RAJKUMAR S C

@autmdu.in

Teaching Fellow
Anna University Regional Campus Madurai



           

https://researchid.co/rajkumar030821

Dr. Rajkumar S C is a highly accomplished individual with a strong background in Computer Science and Engineering. Currently, I hold the position of a Teaching Fellow at Anna University Regional Campus Madurai. My academic journey includes a B.E., M.E., and Ph.D. in Computer Science and Engineering, showcasing my dedication to knowledge acquisition and expertise in the field.
With close to 9 years of experience in teaching, I am deeply committed to academia and the development of future professionals. My areas of specialization revolve around Intelligent Transportation Systems, Deep Learning, Cloud Technologies, and Machine Learning & IoT Technologies, where I continuously strive to expand my knowledge and contribute to advancements in these domains.
My research contributions have been significant, and I take pride in the publication of multiple papers in esteemed international journals such as Elsevier and Wiley.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Information Systems, Human-Computer Interaction

6

Scopus Publications

Scopus Publications

  • Secure session key pairing and a lightweight key authentication scheme for liable drone services
    Rajkumar ．S．C, Jegatha Deborah ．L, Vijayakumar ．P, and Karthick ．KR
    Elsevier BV

  • Optimized traffic flow prediction based on cluster formation and reinforcement learning
    S.C. Rajkumar, Jegatha Deborah L., and Vijayakumar P.
    Wiley
    SummaryIn recent days, the traffic flow information is collected using the global positioning system through the Internet, which is yet to become ubiquitous. A novel technique is proposed for the intelligent transportation system, which leads to reduce the traffic congestion that will become an unavoidable phenomenon in the near future. This system uses a magnetic sensor to identify the type of the vehicle and the exact vehicle count in the traffic environment based on variation in the magnetic flux. This information is transmitted to the cloud server with the help of cluster by utilizing the nearby proximity services. An intelligent agent that uses reinforcement learning is implemented in the cloud server to learn the real‐time traffic flow from multiple sources for the prediction of a valid and optimized route suggestion for the registered users. This work is implemented, and implementation results show that the proposed work achieves an accuracy of 98.36%. Hence, this intelligence method for VANETs will certainly account for improved traffic prediction to the vehicle transportation. It can reduce the vehicles waiting time in traffic and that would minimize the fuel consumption. It will make an eco‐friendly environment of reduced carbon dioxide emissions in urban cities.

  • Passive-Awake Energy Conscious Power Consumption in Smart Electric Vehicles Using Cluster Type Cloud Communication
    Pandi Vijayakumar, S. C. Rajkumar, and L. Jegatha Deborah
    IGI Global
    Nowadays, electric vehicles (e-vehicles) have a significant impact on the current intelligent transportation system, with the goal of establishing a smart environment in the near future. Furthermore, when an intelligent system is integrated with IoT technologies, it produces more efficient results to the society. This research work examines the impact of energy degradation on the wireless transmission to optimize power consumption using a passive-awake cloud-cluster communication system, thereby extending the lifetime of an energy-constrained electric vehicle. Wireless communication means that electromagnetic waves draining a steady amount of energy from the condenser, even if the device is not connected to the internet, which constitutes the main constraint for a long-distance electric vehicle. In this paper, a passive-awake assistant is proposed, which significantly reduces power consumption.

  • An improved public transportation system for effective usage of vehicles in intelligent transportation system
    S.C. Rajkumar and L. Jegatha Deborah
    Wiley
    SummaryProcuring usage of the public transportation system enhances the promising effect of limiting the number of own vehicles usage in the contemporary world. The present research advocates a new paradigm of the Intelligent Transportation System (ITS) in the near future, to rescue fossil fuel and to maintain a healthy environment for the current generation. To provide this facility, Long Short Term Memory (LSTM) based intelligent learner has been proposed. This intelligent learner is mainly used to predict high vehicle demand requests in order to utilize a public transport system effectively. In this way, excess usages of vehicles are reduced from low vehicle demand request locations to the locations where high vehicles demand requests are generated. Moreover, a new enhanced approach has also been designed to establish communication between the onboard vehicles and the passengers for instant reservation of their seats based on real‐time sensors. To achieve the effective usage of the public transportation system, an effective dynamic scheduling algorithm that dedicates more convenient travel in the complex transportation system, has been proposed. The proposed system results are evaluated using real‐time transport data, which are collected from major cities and they are implemented to predict the exact vehicles demand. The performance results are compared with various existing methods and the proposed system has proved its efficiency than the existing methods. When the proposed system is implemented, it improves 87% usage of public transportation as well as the usage of taxis and own vehicles would be reduced drastically in the city.