Research on population trafficking prediction based on K-means and BP neural network
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Zhihong Liu, Zhongxian Zhu, Suxin Liu, Xiaoyu Han
This paper mainly analyzes the issue of human trafficking. First, identify high-risk groups and prevent them in time. Using the data collected from countries around the world, the TOPSIS method was used to evaluate the risk of trafficking in countries around the world. The evaluation results were clustered by K-MEANS method and represented by different colors on the map. Second, locate the victim. Taking the United States as an example, we have further refined the location of the victims. By looking at the number of human trafficking cases in each state in 2012-2017, BP neural network was used to predict the number of human trafficking cases in each state in the next four years.
TOPSIS method, K-MEANS, neural network, graph theory, human trafficking