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Ensemble Neuro-fuzzy Based System For Vehicle Theft Prediction And Recovery


Affiliations
1 Department of Computer Science, Federal University of Agriculture, Abeokuta, Nigeria
2 Department of Information Technology, National Open University of Nigeria, Nigeria
     

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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 NeuroFuzzy 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.

Keywords

Machine learning, Neuro-fuzzy, Prediction, Recovery, Selection, Significant features, Vehicle thef
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  • Ensemble Neuro-fuzzy Based System For Vehicle Theft Prediction And Recovery

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Authors

Femi Johnson
Department of Computer Science, Federal University of Agriculture, Abeokuta, Nigeria
Akintunde Saminu
Department of Information Technology, National Open University of Nigeria, Nigeria

Abstract


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 NeuroFuzzy 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.

Keywords


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

References