Identifying smuggling vessels with artificial neural network and logistics regression in criminal intelligence using vessels smuggling case data

Authors:
Chih-Hao Wen;Ping-Yu Hsu;Chung-yung Wang;Tai-Long Wu
Affiliations:
Department of Business Administration, National Central University, Jhongli, Taiwan, R.O.C. and Department of Logistics Management, National Defense University, Taipei, Taiwan, R.O.C.;Department of Business Administration, National Central University, Jhongli, Taiwan, R.O.C.;Department of Logistics Management, National Defense University, Taipei, Taiwan, R.O.C.;Department of Logistics Management, National Defense University, Taipei, Taiwan, R.O.C.
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2012

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Abstract

In spite of the gradual increase of the academic studies on smuggling crime, they seldom focus on the subject of applying data mining to crime prevention. Artificial Neural Networks and Logistic Regression are used to conduct classification and prediction. This study establishes models for vessels of different tonnage and operation purpose, which can provide the enforcers with clearer judgment criteria. The study results show that the application of Artificial Neural Networks to smuggling fishing vessel can get the average precision as high as 76.49%, the application of Logistic Regression to smuggling fishing vessel can get the average precision as high as 61.58%, both of which are of significantly higher efficiency compared with human inspection. The information technology can greatly help to increase the probabilities of seizing smuggling vessels, what's more, it can make better use of the data in the database to increase the probabilities of seizing smuggling crimes.