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A Fuzzy Based Deep Learning Model to Identify the Pattern Recognition for Licensed Plates in Smart Vehicle Management System
In general, vehicle management is based on the proper maintenance and safety of a vehicle. Based on this the quality of the vehicle is calculated. Most of the older vehicles are currently of poor quality and are producing high levels of pollution. Thus, it is necessary to find information about those vehicles. The number plate is helpful to find the information about the vehicle. In this paper, the number blood detection method is proposed. It is based on the fuzzy model and developed in the way of deep learning. Its main purpose is to provide accurate vehicle details from a given set of data. It has also been upgraded to provide its safety measures to its owner based on the vehicle data. Thus, this proposed model prevents major accidents. These functions can also be very helpful in recovering vehicles based on data from stolen vehicles.
Vehicle Management, Fuzzy Model, Deep Learning, Number Plate
- J. Shashirangana, H. Padmasiri and C. Perera, “Automated License Plate Recognition: A Survey on Methods and Techniques”, IEEE Access, Vol. 9, pp. 11203-11225, 2020.
- L. Zheng, T. Sayed and F. Mannering, “Modeling Traffic Conflicts for Use in Road Safety Analysis: A Review of Analytic Methods and Future Directions”, Analytic Methods in Accident Research, Vol. 29, pp. 1-13, 2020.
- T. Karthikeyan and K.H. Reddy, “Binary Flower Pollination (BFP) Approach to Handle the Dynamic Networking Conditions to Deliver Uninterrupted Connectivity”, Wireless Personal Communications, Vol. 121, No. 4, pp. 3383-3402, 2021.
- K. Praghash and T. Karthikeyan, “Data Privacy Preservation and Trade-off Balance Between Privacy and Utility Using Deep Adaptive Clustering and Elliptic Curve Digital Signature Algorithm”, Wireless Personal Communications, Vol. 121, No. 3, pp. 1-16, 2021.
- B. Bhushan, S. Singh and R. Singla, “License Plate Recognition System using Neural Networks and Multithresholding Technique”, International Journal of Computer Applications, Vol. 84, No. 5, pp. 45-50, 2013.
- R.A. Raja, T. Karthikeyan and K. Praghash, “Improved Authentication in Secured Multicast Wireless Sensor Network (MWSN) Using Opposition Frog Leaping Algorithm to Resist Man-in-Middle Attack”, Wireless Personal Communications, Vol. 121, No. 1, pp. 1-17, 2021.
- A.S. Kumar, L.T. Jule and A.H. Gandomi, “Analysis of False Data Detection Rate in Generative Adversarial Networks using Recurrent Neural Network”, Academic Press, pp. 289-312, 2021.
- A. Puranic, K. Deepak and V. Umadevi, “Vehicle Number Plate Recognition System: A Literature Review and Implementation using Template Matching”, International Journal of Computer Applications, Vol. 134, No. 1, pp. 12-16, 2016.
- R.A. Raja, T. Karthikeyan and K. Praghash, “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. 121, No. 4, pp. 1-20, 2021.
- Y.T. Chen, J.H. Chuang and H.T. Chen, “Robust License Plate Detection in Nighttime Scenes using Multiple Intensity Ir-Illuminator”, Proceedings of IEEE International Symposium on Industrial Electronics, pp 893-898, 2012.
- H. Vashishtha, G. Sharma and A.M. Tripathi, “Vehicle Owner Identification from Number Plate”, Proceedings of International Conference on Recent Trends in Computing, pp. 131-138, 2022.
- Y. Wen, Y. Lu, J. Yan and P. Shi, “An Algorithm for License Plate Recognition Applied to Intelligent Transportation System”, IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 3, pp. 830-845, 2011.
- F. Ullah and D. Kwak, “Barrier Access Control using Sensors Platform and Vehicle License Plate Characters Recognition”, Sensors, Vol. 19, No. 13, pp. 3015-3024, 2019.
- S. Anekar, S. Yeginwar and H. Sonune, “Automated Gate System using Number Plate Recognition (NPR)”, Proceedings of International Conference on ICT Systems and Sustainability, pp. 413-420, 2022.
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