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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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  • Adewumi Isreal Badiora, “Ecological Theories and Spatial Decision Making of Motor Vehicle Theft (MVT) Offenders in Nigeria”, Journal of Applied Security Research, Vol. 12, No. 3.pp. 374-391, 2017.

  • M.A. Andresen, A.S. Curman and S.J. Linning, “The Trajectories of Crime at Places: Understanding the Patterns of Disaggregated Crime Types”, Journal of Quantitative Criminology, Vol. 33, pp. 427-449, 2017.

  • Andrew P. Wheeler and Wouter Steenbeek, “Mapping the Risk Terrain for Crime Using Machine Learning”, Journal of Quantitative Criminology, Vol. 13, pp. 1-36, 2020.

  • Assessment of Auto Vehicle Theft, “Auto Theft Intelligence Coordination Center (ATICC) Report”, Available at http://www.rmiia.org/downloads/ATICC-Annual-Report2019.pdf, Accessed at 2020.

  • A. Badiora, “Motor Vehicle Theft: An Examination of Offenders’ Characteristics and Targeted Locations in Lagos, Nigeria”, Kriminoloji Dergisi: Turkish Journal of Criminology and Criminal Justice, Vol. 4, No. 2, pp. 59-70, 2012.

  • S. Bai, Z. Liu and C. Yao, “Classify Vehicles in Traffic Scene Images with Deformable Part-Based Models”, Machine Vision and Applications, Vol. 29, No. 3, pp. 393403, 2018.

  • Y. Bengio, A. Courville and P. Vincent, “Representation Learning: A Review and New Perspectives”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, pp. 1798-1828, 2013.

  • J. Bergstra and Y. Bengio, “Random Search for Hyper Parameter Optimization”, The Journal of Machine Learning Research, Vol. 13, No. 1, pp. 281-305, 2012.

  • A. Braga and R. Clarke, “Explaining High-Risk Concentrations of Crime in the City: Social Disorganization, Crime Opportunities, and Important Next Steps”, Journal of Research in Crime and Delinquency, Vol. 51, pp. 480-498, 2014.

  • S. Das and N. Roy, “Applications of Artificial Intelligence in Machine Learning: Review and Prospect”, International Journal of Computer Applications, Vol. 9, pp. 115-129, 2015.

  • Deborah Lamm Weisel, William R. Smith, G. David Garson, Alexi Pavliche and Julie Wartell, “Motor Vehicle Theft: Crime and Spatial Analysis in a Non-Urban Region”, Available at https://nij.ojp.gov/library/publications/motorvehicle-theft-crime-and-spatial-analysis-non-urban-region, Accessed at 2006.

  • J. Douglas, A.W. Burgess and R.K. Ressler, “Crime Classification Manual: A Standard System for Investigating and Classifying Violent Crime”, John Wiley and Sons, 2013.

  • G. Drawve, S.A. Thomas and J.T. Walker, “Bringing the Physical Environment Back into Neighborhood Research: The Utility of RTM for Developing an Aggregate Neighborhood Risk of Crime Measure”, Journal of Criminal Justice, Vol. 44, pp. 21-29, 2016.

  • Eric L. Piza and Jeremy G. Carter, “Predicting Initiator and Near Repeat Events in Spatiotemporal Crime Patterns: An Analysis of Residential Burglary and Motor Vehicle Theft”, Justice Quarterly, Vol. 4, pp. 1-30, 2017.

  • P. Gera and R. Vohra, “City Crime Profiling using Cluster Analysis”, International Journal of Computer Science and Information Technologies, Vol. 5, No. 4, pp. 5145-5148, 2014.

  • Gohar Petrossian and Ronald V. Clarke, “Export of Stolen Vehicles Across Land Borders,” Problem-Oriented Guides for Police Problem-Specific Guides”, Available at https://popcenter.asu.edu/sites/default/files/problems/pdfs/e xport_stolen_vehicles.pdf, Accessed at 2018.

  • M. Goyal, V. Bhatnagar and A. Jain, “A Classification Framework for Data Mining Applications in Criminal Science and Investigation”, Proceedings of International Conference on Data Mining Trends and Applications in Criminal Science and Investigations, pp. 32-51, 2016.

  • Hui Yang, Yia-Tang Fuya and Diavi Yang, “Multi-ANFIS Model Based Synchronous Tracking Control of High-Speed Electric Multiple Unit”, IEEE Transactions on Fuzzy Systems, Vol. 26, No. 3, pp. 1472-1484, 2019.

  • H. Huttunen, F.S. Yancheshmeh and K. Chen, “Car Type Recognition with Deep Neural Networks”, Proceedings of International Conference on Intelligent Vehicles, pp. 11151120, 2016.

  • Hyeon-Woo Kang and Hang-Bong Kang, “Prediction of Crime Occurrence from Multi-Modal Data using Deep Learning”, PLoS ONE, Vol. 12, No. 4, pp. 200-215, 2017.

  • O.E. Isafiade, A. Bagula and S. Berman, “On the Advancement of using Data Mining for Crime Situation Recognition: A Comparative Review”, Proceedings of International Conference on Data Mining Trends and Applications in Criminal Science and Investigations, pp. 131, 2016.

  • N. Jain, P. Sharma and D. Kalbande, “Computerized Forensic Approach using Data Mining Techniques”, Proceedings of the ACM Symposium on Women in Research, pp. 55-60, 2016.

  • D. Jomaa and M. Dougherty, “Speed Prediction for Triggering Vehicle Activated Signs”, Corpus Engineering Journal, Vol. 6, No. 2, pp. 1-16, 2016.

  • L.W. Kennedy and H. Buccine Schraeder, “Vulnerability and Exposure to Crime: Applying Risk Terrain Modelling to the Study of Assault in Chicago”. Applied Spatial Analysis and Policy, Vol. 9, pp. 529-548, 2016.

  • P.K. Kim and K.T. Lim, “Vehicle Type Classification using Bagging and Convolutional Neural Network on Multi View Surveillance Image”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 41-46, 2017.

  • Y.A. Kim and J.R. Hipp, “Street Egohood: An Alternative Perspective of Measuring Neighborhood and Spatial Patterns of Crime”, Quant Criminology, Vol. 36, pp. 29-66, 2020.

  • D. Kuang, P.J. Brantingham and A.L. Bertozzi, “Crime Topic Modelling”, Crime Science, Vol. 6, pp. 1-20, 2017.

  • Mrinalini Jangra and Shaveta Kalsi, “Crime Analysis for Multistate Network using Naive Bayes Classifier”, International Journal of Computer Science and Mobile Computing, Vol. 8, No. 6, pp. 134-143, 2019.

  • N. Morgan, O. Shaw, A. Feist and C. Byron, “Reducing Criminal Opportunity: Vehicle Security and Vehicle Crime”, Available at https://assets.publishing.service.gov.uk/government/upload s/system/uploads/attachment_data/file/489097/horr87.pdf, Accessed at 2016.

  • Nesquivel, O. Nicolis and B.P. Márquez, “Predicting Motor Vehicle Theft in Santiago De Chile using GraphConvolutional LSTM”, Proceedings of International Conference on Chilean Computer Science Society, pp. 1-7, 2020.

  • Y. Peng, J.S. Jin and Y. Cui, “Vehicle Type Classification using Data Mining Techniques”, Proceedings of International Conference on Era of Interactive Media, pp. 325-335, 2013.

  • Y. Peng, J.S. Jin and S. Luo, “Vehicle Type Classification using PCA with Self-Clustering”, Proceedings of International Conference on Multimedia and Expo Workshops, pp. 384-389, 2012.

  • B.M. Prakoso, “The Efforts of the Sabhara Unit Patrol Unit in Preventing Crime of Motor Vehicle Theft in Sumedang Police Jurisdiction: Upaya Unit Patroli Satuan Sabhara dalam Mencegah Tindak Pidana Pencurian Kendaraan Bermotor di Wilayah Hukum Polres Sumedang”, Indonesian Journal of Police Studies, Vol. 5, No. 1, pp. 1734, 2021.

  • Shirota Shinichiro, Alan. E. Gelfand and Jorge Mateu, “Analyzing Car Thefts and Recoveries with Connections to Modelling Origin”, Proceedings of International Conference on Destination Point Patterns, Vol. 3, pp. 1-28, 2020.

  • G. Song, W. Bernasco, L. Liu and W. Liao, “Crime Feeds on Legal Activities: Daily Mobility Flows help to Explain Thieves’ Target Location Choices”, Journal of Quantitative Criminology, Vol. 35, pp. 831-854, 2019.

  • W. Sun, X. Zhang and Y. Jin, “Vehicle Type Recognition Combining Global and Local Features Via Two-Stage Classification”, Mathematical Problems in Engineering, Vol. 2017, pp. 134- 145, 2017.

  • N.V. Kousik, “Analyses on Artificial Intelligence Framework to Detect Crime Pattern”, Proceedings of International Conference on Intelligent Data Analytics for Terror Threat Prediction: Architectures, Methodologies, Techniques and Applications, pp. 119-132, 2021.

  • Tayal, D. K., Jain, A., Arora, S., Agarwal, S., Gupta, T., and Tyagi, N. “Crime detection and criminal identification in India using data mining techniques”, AI and SOCIETY, vol. 30(1), pp. 117-127, 2015.

  • K. Praghash and T. Karthikeyan, “An Investigation of Garbage Disposal Electric Vehicles (GDEVs) Integrated with Deep Neural Networking (DNN) and Intelligent Transportation System (ITS) in Smart City Management System (SCMS)”, Wireless Personal Communications, Vol. 23, pp. 1-20, 2021.

  • Xingyang Ni and Heikki Huttunen, “Vehicle Attribute Recognition by Appearance: Computer Vision Methods for Vehicle Type, Make and Model Classification”, Journal of Signal Processing Systems, Vol. 34, pp. 1-12, 2020.

  • Xiangyu Zhao and Jiliang Tang “Exploring Transfer Learning for Crime Prediction”, Proceedings of

  • International Conference on Data Mining, pp. 1-3, 2017.

  • Z. Xu, W. Yang and L. Huang, “Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline”, Proceedings of European Conference on Computer Vision, pp. 255- 271, 2018.

  • L. Yang, P. Luo and X. Tang, “A Large-Scale Car Dataset for Fine-Grained Categorization and Verification”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3973-3981, 2015.

  • Y. Zhang and L. Hoover, “Space-Time Clustering of Crime Events and Neighborhood Characteristics in Houston”, Criminal Justice Review, Vol. 40, pp. 340-360, 2015.

  • Shiwen Zhang, Yingying Xing, Jian Lu and H. Michael Zhang, “Exploring the Influence of Truck Proportion on Freeway Traffic Safety using Adaptive Network-Based Fuzzy Inference System”, Journal of Advanced Transportation, Vol. 2019, pp.1-14, 2019.


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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