Evaluating the latest trends of Industry 4.0 based on LDA topic model


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ÖZYURT Ö., Özköse H., AYAZ A.

Journal of Supercomputing, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2024
  • Doi Number: 10.1007/s11227-024-06247-x
  • Journal Name: Journal of Supercomputing
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Keywords: Industry 4.0, Latent Dirichlet allocation, Text mining, Topic modeling, Trend analysis
  • Karadeniz Technical University Affiliated: Yes

Abstract

This study employs the Latent Dirichlet allocation method, a topic modeling technique, to reveal hidden patterns in Industry 4.0 research. The dataset comprises 8584 articles published in the Scopus database from 2011 to the end of 2022. The analysis categorized the articles into 12 distinct topics. The three most prominent topics identified are “Smart Cyber-Physical Systems,” “Digital Transformation and Knowledge Management” and “Data Science in Energy,” respectively. The findings from this topic modeling provide a comprehensive overview for researchers in the field of Industry 4.0, offering valuable insights into current trends and potential future research directions.