Dr MADHIARASAN M, French Institute of Pondicherry, India
Publication profile
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EDUCATIONAL PROFILE
Dr. Madhiarasan M holds an impressive educational background in electrical engineering. He earned his Ph.D. in Electrical Engineering from Anna University, Chennai (2018). He completed his M.E. in Electrical Drives and Embedded Control with a CGPA of 8.403 (2013) and a B.E. in Electrical and Electronics Engineering from Anna University (2010). His recent postdoctoral fellowships include roles at the Transilvania University of BraΘov (2022-2023) and IIT Roorkee (2020-2022). These credentials position him as a scholar with robust expertise in both engineering theory and practical applications.
FIELD OF INTEREST & ACADEMIC PROJECTS
Dr. Madhiarasanβs research interests span SCADA-based supervisory control, robotics for power transmission line inspection, and hybrid neural network models for renewable energy forecasting. His UG and PG projects focused on control systems and robotics, while his Ph.D. tackled neural network applications in energy forecasting. His research aligns with global energy sustainability initiatives, showcasing his commitment to solving pressing electrical engineering challenges.
EXPERIENCE
He has accumulated extensive academic and research experience, starting as a lecturer in 2010. He has taught at various institutions, including the Madras Institute of Technology and Anna University. As an assistant professor at the Bharat Institute of Engineering and Technology (2018-2020), and later as a research fellow, his contributions to academia are significant. His current role at the French Institute of Pondicherry further extends his research prowess.
AWARDS
Among his accolades, Dr. Madhiarasan received the UGC Fellowship and Transilvania Fellowship for young researchers. In 2023, he was honored with the Best Researcher Award at the 10th Global Research Awards on Artificial Intelligence and Robotics. His accolades highlight his excellence in research, particularly in AI and renewable energy applications.
PROJECT GUIDANCE
His project supervision includes the development of IoT-based systems, including energy monitoring and robotics for military use. His work has practical applications, particularly in power transmission monitoring and control, emphasizing his capability to guide impactful projects in the field of electrical and computer engineering.
WORKSHOPS/SEMINARS/CONFERENCE ORGANIZATION
Dr. Madhiarasan has organized key academic events, such as the World Entrepreneursβ Day TECHFEST 2023 and national workshops on research writing. He also co-coordinated AICTE-sponsored faculty development programs on artificial intelligence at IIT Roorkee. His leadership in academic forums demonstrates his active engagement in sharing knowledge with the broader research community.
EDITOR & REVIEWER
He has contributed as an editor for various international journals and special issues, including those on renewable energy and embedded technologies. His editorial work in prestigious journals like Sensors (MDPI) reflects his strong academic standing in the field of electrical engineering and renewable energy research.
CO-CURRICULAR CERTIFICATIONS
Dr. Madhiarasan has completed numerous certifications related to wind and solar energy, electronics, and design thinking, which reflect his commitment to continuous learning. These certifications from top global universities via Coursera strengthen his expertise in energy systems and innovation.
ACHIEVEMENTS
Throughout his career, Dr. Madhiarasan has excelled in academics, including ranking second in his M.E. program. His extracurricular achievements include awards in oratory competitions and television appearances. His participation in national seminars and debates further demonstrates his well-rounded profile as a scholar and communicator.
RESEARCH FOCUS
M. Madhiarasan’s research primarily focuses on applying artificial neural networks (ANNs) and machine learning techniques to wind speed forecasting. His work includes developing methods to optimize neural network architectures, such as selecting the appropriate number of hidden neurons for improved prediction accuracy. He has explored various ANN models, including backpropagation, radial basis function, and spiking neural networks, enhancing their performance with optimization algorithms like Grey Wolf Optimization. Additionally, his studies cover different forecast horizons, long-term predictions, and comparative analyses of network architectures for wind energy applications. π¬οΈππ€π
CONCLUSION
Dr. Madhiarasan M is highly suitable for the Best Researcher Award. With his comprehensive educational background, extensive research experience, and significant contributions to the field of electrical engineering and renewable energy, he stands out as a leader in academic and industrial collaborations. His awards, editorial roles, and leadership in organizing academic events further support his candidacy for this prestigious honor.
PUBLICATION TOP NOTES
Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting
A novel criterion to select hidden neuron numbers in improved back propagation networks for wind speed forecasting
Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network
Analysis of artificial neural network: architecture, types, and forecasting applications
Long-Term Wind Speed Forecasting using Spiking Neural Network Optimized by Improved Modified Grey Wolf Optimization Algorithm
A comprehensive review of sign language recognition: Different types, modalities, and datasets
ELMAN Neural Network with Modified Grey Wolf Optimizer for Enhanced Wind Speed Forecasting
New criteria for estimating the hidden layer neuron numbers for recursive radial basis function networks and its application in wind speed forecasting
Performance investigation of six artificial neural networks for different time scale wind speed forecasting in three wind farms of coimbatore region
Analysis of artificial neural network performance based on influencing factors for temperature forecasting applications
A Novel Method to Select Hidden Neurons in ELMAN Neural Network for Wind Speed Prediction Application