ENSEMBLE NEURO-FUZZY BASED SYSTEM FOR VEHICLE THEFT PREDICTION AND RECOVERY

ICTACT Journal on Communication Technology ( Volume: 12 , Issue: 4 )

Abstract

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Vehicle theft is continuously being reported as a global prevalent crime. It often aids the perpetuation of other related crimes such as kidnapping, armed robbery, terrorism and human trafficking. The traditional mode of combating vehicle theft crime is faced with abnormallies hindering accurate, timely prediction and recovery of stolen vehicles from criminals. This paper presents a computational Artificial Intelligence (A.I) technique known as Ensemble Neuro-Fuzzy modeled system with the aim of minimizing investigation time and number of deployed security operatives towards achieving a high successful rate in the prediction, detection and recovery of stolen cars. A collection of data collected from the Criminal Investigation Department of the Nigeria Police Force, were further analyzed through Dimensionality Reduction formula and Routine Activity Approach (RTA) to extract the most significant features. Dataset were sub-divided into 60, 20 and 20% for training, testing and validating the model respectively. A significant result of 92.91% obtained with this model showed that it is most efficient in predicting, detecting and recovering of stolen vehicles as compared with other machine learning algorithms such as Random Tree, Naïve Bayes, J48 and Decision Rule of prediction accuracy of 86.51%, 71.24%, 67.68% and 55.73% respectively.

Authors

Femi Johnson1,Akintunde Saminu2
Federal University of Agriculture, Nigeria1,National Open University of Nigeria, Nigeria2

Keywords

Machine learning, Neuro-fuzzy, Prediction, Recovery, Selection, Significant features, Vehicle theft

Published By
ICTACT
Published In
ICTACT Journal on Communication Technology
( Volume: 12 , Issue: 4 )
Date of Publication
December 2021
Pages
2539-2546

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