Author ORCID Identifier

Mahdi Hashemi

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IEEE Access



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The toll of human casualties and psychological impacts on societies make any study on violent extremism worthwhile, let alone attempting to detect patterns among them. This paper is an effort to predict which violent extremist organization (VEO), among 14 currently active ones throughout the world, is responsible for a violent act based on 14 features, including its human and structural tolls, its target type and value, intelligence, and weapons utilized in the attack. Three main steps in our paper include: 1) the visualization of the violent acts through linear and non-linear dimensionality reduction techniques; 2) sequential forward feature selection based on the generalization accuracy of three machine learning models–decision tree, and linear and nonlinear SVM; and 3) employing multilayer perceptron to predict the VEO based on the selected features of a violent act. Top-ranked selected features were related to the target type and plan and the multilayer perceptron achieved up to 40% test accuracy.


This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see

Digital Object Identifier 10.1109/ACCESS.2018.2879056

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

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Funded by the University of Nebraska at Omaha Open Access Fund