Identifying Smuggling Vessels with Artificial Neural Network and Logistics Regression in Criminal Intelligence Using Vessels Smuggling Case Data

机译：使用船只走私案件数据在人工情报网络中识别走私船只并在刑事情报中进行物流回归

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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.

机译：尽管关于走私犯罪的学术研究逐渐增多，但他们很少关注将数据挖掘应用于预防犯罪的主题。人工神经网络和Logistic回归用于进行分类和预测。本研究建立了不同吨位和操作目的的船舶模型，可以为执法者提供更清晰的判断标准。研究结果表明，人工神经网络在走私渔船上的平均精度高达76.49％，Logistic回归在走私渔船上的平均精度高达61.58％，两者与人工检查相比，效率显着提高。信息技术可以极大地帮助提高查获走私船的可能性，而且可以更好地利用数据库中的数据来提高查获走私罪的可能性。

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《Asian conference on intelligent information and database systems;ACIIDS 2012》|2012年|p.539-548|共10页
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Chih-Hao Wen; Ping-Yu Hsu; Chung-yung Wang; Tai-Long Wu;
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中图分类人工智能理论;
关键词
artificial intelligent; crime data mining; smuggling predicts; artificial neural networks; logistics regression; hybrid model;

机译：人工智能犯罪数据挖掘;走私预测人工神经网络;物流回归;混合模型;

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Identifying Smuggling Vessels with Artificial Neural Network and Logistics Regression in Criminal Intelligence Using Vessels Smuggling Case Data

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