Dr. Rajesh Kumar M
Vellore Institute of Technology University, Vellore, Tamil Nādu, International
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Academic Career Length
21Years (2006-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 November 2017 in Solar Energy
Journal Article
DMDerick Mathew
Derick MathewCRC. Rani
C. Rani
Rajesh Kumar M
Mohamed Emad FarragYWY. Wang
Y. Wang
KBK. Busawon
K. Busawon
7 December 2017 in IEEE Journal of Photovoltaics
Journal Article
DMDerick Mathew
Derick MathewRNRani Chinnappa Naidu
Rani Chinnappa NaiduRajesh Kumar MYWYue Wang
Yue Wang
RBRichard Binns
Richard BinnsKBKrishna Busawon
Krishna Busawon
In order to increase the efficiency of the solar photovoltaic (PV) system, accurate electrical modeling of the system under different environmental conditions is necessary. The double-diode electrical model of solar PV is known to be more accurate than its single diode model counterpart since it takes into account the effect of recombination. However, because of its nonlinear characteristics, the parameters of the double-diode model (DDM) have to be identified using optimization algorithms. In this paper, the wind-driven optimization (WDO) algorithm is proposed as a potential new method for identifying the parameters of a 12-parameter DDM of the solar PV. The accuracy and flexibility of the proposed method are verified using three different sets of data: first, experimental data at the controlled environmental condition; second, data sheet values of different solar PV modules; and third, real-time experimental data at the uncontrolled environmental condition. Additionally, the performance of the WDO is compared with other well-known existing optimization techniques. The obtained results show that the WDO algorithm can provide optimized values with reduced mean absolute error in power and reduced root mean square error for different types of solar PV modules at different environmental conditions. We show that the WDO can be confidently recommended as a reliable optimization algorithm for parameter estimation of solar PV model.
Journal Article
MTMithul Raaj A T
Mithul Raaj A TBBBalaji B
Balaji BSRSai Arun Pravin R R
Sai Arun Pravin R RRNRani Chinnappa Naidu
Rani Chinnappa NaiduRajesh Kumar M
Arooj Mubashara Siddiqui +4
In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.
24 August 2020 in Multimedia Tools and Applications
Journal Article
1 June 2017
Conference Paper
SMS Maghilnan
S Maghilnan
Rajesh Kumar MSentiment analysis has evolved over past few decades, most of the work in it revolved around textual sentiment analysis with text mining techniques. But audio sentiment analysis is still in a nascent stage in the research community. In this proposed research, we perform sentiment analysis on speaker discriminated speech transcripts to detect the emotions of the individual speakers involved in the conversation. We analyzed different techniques to perform speaker discrimination and sentiment analysis to find efficient algorithms to perform this task.
28 March 2024 in IEEE Access
Journal Article
AAafnan al-ali
afnan al-aliMAMariam AlMaadeed
Mariam AlMaadeedMSMoutaz Saleh
Moutaz SalehRNRani Chinnappa Naidu
Rani Chinnappa NaiduZAZachariah C Alex
Zachariah C AlexRajesh Kumar M +2
Dysarthria, a speech disorder stemming from neurological conditions, affects communication and life quality. Precise classification and severity assessment are pivotal for therapy but are often subjective in traditional speech-language pathologist evaluations. Machine learning models offer objective assessment potential, enhancing diagnostic precision. This systematic review aims to comprehensively analyze current methodologies for classifying dysarthria based on severity levels, highlighting effective features for automatic classification and optimal AI techniques. We systematically reviewed the literature on the automatic classification of dysarthria severity levels. Sources of information will include electronic databases and grey literature. Selection criteria will be established based on relevance to the research questions. The findings of this systematic review will contribute to the current understanding of dysarthria classification, inform future research, and support the development of improved diagnostic tools. The implications of these findings could be significant in advancing patient care and improving therapeutic outcomes for individuals affected by dysarthria.
About Me
Rajesh Kumar Muthu is a Chartered Engineer of IET, U.K., and a Senior Member of IEEE. An accomplished academician with 24 years of teaching and research experience across India (2000-2010, 2015-2021, 2023-Present), United Kingdom (2010-2015), and Mauritius (2022). He received his B.E. degree in electronics and communication engineering from the University of Madras, Chennai, India. The M.E. degree in electrical drives and embedded control from the College of Engineering, Guindy, Anna University, Chennai, India. Subsequently, he earned an M.B.A. degree in systems from Alagappa University, Karaikudi, India. In 2010, Rajesh Kumar Muthu was awarded the Research Scholarships Award for his PhD degree from the Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom. He was also a Senior Research Assistant with the Department of Mathematics and Information Science, Northumbria University, U.K., in early 2015. Additionally, he received grants from e-COST (2013), VIT University SEED, he was a CO-PI for a project funded by the Royal Academy of Engineering, U.K. In France- Mauritius ULIMA project he was invited International transversal researcher (2024). He was awarded VIT faculty startup grant 2024. QU: IRCC Qatar(23-24), His research group is focused on speech signal processing, biomedical signal processing, medical image processing, data analytics, Artificial Intelligence/ Machine Learning in healthcare applications,