywordAutomaçãopt_BR
dc.rights.license© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).pt_BR
dc.identifier.doihttps://doi.org/10.3390/app14093679pt_BR
dc.description.abstract1The academic community has witnessed a notable increase in paper publications, whereby the rapid pace at which modern society seeks information underscores the critical need for literature mapping. This study introduces an innovative automatic model for categorizing articles by subject matter using Machine Learning (ML) algorithms for classification and category labeling, alongside a proposed ranking method called SSS (Scientific Significance Score) and using Z-score to select the finest papers. This paper’s use case concerns industry productivity. The key findings include the following: (1) The Decision Tree model demonstrated superior performance with an accuracy rate of 75% in classifying articles within the productivity and industry theme. (2) Through a ranking methodology based on citation count and publication date, it identified the finest papers. (3) Recent publications with higher citation counts achieved better scores. (4) The model’s sensitivity to outliers underscores the importance of addressing database imbalances, necessitating caution during training by excluding biased categories. These findings not only advance the utilization of ML models for paper classification but also lay a foundation for further research into productivity within the industry, exploring themes such as artificial intelligence, efficiency, industry 4.0, innovation, and sustainability.pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-4938-2076pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-4551-2240pt_BR
dc.identifier.orcidhttps://orcid.org/0009-0008-5941-1601pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-1511-6239pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-6517-1957pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-4502-2153pt_BR
dc.identifier.orcidhttps://orcid.org/0009-0001-0769-2826pt_BR
dc.identifier.orcidhttps://orcid.org/0009-0004-7544-0059pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-3771-2605pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-4320-8795pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-5182-0496pt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Electrical Engineeringpt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Electrical Engineeringpt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Electrical Engineeringpt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Economicspt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Electrical Engineeringpt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Electrical Engineeringpt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Electrical Engineeringpt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Economicspt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Electrical Engineeringpt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Electrical Engineeringpt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Electrical Engineeringpt_BR
dc.description.unidadeFaculdade de Tecnologia (FT)pt_BR
dc.description.unidadeDepartamento de Engenharia Elétrica (FT ENE)pt_BR
dc.description.unidadeFaculdade de Economia, Administração, Contabilidade e Gestão de Políticas Públicas (FACE)pt_BR
dc.description.unidadeDepartamento de Economia (FACE ECO)pt_BR
dc.description.ppgPrograma de Pós-Graduação em Engenharia Elétrica, Mestrado Profissionalpt_BR
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