A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Karthik, N.
- Automatic License Plate Recognition and E-Ticketing System for Transportation
Authors
1 Department of Electronics & Communication Engineering, Tamilnadu College of Engineering, Coimbatore - 641659, IN
Source
Data Mining and Knowledge Engineering, Vol 11, No 3 (2019), Pagination: 33-37Abstract
Electronics Toll Collection system developed in India to save the time by collecting the toll electronically instead of manually. In order to provide zero delay toll collection system, so many modern toll collection systems are used like RF Tags based toll collection system, Barcode Scanner based toll collection system, and number plate recognition based toll collection system. As all the aforesaid systems are reliable, but still it’s not defined as system without human interaction. The paper presents smart toll collection system using embedded Linux environment. The whole system is balanced and focused to design and develop an entirely automated license plate recognition system which will be an excellent low-cost alternative to all other systems. The entire system is design using embedded Linux development board such as Raspberry Pi. The board is most suitable for Implementing Image processing algorithm. In the suggested system one webcam is interfaced with Raspberry Pi Board which is used to capture the image of vehicle’s license plate which will pass through the toll booth. These images of license plates are processed through Optical Character Recognition (OCR) engine such that image of license plate will be converted into equivalent ASCII characters. This extracted information will further send to the RTO server to identify the type of vehicle and owner of the vehicle. The retrieval information will once again send to the system through GSM module interfaced with raspberry pi. According to the type of the vehicle the nominal toll will be deducted from owner’s account. After receiving the notification message on registered mobile number of the owner about the deducted amount from owner’s registered account, the barrier will open and vehicle is allowed to leave the toll booth.
Keywords
License Plate, OCR, Toll.References
- Suryatali, V. Dharmadhikari, “Computer Vision Based Vehicle Detection for Toll Collection System Using Embedded Linux”, ICCPCT, 2015
- A. Wijetunge and D. Ratnaweera, “Real-Time Recognition of License Plates of Moving Vehicles in Sri Lanka”, ICIIS,2011, pp. 82-87
- S. Ramiah, T. Liong, M. Jayabalan,” Detecting Text Based Image With Optical Character Recognition for English Translation and Speech using Android”, SCOReD, 2015, pp. 272-277
- Swarm Intelligence Embedded Data Mining for Precision Agriculture Advancements
Authors
1 Department of Information Technology and Cognitive Systems, Sri Krishna Arts and Science College, IN
2 Department of Mechanical Engineering, Bharati Vidyapeeth College of Engineering, IN
3 Department of Electronics and Communication, Nitte Meenakshi Institute of Technology, IN
4 Department of Computer Applications, IFTM University, IN
Source
ICTACT Journal on Soft Computing, Vol 14, No 2 (2023), Pagination: 3218-3223Abstract
The present study investigates the potential of Swarm Intelligence (SI) in driving breakthroughs in Precision Agriculture (PA). It focuses on the research of mining techniques to uncover novel insights and developments in the field of PA. Social informatics (SI) is an academic discipline that focuses on the examination of collective behaviour within both herbal and synthetic structures. In order to gather, analyse, and synthesise information, SI utilises self-sufficient mobile devices known as Autonomous Mobile Agents (AMAs). These entities refer to robotic and computational frameworks that engage in mutual interaction, facilitating the examination of collective intelligence. This essay examines the potential impact of utilising the System of International Units (SI) on enhancing the accuracy and precision of commodity production and control in the field of production agriculture (PA). It also highlights the existing advancements that have been achieved in this regard. This analysis examines possible uses of Swarm Intelligence in the Public Administration (PA) industry, as well as the challenges that need to be solved in order to enhance the efficiency and accuracy of PA operations.Keywords
Swarm Intelligence, Embedded Data Mining, Precision Agriculture, Machine Learning, Artificial Intelligence, Crop Yield.References
- M. Zhang and N.C. Chi, “Using Artificial Intelligence to Improve Pain Assessment and Pain Management: A Scoping Review”, Journal of the American Medical Informatics Association, Vol. 30, No. 3, pp. 570-587, 2023.
- Dervis Karaboga and B. Akay, “A Survey: Algorithms Simulating Bee Swarm Intelligence”, Artificial Intelligence Review, Vol. 31, No. 1-4, pp. 61-85, 2009.
- G.P. Obi Reddy and G. Ravindra Chary, “Applications of Geospatial and Big Data Technologies in Smart Farming”, Proceedings of International Conference on Smart Agriculture for Developing Nations: Status, Perspectives and Challenges, pp. 15-31, 2023.
- C. Nithya and V. Saravanan, “A Study of Machine Learning Techniques in Data Mining”, International Scientific Refereed Research Journal, Vol. 1, pp. 31-38, 2018.
- N.A. Farooqui and R. Mehra, “IOT based Automated Greenhouse using Machine Learning Approach”, International Journal of Intelligent Systems and Applications in Engineering, Vol. 10, No. 2, pp. 226-231, 2022.
- B.Y. Tasisa and P. John, “Machine Learning Based Massive Leaf Falling Detection For Managing The Waste Disposal Efficiently”, Journal of Contemporary Issues in Business and Government, Vol. 27, No. 1, pp. 1-12, 2021.
- S. Selvi and V. Saravanan, “Mapping and Classification of Soil Properties from Text Dataset using Recurrent Convolutional Neural Network”, ICTACT Journal on Soft Computing, Vol. 11, No. 4, pp. 2438-2443, 2021.
- N. Ambika, “Enhancing Security in IoT Instruments using Artificial Intelligence”, IoT and Cloud Computing for Societal Good, Vol. 45, pp. 259-276, 2022.
- J.N. Nagireddi and L. Manchikanti, “The Analysis of Pain Research through the Lens of Artificial Intelligence and Machine Learning”, Pain Physician, Vol. 25, No. 2, pp. 211-218, 2021.
- A.G. Ismaeel, S. Alani and A.H. Shather, “Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network”, Sustainability, Vol. 15, No. 19, pp. 14522-14543, 2023.
- R. Shesayar, S. Rustagi and S. Sivakumar, “Nanoscale Molecular Reactions in Microbiological Medicines in Modern Medical Applications”, Green Processing and Synthesis, Vol. 12, No. 1, pp. 1-13, 2023.
- W. Serrano, “iBuilding: Artificial Intelligence in Intelligent Buildings”, Neural Computing and Applications, Vol. 23, pp. 1-23, 2022.
- I. Cappelli and G. Peruzzi, “A Machine Learning Model for Microcontrollers Enabling Low Power Indoor Positioning Systems via Visible Light Communication”, Proceedings of IEEE International Symposium on Measurements and Networking, pp. 1-6, 2022.
- O. Vermesan and J. Bacquet, “Internet of Things–The Call of the Edge: Everything Intelligent Everywhere”, CRC Press, 2022.
- K.N.G. Veerappan, J. Perumal and S.J.N. Kumar, “Categorical Data Clustering using Meta Heuristic Link-Based Ensemble Method: Data Clustering using Soft Computing Techniques”, Proceedings of International Conference on Dynamics of Swarm Intelligence Health Analysis for the Next Generation, pp. 226-238, 2023.
- C. Sivakumar and A. Shankar, “The Speech-Language Processing Model for Managing the Neuro-Muscle Disorder Patients by Using Deep Learning”, NeuroQuantology, Vol. 20, No. 8, pp. 918-925, 2022.