Intelligent Deep Fusion Network for Anomaly Identification in Maritime Transportation Systems

This paper introduces a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with decomposition is explored to find abnormal behavior in maritime data. The set of maritime data is first decomposed into similar clusters containing homogeneous data, and then a convolutional neural network is used for each data cluster. Different models are trained (one per cluster), and each model is learned from highly correlated data. Finally, the results of the models are merged using a simple but efficient fusion strategy. To verify the performance of the proposed framework, intensive experiments were conducted on marine data. The results show the superiority of the proposed framework compared to the baseline solutions in terms of several accuracy metrics.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2022.3151490
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Djenouri, Youcef
  • Belhadi, Asma
  • Djenouri, Djamel
  • Srivastava, Gautam
  • Lin, Jerry Chun-Wei
• Publication Date: 2023-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 2392-2400
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Data analysis; Data quality; Machine learning; Neural networks; Water transportation
• Subject Areas: Data and Information Technology; Marine Transportation;

Filing Info

• Accession Number: 01886712
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 28 2023 4:57PM