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Account of Spatio-Temporal Characteristics in Customs Anti-Smuggling Intelligence Acquisition: A Combined LSTM+CRF Model Using TF-IDF and Levenshtein

Author

Listed:
  • Zhanhai Yang

    (Nanjing University, China)

  • XuAn Wang

    (Engineering University of PAP, China)

  • Mingyue Qiu

    (Nanjing Police University, China)

  • Senlin Hou

    (Key Laboratory of Wildlife Evidence Technology State Forest and Grassland Administration, China)

  • Yuqiang Wu

    (Nanjing Police University, China)

Abstract

The related information on smuggling crimes exists extensively in various media, with multiple data sources. Anti-smuggling intelligence faces the contradiction between the explosive growth of data size and high-efficiency intelligence judgment. Considering the current characteristics of smuggling activities, it is urgent to obtain knowledge from multi-source case data. Aiming to explore a smuggling knowledge acquisition algorithm based on deep learning, this study proposed an anti-smuggling knowledge representation model with both temporal and spatial characteristics and a knowledge-driven anti-smuggling intelligent judgment method. By combining two means, data, information, knowledge, and intelligence were effectively fused via the Term Frequency-Inverse Document Frequency (TF-IDF) technique and Levenshtein distance algorithms, promoting deep mining and application of anti-smuggling big-data resources and enhancing both automation and intelligence levels in anti-smuggling intelligence judgment.

Suggested Citation

  • Zhanhai Yang & XuAn Wang & Mingyue Qiu & Senlin Hou & Yuqiang Wu, 2024. "Account of Spatio-Temporal Characteristics in Customs Anti-Smuggling Intelligence Acquisition: A Combined LSTM+CRF Model Using TF-IDF and Levenshtein," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 20(1), pages 1-20, January.
    • Handle: RePEc:igg:jdwm00:v:20:y:2024:i:1:p:1-20
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    References listed on IDEAS

    as
    1. Chih-Hao Wen & Ping-Yu Hsu & Ming-Shien Cheng, 2017. "Applying intelligent methods in detecting maritime smuggling," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(3), pages 573-599, August.
    2. Edmond Awad & Sohan Dsouza & Richard Kim & Jonathan Schulz & Joseph Henrich & Azim Shariff & Jean-François Bonnefon & Iyad Rahwan, 2018. "The Moral Machine experiment," Nature, Nature, vol. 563(7729), pages 59-64, November.
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