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Terrorism prevention: a mathematical model for assessing individuals with profiling


Oludare Isaac Abiodun, Aman Jantan, Abiodun Oludare Esther Omolara, Manmeet Mahinderjit Singh, Mohammed Anbar, Kemi Victoria Dada


Vol. 18  No. 7  pp. 117-127


The act of terrorism in the last two decades has caused society of many damages and developmental setbacks in which the cost may be inestimable as trillions of United States Dollars have been lost globally. This study provides an empirical model technique for assessing individuals towards terrorism using people’s profiling to mitigate frequency attacks. Though, there have been research efforts in the direction of terrorism prevention yet assessing individuals with profiling on a large scale through empirical model remains a gap in literature. This research used least square regression technique to generate a mathematical or empirical model that can assessed individual tendency towards crime such as terrorism. During data analysis and model evaluation, a profile of one hundred people was selected by random sampling. The experimental result shows a record of 97.34% assessing accuracy on the generalized test. Assessing rate shows the effectiveness and superiority of the model when compared with some state-of-the-art algorithms and models on terrorism prevention studies such as using data mining technique, machine learning algorithms, fuzzy logic, social media analysis models, knowledge-based framework, anomalies detection etc. Hence, this work proposes a simple but efficient method of the least square empirical model for assessing individuals’ tendency towards terrorism.


Security, people’s profiling, integration, empirical model, assessing individual, and terrorism prevention.