Development of an Intelligence Framework for Spotting Suicide Bombers Using SmartPLS.

Abstract

Suicide bombing is an act of terrorizing lives and properties in a community. Despite the advancement in military Munitions to combat terrorist activities, there exists a global rise in terrorism almost daily. Terrorism is an act committed by an unidentified group or individual with the intent to damage or jeopardize a community’s safety. Development of an intelligent model for spotting suicide bombers in a crowd is expedient. Document analysis and interview protocol were used to gather facts from the security experts in South Western Nigeria to get the variables that they possibly used to identify terrorists. Their responses were analyzed thematically to form constructs and sub-constructs that were used for the questionnaires for the study. The responses from the questionnaires were used to form the latent variables for the study, which was analyzed using SmartPLS 4. The PLS-SEM algorithm stopped when it reached the stop criterion of 1.0E-7 (i.e., 0.0000001). The reliability of the model was measured using Cronbach’s Alpha and Composite Reliability (CR) when items have factor loading of (>=0.7). The convergent validity was measured using The Average Variance Extracted (AVE) at (0.500). The result shows that the only variables that can be used to form an intelligent framework for suicide bombers are training and location.