Automatic literature mapping selection : classification of papers on industry productivity
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| Main Author: | |
|---|---|
| Publication Date: | 2024 |
| Other Authors: | , , , , , , , , , |
| Format: | Article |
| Language: | eng |
| Source: | Repositório Institucional da UnB |
| Download full: | http://repositorio.unb.br/handle/10482/54329 https://doi.org/10.3390/app14093679 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 |
Summary: | 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. |
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http://repositorio.unb.br/handle/10482/54329https://doi.org/10.3390/app14093679
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