Quantitative analysis of terrorist attacks combined with multiple models
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Qingkuo Li, Kun Li, Ziqing Zhang
The quantitative analysis of the data recorded in the terrorist attacks will help to improve the pertinence and efficiency of the fight against terrorism. This paper comprehensively uses neural network, PCA, k-means, wavelet analysis and other models to determine the level of terrorist attacks, and predicts and analyzes the future counter-terrorism situation, and proposes a process model for terrorists to choose targets, and has received terrorist attacks. The level of hazard of the incident and the suspicion of unknown terrorists and the pre-judgment of future counter-terrorism.
Neural network, prediction, k-means, sensitivity analysis, clustering