Enhancing Industrial Productivity Through AI-Driven Systematic Literature Reviews

Jaqueline Coelho, Jaqueline Coelho, Guilherme Bispo, Guilherme Bispo, Guilherme Vergara, Guilherme Vergara, Guilherme Vergara, Gabriela Saiki, Gabriela Saiki, André Serrano, André Serrano, André Serrano, Li Weigang, Li Weigang, Li Weigang, Clovis Neumann, Clovis Neumann, Clovis Neumann, Patricia Martins, Patricia Martins, Welber Santos de Oliveira, Welber Santos de Oliveira, Welber Santos de Oliveira, Angela Albarello, Angela Albarello, Ricardo Casonatto, Ricardo Casonatto, Patrícia Missel, Patrícia Missel, Roberto Medeiros Junior, Roberto Medeiros Junior, Jefferson Gomes, Jefferson Gomes, Carlos Rosano-Peña, Caroline F. da Costa, Caroline F. da Costa

2023

Abstract

The advent of Artificial Intelligence (AI) has opened up new possibilities for improving productivity in various industry sectors. In this paper, we propose a novel framework aimed at optimizing systematic literature reviews (SLRs) for industrial productivity. By combining traditional keyword selection methods with AI-driven classification techniques, we streamline the review process, making it more efficient. Leveraging advanced natural language processing (NLP) approaches, we identify six key sectors for optimization, thereby reducing workload in less relevant areas and enhancing the efficiency of SLRs. This approach helps conserve valuable time and resources in scientific research. Additionally, we implemented four machine learning models for category classification, achieving an impressive accuracy rate of over 75%. The results of our analyses demonstrate a promising pathway for future automation and refinements to boost productivity in the industry.

Paper Citation

in Harvard Style

Coelho J., Bispo G., Vergara G., Saiki G., Serrano A., Weigang L., Neumann C., Martins P., Santos de Oliveira W., Albarello A., Casonatto R., Missel P., Medeiros Junior R., Gomes J., Rosano-Peña C. and F. da Costa C. (2023). Enhancing Industrial Productivity Through AI-Driven Systematic Literature Reviews. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-672-9, SciTePress, pages 472-479. DOI: 10.5220/0012235000003584

in Bibtex Style

@conference{webist23,
author={Jaqueline Coelho and Guilherme Bispo and Guilherme Vergara and Gabriela Saiki and André Serrano and Li Weigang and Clovis Neumann and Patricia Martins and Welber Santos de Oliveira and Angela Albarello and Ricardo Casonatto and Patrícia Missel and Roberto Medeiros Junior and Jefferson Gomes and Carlos Rosano-Peña and Caroline F. da Costa},
title={Enhancing Industrial Productivity Through AI-Driven Systematic Literature Reviews},
booktitle={Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2023},
pages={472-479},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012235000003584},
isbn={978-989-758-672-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Enhancing Industrial Productivity Through AI-Driven Systematic Literature Reviews
SN - 978-989-758-672-9
AU - Coelho J.
AU - Bispo G.
AU - Vergara G.
AU - Saiki G.
AU - Serrano A.
AU - Weigang L.
AU - Neumann C.
AU - Martins P.
AU - Santos de Oliveira W.
AU - Albarello A.
AU - Casonatto R.
AU - Missel P.
AU - Medeiros Junior R.
AU - Gomes J.
AU - Rosano-Peña C.
AU - F. da Costa C.
PY - 2023
SP - 472
EP - 479
DO - 10.5220/0012235000003584
PB - SciTePress