Automatic literature mapping selection : classification of papers on industry productivity

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Brasilia, Department of Electrical Engineering-
Autor(es): dc.contributorUniversity of Brasilia, Department of Electrical Engineering-
Autor(es): dc.contributorUniversity of Brasilia, Department of Electrical Engineering-
Autor(es): dc.contributorUniversity of Brasilia, Department of Economics-
Autor(es): dc.contributorUniversity of Brasilia, Department of Electrical Engineering-
Autor(es): dc.contributorUniversity of Brasilia, Department of Electrical Engineering-
Autor(es): dc.contributorUniversity of Brasilia, Department of Electrical Engineering-
Autor(es): dc.contributorUniversity of Brasilia, Department of Economics-
Autor(es): dc.contributorUniversity of Brasilia, Department of Electrical Engineering-
Autor(es): dc.contributorUniversity of Brasilia, Department of Electrical Engineering-
Autor(es): dc.contributorUniversity of Brasilia, Department of Electrical Engineering-
Autor(es): dc.creatorBispo, Guilherme Dantas-
Autor(es): dc.creatorVergara, Guilherme Fay-
Autor(es): dc.creatorSaiki, Gabriela Mayumi-
Autor(es): dc.creatorMartins, Patrícia Helena dos Santos-
Autor(es): dc.creatorCoelho, Jaqueline Gutierri-
Autor(es): dc.creatorRodrigues, Gabriel Arquelau Pimenta-
Autor(es): dc.creatorOliveira, Matheus Noschang de-
Autor(es): dc.creatorMosquéra, Letícia Rezende-
Autor(es): dc.creatorGonçalves, Vinícius Pereira-
Autor(es): dc.creatorNeumann, Clovis-
Autor(es): dc.creatorSerrano, André Luiz Marques-
Data de aceite: dc.date.accessioned2026-08-11T11:00:50Z-
Data de disponibilização: dc.date.available2026-08-11T11:00:50Z-
Data de envio: dc.date.issued2026-04-30-
Data de envio: dc.date.issued2026-04-30-
Data de envio: dc.date.issued2024-04-
Fonte completa do material: dc.identifierhttp://repositorio.unb.br/handle/10482/54329-
Fonte completa do material: dc.identifierhttps://doi.org/10.3390/app14093679-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0002-4938-2076-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0002-4551-2240-
Fonte completa do material: dc.identifierhttps://orcid.org/0009-0008-5941-1601-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0002-1511-6239-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0002-6517-1957-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0002-4502-2153-
Fonte completa do material: dc.identifierhttps://orcid.org/0009-0001-0769-2826-
Fonte completa do material: dc.identifierhttps://orcid.org/0009-0004-7544-0059-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0002-3771-2605-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0003-4320-8795-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0001-5182-0496-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1186642-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionFaculdade de Tecnologia (FT)-
Descrição: dc.descriptionDepartamento de Engenharia Elétrica (FT ENE)-
Descrição: dc.descriptionFaculdade de Economia, Administração, Contabilidade e Gestão de Políticas Públicas (FACE)-
Descrição: dc.descriptionDepartamento de Economia (FACE ECO)-
Descrição: dc.descriptionPrograma de Pós-Graduação em Engenharia Elétrica, Mestrado Profissional-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherMDPI-
Direitos: dc.rightsAcesso Aberto-
Direitos: dc.rights© 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/).-
Palavras-chave: dc.subjectInteligência-
Palavras-chave: dc.subjectInovação-
Palavras-chave: dc.subjectProdutividade industrial-
Palavras-chave: dc.subjectSustentabilidade-
Palavras-chave: dc.subjectAutomação-
Título: dc.titleAutomatic literature mapping selection : classification of papers on industry productivity-
Tipo de arquivo: dc.typelivro digital-
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