A Bibliometric Analysis of Machine Learning in Maritime Industry as Part of Industry 4.0/5.0

Abstract

Machine learning technologies are increasingly integrated into maritime operations, transforming management practices. They optimize fleet management, enhance safety, and improve efficiency, particularly within Industry 4.0/5.0. This study analyzes publication and citation patterns of deep learning in maritime operations from 2015 to 2024, focusing on leading authors, countries, active journals, and years with the highest output. Data were extracted from the Web of Science (WoS) through a detailed search and analyzed with VOSviewer, resulting in 822 documents. The year 2024 recorded the highest number of studies <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(n=188)$</tex>, with China as the top contributor and Elsevier as the leading publisher. This work provides the first complete bibliometric evaluation combining productivity and citation metrics to map machine learning research in the maritime industry.