Titre:	Automatic literature mapping selection : classification of papers on industry productivity
Auteur(s):	Bispo, Guilherme Dantas Vergara, Guilherme Fay Saiki, Gabriela Mayumi Martins, Patrícia Helena dos Santos Coelho, Jaqueline Gutierri Rodrigues, Gabriel Arquelau Pimenta Oliveira, Matheus Noschang de Mosquéra, Letícia Rezende Gonçalves, Vinícius Pereira Neumann, Clovis Serrano, André Luiz Marques
metadata.dc.identifier.orcid:	https://orcid.org/0000-0002-4938-2076 https://orcid.org/0000-0002-4551-2240 https://orcid.org/0009-0008-5941-1601 https://orcid.org/0000-0002-1511-6239 https://orcid.org/0000-0002-6517-1957 https://orcid.org/0000-0002-4502-2153 https://orcid.org/0009-0001-0769-2826 https://orcid.org/0009-0004-7544-0059 https://orcid.org/0000-0002-3771-2605 https://orcid.org/0000-0003-4320-8795 https://orcid.org/0000-0001-5182-0496
metadata.dc.contributor.affiliation:	University of Brasilia, Department of Electrical Engineering University of Brasilia, Department of Electrical Engineering University of Brasilia, Department of Electrical Engineering University of Brasilia, Department of Economics University of Brasilia, Department of Electrical Engineering University of Brasilia, Department of Electrical Engineering University of Brasilia, Department of Electrical Engineering University of Brasilia, Department of Economics University of Brasilia, Department of Electrical Engineering University of Brasilia, Department of Electrical Engineering University of Brasilia, Department of Electrical Engineering
Assunto::	Inteligência Inovação Produtividade industrial Sustentabilidade Automação
Date de publication:	avr-2024
Editeur:	MDPI
Référence bibliographique:	BISPO, 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.
Abstract:	The 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.
metadata.dc.description.unidade:	Faculdade de Tecnologia (FT) Departamento de Engenharia Elétrica (FT ENE) Faculdade de Economia, Administração, Contabilidade e Gestão de Políticas Públicas (FACE) Departamento de Economia (FACE ECO)
metadata.dc.description.ppg:	Programa de Pós-Graduação em Engenharia Elétrica, Mestrado Profissional
Licença::	© 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/).
DOI:	https://doi.org/10.3390/app14093679
Collection(s) :	Artigos publicados em periódicos e afins

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