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Account of Spatio-Temporal Characteristics in Customs Anti-Smuggling Intelligence Acquisition

Zhanhai Yang et al.

International Journal of Data Warehousing and Mining2025https://doi.org/10.4018/ijdwm.364846article
ABDC C
Weight
0.37

What the paper says

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.

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https://doi.org/https://doi.org/10.4018/ijdwm.364846

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@article{zhanhai2025,
  title        = {{Account of Spatio-Temporal Characteristics in Customs Anti-Smuggling Intelligence Acquisition}},
  author       = {Zhanhai Yang et al.},
  journal      = {International Journal of Data Warehousing and Mining},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijdwm.364846},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.