Open Access

ARTICLE

Predicting the Type of Crime: Intelligence Gathering and Crime Analysis

Saleh Albahli¹, Anadil Alsaqabi¹, Fatimah Aldhubayi¹, Hafiz Tayyab Rauf^2,*, Muhammad Arif³, Mazin Abed Mohammed⁴

1 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
2 Department of Computer Science, University of Gujrat, Gujrat, Pakistan
3 School of Computer Science, Guanzghou University, Guangzhou, 510006, China
College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq

* Corresponding Author: Hafiz Tayyab Rauf. Email:

(This article belongs to the Special Issue: Intelligent techniques for energy efficient service management in Edge computing)

Computers, Materials & Continua 2021, 66(3), 2317-2341. https://doi.org/10.32604/cmc.2021.014113

Received 31 August 2020; Accepted 05 October 2020; Issue published 28 December 2020

Abstract

Crimes are expected to rise with an increase in population and the rising gap between society’s income levels. Crimes contribute to a significant portion of the socioeconomic loss to any society, not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy, social parameters, and reputation of a nation. Policing and other preventive resources are limited and have to be utilized. The conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are complex. Making it possible for such algorithms to provide indicators of specific areas that may become criminal hot-spots. These predictions can be used by policymakers and police personals alike to make effective and informed strategies that can curtail criminal activities and contribute to the nation’s development. This paper aims to predict factors that most affected crimes in Saudi Arabia by developing a machine learning model to predict an acceptable output value. Our results show that FAMD as features selection methods showed more accuracy on machine learning classifiers than the PCA method. The naïve Bayes classifier performs better than other classifiers on both features selections methods with an accuracy of 97.53% for FAMD, and PCA equals to 97.10%.

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