Academic Journal

Automatic Literature Mapping Selection: Classification of Papers on Industry Productivity.

التفاصيل البيبلوغرافية
العنوان: Automatic Literature Mapping Selection: Classification of Papers on Industry Productivity.
المؤلفون: 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
المصدر: Applied Sciences (2076-3417); May2024, Vol. 14 Issue 9, p3679, 17p
مصطلحات موضوعية: INDUSTRY classification, PAPER industry, ARTIFICIAL intelligence, DATABASES, CLASSIFICATION algorithms, ELECTRONIC publications
مستخلص: 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. [ABSTRACT FROM AUTHOR]
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قاعدة البيانات: Complementary Index
الوصف
تدمد:20763417
DOI:10.3390/app14093679