vioft2nntf2t|tblJournal|Abstract_paper|0xf4fff2312c000000051e060001000200 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.
Femi Johnson1,Akintunde Saminu2
Federal University of Agriculture, Nigeria1,National Open University of Nigeria, Nigeria2
Machine learning, Neuro-fuzzy, Prediction, Recovery, Selection, Significant features, Vehicle theft
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 12 , Issue: 4 , Pages: 2539-2546 )
Date of Publication :
December 2021
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278
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