AI framework by using patterns and relations to understand terrorist behaviors

To address this issue, this research aims to propose a new framework by defining the useful patterns of suicide attacks to analyze the terrorist activity patterns and relations, to understand behaviors and their future moves, and finally to prevent potential terrorist attacks..In the framework, a new network model is formed, and the structure of the relations is analyzed to infer knowledge about terrorist attacks.More specifically, an Evolutionary Simulating Annealing Lasso Logistic Regression (ESALLOR) model is proposed to select key features for similarity function..Subsequently, a new weighted heterogeneous similarity function is proposed to estimate the relationships among attacks..Moreover, a graph-based outbreak detection is proposed to define hazardous places for the outbreak of violence..Experimental results demonstrate the effectiveness of our framework with high accuracy (more than 90% accuracy) for finding patterns when compared with that of actual terrorism events in 2014 and 2015..In conclusion, by using this intelligent framework, governments can understand automatically how terrorism will impact future events, and governments can control terrorists’ behaviors and tactics to reduce the risk of future events.Figure 2..Recent suicide bombing attacks in the world.In this research, patterns and relations (as seen in Figures 4 and 5) are extracted by using collected data..The experimental results are presented by using the collected data and the proposed approaches..Our experiments include two parts..First, the key features are selected with their weights for similarity function..Moreover, the proposed similarity function is used to define popularity and outliers to understand how terrorist groups will attack in the future..Second, we show the finding patterns (see in below Figure 4) to make provision for future terrorist attacks by using calculating relations.. More details

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