Tushar Kafare | Artificial Intelligence | Best Researcher Award

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Dr. Tushar Kafare | Artificial Intelligence | Best Researcher Award

Assistant Professor at Sinhgad College of Engineering, India

Dr. Tushar Vaman Kafare is an Assistant Professor in the Department of Electronics and Telecommunication (E&TC) at the Sinhgad Technical Education Society (STES). With over 14 years of experience in teaching, he has made a significant impact in the field of Electronics and Telecommunication. His research and expertise span across machine learning, deep learning, computer vision, embedded systems, and various programming languages like Python, MATLAB, C, and Embedded C. Dr. Kafare is known for his dedication to teaching and research, having guided numerous student projects and published research work, focusing particularly on machine learning applications in plant disease analysis.

Profile

Google Scholar

Education

Dr. Kafare holds an M.E. degree in Electronics and Telecommunication, as well as a B.E. in Electronics. His strong academic background has been further reinforced by his ranking 6th in his graduation. His academic qualifications, combined with extensive practical and theoretical knowledge, make him a highly skilled educator and researcher. His ongoing Ph.D. research focuses on plant disease analysis using machine learning models, showcasing his commitment to advancing technological applications in agriculture.

Experience

Having joined STES on September 7, 2022, Dr. Kafare brings with him a wealth of experience in academia and industry. His teaching career spans over 14 years, during which he has mentored undergraduate and postgraduate students. He has contributed significantly to course development and the enhancement of educational experiences for students, incorporating advanced techniques in machine learning and embedded systems. Additionally, Dr. Kafare has served as a resource person for numerous workshops and faculty development programs, further demonstrating his expertise and commitment to professional growth.

Research Interests

Dr. Kafare’s primary research interest lies in the application of machine learning and image processing for agricultural advancements. His Ph.D. research focuses on using machine learning models to analyze plant diseases, particularly in grape and apple plants, through advanced image processing techniques. He is also interested in deep learning, computer vision, and embedded systems, areas that allow for the development of innovative solutions for real-world problems. Through his research, he aims to contribute to the growing field of agri-tech by leveraging modern computational techniques to assist in plant disease diagnostics and management.

Awards

Dr. Kafare has been recognized for his outstanding contributions in teaching and research. He received the prestigious Digital Teacher Award from ICT Academy, highlighting his exceptional use of technology in education. Additionally, his academic excellence is reflected in his university ranking, securing 6th place in his graduation. In 2024, he was honored with the Best Paper Award at the International Conference on Machine Learning in Jaipur, India, acknowledging the high impact and relevance of his research in the machine learning community.

Publications

Dr. Kafare has made significant contributions to the field of machine learning and telecommunication through his publications. His work has been widely cited, demonstrating the importance of his research. Below is a list of selected publications:

Kafare, T.V. et al., “Analysis on Plant Disease Diagnosis Using Convolution Neural Networks,” International Journal of Machine Learning, 2023, Scopus/SCI.

Kafare, T.V. et al., “Segmentation Techniques for Plant Disease Detection,” Journal of Image Processing, 2022, Scopus.

Kafare, T.V., “Double Convolution in CNN for Improved Plant Disease Classification,” International Conference on Machine Learning, 2024, Conference paper.

Kafare, T.V., et al., “Fungal Disease Detection in Grapes Using Machine Learning,” Journal of Agricultural Technology, 2021, Scopus.

Conclusion

Dr. Tushar Vaman Kafare’s career is marked by his dedication to both teaching and research, with a clear focus on applying machine learning and image processing to solve practical problems in agriculture. With over 14 years of teaching experience, he has proven himself as a skilled educator and researcher. His ongoing Ph.D. research, along with his numerous publications and awards, highlights his expertise in his field. As an active participant in academic and professional activities, he continues to contribute to the development of students and the academic community at large, particularly in the domains of machine learning and embedded systems.

Amir veisi | Artificial Intelligence | Best Researcher Award

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Dr. Amir veisi | Artificial Intelligence | Best Researcher Award

PhD | Bu-Ali Sina University | Iran

Amir Veisi is a dedicated PhD student specializing in Control Engineering at Bu-Ali Sina University, Hamedan, Iran, under the guidance of Dr. Hadi Delavari. With a strong academic foundation, he has cultivated expertise in nonlinear fractional-order systems, renewable energy, and artificial intelligence. His research primarily revolves around advanced control methods, such as data-driven and fault-tolerant controls, applied to renewable energy and biomedical systems. Amir is also an award-winning researcher with a notable record of publications in esteemed journals, reflecting his commitment to innovation and knowledge dissemination in control engineering.

Profile

Scholar

Education

Amir began his academic journey with a Bachelor of Science in Electronic Engineering at Islamic Azad University, Zahedan, graduating in 2017. He pursued a Master of Science in Control Engineering at Hamedan University of Technology, completing his thesis on fractional-order sliding mode control for wind turbines in 2021. Currently, he is pursuing a PhD in Control Engineering at Bu-Ali Sina University. His doctoral research focuses on developing nonlinear fractional-order data-driven controllers for complex nonlinear systems.

Experience

Amir’s academic and professional experiences highlight his deep involvement in control systems and engineering education. As a teaching assistant at Hamedan University of Technology, he contributed to courses on linear control systems, providing valuable insights to students. Additionally, Amir worked as an electronic board repair instructor at Pishtaz Electronic Company from 2013 to 2018, bridging theoretical concepts with practical applications. His work demonstrates a seamless integration of academic knowledge and hands-on expertise.

Research Interests

Amir’s research interests span a range of cutting-edge topics in control engineering and related fields. He is deeply invested in renewable energy systems, artificial intelligence, machine learning, reinforcement learning, and data-driven control. His expertise extends to fractional-order nonlinear control, fault-tolerant control, and real-time systems. Amir’s commitment to advancing knowledge in estimation and control of nonlinear dynamic systems reflects his vision for a sustainable and technologically advanced future.

Awards

Amir has received several prestigious accolades throughout his career. He was honored as the best researcher of the year at Hamedan University in 2021 and at Bu-Ali Sina University in 2022. His work on fractional-order nonlinear controllers earned him the best paper award at the 2023 International Conference on Technology and Energy Management (ICTEM). Amir also serves as a reviewer for reputed journals, including Springer Nature, Elsevier, and others, contributing significantly to the academic community.

Publications

Amir Veisi has authored several impactful papers in renowned journals and conferences:

Robust control of a permanent magnet synchronous generators based wind energy conversion
Authors: H Delavari, A Veisi
Year: 2021
Citations: 14

Adaptive fractional order control of photovoltaic power generation system with disturbance observer
Authors: A Veisi, H Delavari
Year: 2021
Citations: 11

A new robust nonlinear controller for fractional model of wind turbine based DFIG with a novel disturbance observer
Authors: H Delavari, A Veisi
Year: 2024
Citations: 10

Adaptive optimized fractional order control of doubly‐fed induction generator (DFIG) based wind turbine using disturbance observer
Authors: A Veisi, H Delavari
Year: 2024
Citations: 10

Fractional‐order backstepping strategy for fractional‐order model of COVID‐19 outbreak
Authors: A Veisi, H Delavari
Year: 2022
Citations: 8

Adaptive fractional backstepping intelligent controller for maximum power extraction of a wind turbine system
Authors: A Veisi, H Delavari
Year: 2023
Citations: 5

Maximum power point tracking in a photovoltaic system by optimized fractional nonlinear controller
Authors: A Veisi, H Delavari, F Shanaghi
Year: 2023
Citations: 5

Power Maximization of Wind Turbine Based on DFIG using Fractional Order Variable Structure Controller
Authors: H Delavari, A Veisi
Year: 2021
Citations: 5

Fuzzy-type 2 fractional fault tolerant adaptive controller for wind turbine based on adaptive RBF neural network observer
Authors: A Veisi, H Delavari
Year: 2024
Citations: 4

Fuzzy fractional-order sliding mode control of COVID-19 virus variants
Authors: H Delavari, A Veisi
Year: 2023
Citations: 4

Conclusion

Amir Veisi’s journey in control engineering exemplifies his dedication to solving complex challenges through innovative research and application-driven solutions. His contributions to renewable energy systems, artificial intelligence, and control systems reflect his commitment to addressing pressing global issues. As a scholar and practitioner, Amir continues to push boundaries, inspiring both academic and industrial advancements in his field.