Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Kaur, Swapandeep; Kumar, Raman; Singh, Kanwardeep; Huang, Yinglai

doi:10.56578/jse030101

Cited by 6 publications

(1 citation statement)

References 108 publications

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“…Table 10 presents various AI approaches from these two clusters and briefly outlines their specific applications in the paper. It also showcases the practical implementations of these approaches in the shipping industry, paving the path for innovation in the field of shipping AI [50,51].…”

Section: Keyword Co-occurrence Analysismentioning

confidence: 90%

The Application of Artificial Intelligence Technology in Shipping: A Bibliometric Review

Xiao,

Yang,

et al. 2024

JMSE

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Artificial intelligence (AI) technologies are increasingly being applied to the shipping industry to advance its development. In this study, 476 articles published in the Science Citation Index Expanded (SCI-EXPANDED) and the Social Sciences Citation Index (SSCI) of the Web of Science Core Collection from 2001 to 2022 were collected, and bibliometric methods were applied to conduct a systematic literature of the field of AI technology applications in the shipping industry. The review commences with an annual publication trend analysis, which shows that research in the field has been growing rapidly in recent years. This is followed by a statistical analysis of journals and a collaborative network analysis to identify the most productive journals, countries, institutions, and authors. The keyword “co-occurrence analysis” is then utilized to identify major research clusters, as well as hot research directions in the field, providing directions for future research in the field. Finally, based on the results of the keyword co-occurrence analysis and the content analysis of the papers published in recent years, the research gaps in AIS data applications, ship trajectory, and anomaly detection, as well as the possible future research directions, are discussed. The findings indicate that AIS data in the future research direction are mainly reflected in the analysis of ship behavior and AIS data repair. Ship trajectory in the future research direction is mainly reflected in the deep learning-based method research and the discussion of ship trajectory classification. Anomaly detection in the future research direction is mainly reflected in the application of deep learning technology in ship anomaly detection and improving the efficiency of ship anomaly detection. These insights offer guidance for researchers’ future investigations in this area. In addition, we discuss the implications of research in the field of shipping AI from both theoretical and practical perspectives. Overall, this review can help researchers understand the status and development trend of the application field of AI technology in shipping, correctly grasp the research direction and methodology, and promote the further development of the field.

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Section: Keyword Co-occurrence Analysismentioning

confidence: 90%