A machine learning approach towards reviewing the role of ‘Internet of Things’ in the shipping industry

被引：0
|
作者：
Gerakoudi K. [1 ]
Kokosalakis G. [1 ]
Stavroulakis P.J. [1 ,2 ]
机构：
[1] Department of Maritime Transport and Logistics, School of Business and Economics, The American College of Greece, 6 Gravias Street, Ag. Paraskevi
[2] Department of Maritime Studies, School of Maritime and Industrial Studies, University of Piraeus, Karaoli & Dimitriou St. 80, Piraeus
来源：
Journal of Shipping and Trade | / 9卷 / 1期
关键词：
Internet of Things; Machine learning; Natural language processing; Shipping industry;
D O I：
10.1186/s41072-024-00177-w
中图分类号：
学科分类号：
摘要：
The technology of the Internet of Things (IoT) represents a cornerstone of the fourth industrial revolution. We adopt a machine learning approach to examine the effect of IoT technology on shipping business operations. Text mining and the probabilistic latent Dirichlet allocation are applied for an unsupervised topic modelling analysis of two hundred and twenty-eight academic papers. Our findings reveal the potential of IoT to provide more efficient approaches to business operations and improve the quality of services, highlighting the value of instant and secure information flow among all parties involved. Problematic areas of the new technology are also identified, in reference to issues of standardization and interoperability. Relatively few studies have used machine learning techniques to elicit insights into the holistic effect of emerging IoT technology in the shipping industry. The research findings highlight the potential of IoT technology to transform shipping operations, offering useful and practical implications to academics and professionals. © The Author(s) 2024.
引用
收藏
相关论文
共 50 条
  • [31] Securing Internet of Things (IoT) with machine learning
    Zeadally, Sherali
    Tsikerdekis, Michail
    INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, 2020, 33 (01)
  • [32] A survey on application of machine learning for Internet of Things
    Laizhong Cui
    Shu Yang
    Fei Chen
    Zhong Ming
    Nan Lu
    Jing Qin
    International Journal of Machine Learning and Cybernetics, 2018, 9 : 1399 - 1417
  • [33] MACHINE LEARNING FOR MASSIVE INDUSTRIAL INTERNET OF THINGS
    Zhou, Hui
    She, Changyang
    Deng, Yansha
    Dohler, Mischa
    Nallanathan, Arumugam
    IEEE WIRELESS COMMUNICATIONS, 2021, 28 (04) : 81 - 87
  • [34] Machine Learning Approaches for Securing Internet of Things
    Kadale, Nilam K.
    Prasad, Jayashree R.
    Prasad, Rajesh S.
    2019 IEEE PUNE SECTION INTERNATIONAL CONFERENCE (PUNECON), 2019,
  • [35] Triboelectric Nanogenerators with Machine Learning for Internet of Things
    Yang, Jiayi
    Hong, Keke
    Hao, Yijun
    Zhu, Xiaopeng
    Qin, Yong
    Su, Wei
    Zhang, Hongke
    Zhang, Chuguo
    Wang, Zhong Lin
    Li, Xiuhan
    ADVANCED MATERIALS TECHNOLOGIES, 2025, 10 (04):
  • [36] Security of Internet Of Things Using Machine Learning
    Baja, Youssra
    Chougdali, Khalid
    2022 9TH INTERNATIONAL CONFERENCE ON WIRELESS NETWORKS AND MOBILE COMMUNICATIONS, WINCOM, 2022, : 30 - 35
  • [37] Massive connectivity with machine learning for the Internet of Things
    Balci, Abdullah
    Sokullu, Radosveta
    COMPUTER NETWORKS, 2021, 184
  • [38] Towards internet of things modeling: a gateway approach
    Altamimi, Ahmed B.
    Ramadan, Rabie A.
    COMPLEX ADAPTIVE SYSTEMS MODELING, 2016, 4
  • [39] A Machine Learning Approach to Shipping Box Design
    Yang, Guang
    Mu, Cun
    INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 1, 2020, 1037 : 402 - 407
  • [40] Carbon footprint assessment in manufacturing Industry 4.0 using machine learning with intelligent Internet of things
    Liu, Zhao
    Yang, Gangying
    Zhang, Yi
    International Journal of Advanced Manufacturing Technology, 2023,
12345