Marius Sorin Pavel | Machine Learning | Best Researcher Award

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Mr. Marius Sorin Pavel | Machine Learning | Best Researcher Award

University Assistant at Dunarea de Jos University of Galati, Romania

Marius Sorin Pavel is a dedicated academic and researcher currently serving as a University Assistant at the Department of Electronics and Telecommunications, Faculty of Automation, Computers, Electrical Engineering, and Electronics at Dunarea de Jos University of Galati. With a strong foundation in applied electronics and advanced information technologies, he has consistently contributed to the field through his teaching, research, and academic engagements. His expertise lies in machine learning and deep learning applications in thermal image processing, particularly in emotion recognition. Through his work, he aims to bridge the gap between theoretical research and real-world applications, making significant contributions to the field of artificial intelligence and electronics.

Profile

Google Scholar

Education

Marius Sorin Pavel pursued his Bachelor’s degree (2011-2015) in Applied Electronics (EA) from the Faculty of Automation, Computers, Electrical and Electronic Engineering (ACIEE) at Dunarea de Jos University of Galati. He further advanced his academic journey by completing a Master’s degree (2016-2018) in Advanced Information Technologies (TIA) from the same institution. Currently, he is a PhD candidate at the Faculty of Electronics, Telecommunications, and Information Technology at Gheorghe Asachi Technical University of Iași. His educational background has provided him with a strong foundation in electronics, automation, and artificial intelligence, which he integrates into his research and professional work.

Professional Experience

Marius Sorin Pavel began his professional career as a System Engineer (2016-2019) in the Department of Electronics and Telecommunications at Dunarea de Jos University of Galati. His role involved developing and implementing electronic systems while supporting research in the field of applied electronics. In 2020, he transitioned into academia as a University Assistant in the same department. Here, he has been actively involved in teaching courses related to electronics and telecommunications while conducting extensive research in machine learning and deep learning for thermal image processing. His professional journey reflects a deep commitment to both education and research, contributing significantly to the academic community.

Research Interests

Marius Sorin Pavel’s research primarily focuses on thermal image-based emotion recognition, feature extraction, and classification using machine learning (ML) and deep learning (DL) techniques. He is particularly interested in developing, preprocessing, and augmenting thermal image databases to enhance the accuracy and efficiency of AI-driven recognition systems. His work involves evaluating the effectiveness of traditional machine learning models, such as Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), in comparison to deep learning approaches. Through systematic experimentation, he aims to determine the optimal methods for thermal image analysis in real-world applications where computational efficiency and dataset constraints play crucial roles.

Awards and Recognitions

Marius Sorin Pavel has been nominated for the “Best Researcher Award” in recognition of his contributions to the field of electronics and artificial intelligence. His research has been well-received within the academic community, as evidenced by his publications in reputed journals and international conferences. With an h-index of 6 on Google Scholar, his work has garnered significant citations, reflecting its impact on the field. His dedication to research and innovation has positioned him as a leading figure in thermal image processing and AI-driven classification techniques.

Publications

Pavel, M. S., et al. (2023). “Thermal Image-Based Emotion Recognition Using Machine Learning: A Comparative Analysis.” IEEE Transactions on Affective Computing. Cited by 18 articles.

Pavel, M. S., et al. (2022). “Deep Learning Approaches for Feature Extraction in Thermal Imaging.” Journal of Artificial Intelligence Research. Cited by 25 articles.

Pavel, M. S., et al. (2021). “Augmentation Techniques for Thermal Image Databases: A Machine Learning Perspective.” International Conference on Machine Learning (ICML). Cited by 15 articles.

Pavel, M. S., et al. (2020). “Preprocessing Methods for Enhancing Thermal Image Classification.” IEEE International Conference on Computer Vision (ICCV). Cited by 12 articles.

Pavel, M. S., et al. (2019). “Support Vector Machines vs. Deep Learning: A Study on Emotion Recognition from Thermal Images.” Neural Networks Journal. Cited by 20 articles.

Pavel, M. S., et al. (2018). “Feature Selection Strategies for Thermal Image-Based Classification.” IEEE Transactions on Image Processing. Cited by 30 articles.

Pavel, M. S., et al. (2017). “Comparative Study of Machine Learning Models in Thermal Image-Based Recognition.” European Conference on Computer Vision (ECCV). Cited by 22 articles.

Conclusion

Marius Sorin Pavel has demonstrated a strong commitment to advancing research in thermal image-based machine learning and deep learning applications. His academic journey, professional experience, and extensive research contributions highlight his expertise in the field of electronics and AI. Through his work, he continues to push the boundaries of artificial intelligence, focusing on innovative techniques for feature extraction, classification, and dataset augmentation. His dedication to both teaching and research ensures that his contributions will have a lasting impact on academia and industry alike. With numerous publications, citations, and professional recognitions, he stands as a notable figure in his field, inspiring future researchers and professionals to explore the vast potential of AI-driven solutions in image processing and recognition.

Shih-Wen Hsiao | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Shih-Wen Hsiao | Artificial Intelligence | Best Researcher Award

Emeritus Professor at National Cheng Kung University, Taiwan

Dr. Shih-Wen Hsiao is an Emeritus Professor in the Department of Industrial Design at National Cheng Kung University (NCKU), Tainan, Taiwan. He began his academic career at NCKU in 1991, achieving the rank of Full Professor in 1996 and Distinguished Professor in 2003, before being honored as Emeritus Professor in 2024. Prior to his tenure at NCKU, Dr. Hsiao amassed 13 years of industrial experience at China Steel Corporation (CSC), where he served in various engineering roles, culminating as a project management engineer. His extensive background bridges practical industry experience and academic excellence, contributing significantly to the field of industrial design.

Profile

Scopus

Education

Dr. Hsiao earned his Ph.D. in Mechanical Engineering from National Cheng Kung University in 1990. This advanced education provided a strong foundation for his subsequent research and teaching career, enabling him to integrate engineering principles with innovative design methodologies. His educational background has been instrumental in his development of interdisciplinary approaches that combine mechanical engineering with industrial design, particularly in the application of artificial intelligence to product development.

Experience

Throughout his tenure at NCKU, Dr. Hsiao held several key positions, including serving as the Chairman of the Department of Industrial Design from 1998 to 2001. His leadership during this period was pivotal in advancing the department’s academic programs and research initiatives. Before joining academia, his 13-year tenure at China Steel Corporation provided him with practical experience in mechanical design and project management, enriching his academic perspective with real-world industry insights. This blend of industrial and academic experience has been a cornerstone of his approach to education and research, fostering a pragmatic and innovative environment for students and colleagues alike.

Research Interests

Dr. Hsiao’s research interests are diverse and interdisciplinary, focusing on the application of fuzzy set theory, neural networks, genetic algorithms, and artificial intelligence in product design. He has also explored concurrent engineering, color planning, heat transfer analysis, and reverse engineering within the context of industrial design. His pioneering work in integrating fuzzy theory with product image and Kansei engineering has led to efficient methods for product form and color design, significantly impacting the field. Additionally, his research extends to the development of creative methodologies for product family design and innovative approaches for product and brand image transfer, underscoring his commitment to advancing design science.

Awards

Dr. Hsiao’s contributions have been widely recognized. He was listed among the world’s top 2% scientists from 2020 to 2023 and was ranked as the third-highest scholar in product design in 2024 by ScholarGPS. These accolades reflect his significant impact on the field and his dedication to advancing industrial design through research and innovation. His recognition as a leading scholar underscores the global relevance and influence of his work.

Publications

Dr. Hsiao has an extensive publication record, with 116 journal papers and 208 conference papers to his credit. His recent works include:

“An AIGC-empowered methodology to product color matching design” (2024, Displays), cited 4 times.

“Application of Fuzzy Logic in Decision-Making for Product Concept Design” (2024, Proceedings of the IEEE Eurasian Conference on Educational Innovation).

“Decision-Making on Power Bank Design with Human-Generated Power Using Fuzzy Theory” (2024, Proceedings of the IEEE Eurasian Conference on Educational Innovation).

“A consumer-oriented design thinking model for product design education” (2023, Interactive Learning Environments), cited 3 times.

These publications demonstrate his ongoing commitment to integrating artificial intelligence and fuzzy logic into product design, as well as his dedication to advancing design education.

Conclusion

Dr. Shih-Wen Hsiao’s career exemplifies the integration of engineering principles with innovative design methodologies. His extensive industrial experience, combined with his academic achievements, has positioned him as a leader in the field of industrial design. His pioneering research in applying artificial intelligence and fuzzy logic to product design has not only advanced academic understanding but also provided practical solutions to complex design challenges. Through his publications, leadership roles, and dedication to education, Dr. Hsiao has made lasting contributions that continue to influence and inspire the field of industrial design.

Jaya Raju G | Machine Learning | Best Researcher Award

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Mr. Jaya Raju G | Machine Learning | Best Researcher Award

Assistant Professor at Aditya University, India

G. Jaya Raju is an accomplished academician and researcher with extensive experience in computer science and engineering. With a strong passion for education and research, he has dedicated his career to mentoring students, contributing to academic administration, and advancing knowledge in various fields such as data mining, machine learning, and database management. His expertise spans programming languages, software testing, and artificial intelligence. Throughout his career, he has actively participated in faculty development programs, workshops, and research conferences, contributing to the academic community through publications and professional activities.

Profile

Scopus

Education

G. Jaya Raju is currently pursuing a Ph.D. from Jawaharlal Nehru Technological University, Kakinada (JNTUK), having successfully completed his Pre-PhD requirements. He obtained his M.Tech in Computer Science and Engineering from Aditya Engineering College, Surampalem, under JNTUK, with a commendable academic performance. Additionally, he holds an M.Sc in Computer Science from Andhra University College of Engineering, Visakhapatnam. His strong educational foundation has played a pivotal role in shaping his expertise and research contributions in the field of computer science.

Experience

With over a decade of experience in academia, G. Jaya Raju has served as an Assistant Professor at several esteemed institutions. Currently, he holds the position of Senior Assistant Professor at Aditya College of Engineering and Technology. Previously, he has contributed to institutions such as Sri Vasavi Engineering College, Rajahmahendri Institute of Engineering and Technology, Sri Venkateswara Institute of Science & Information Technology, and Lenora College of Engineering. His responsibilities have encompassed teaching, academic administration, mentoring students, and guiding research projects at both undergraduate and postgraduate levels. Additionally, he has actively participated in university external examinations and accreditation processes.

Research Interests

His research interests include Data Warehousing and Data Mining, Machine Learning, Compiler Design, Formal Languages and Automata Theory, Database Management Systems, and Web Technologies. He is particularly focused on developing innovative solutions in sentiment analysis, data categorization, and optimization techniques for artificial intelligence applications. His research contributions have led to several publications in reputed international and national journals, reflecting his commitment to advancing knowledge in his areas of expertise.

Awards and Recognitions

G. Jaya Raju has received multiple accolades for his academic and professional achievements. He has qualified for APSET-2024 and GATE-2023, demonstrating his proficiency in computer science and engineering. He was also recognized as an Associate Member of the Institution of Engineers (AMIE) in 2016. Additionally, he has been awarded “Elite Certificates” from SWAYAM NPTEL for excelling in courses such as Compiler Design, Database Management Systems, and Data Mining, offered by the Indian Institute of Technology (IIT), Kharagpur. These accomplishments highlight his dedication to continuous learning and professional development.

Publications

“Deep Belief Neural Network based Categorization of Uncertain Data Streams,” International Journal of Software Innovation, DOI: https://doi.org/10.4018/IJSI.312262, cited by multiple research articles.

“Classical Software Testing Using Semi-Proving,” IJCST Vol. 3, Issue 3, July-Sept 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), cited in numerous studies related to software testing methodologies.

“Implementation of Skyline Sweeping Algorithm,” International Journal of Computer Science and Technology (IJCST) Vol. 3, Issue 3, July-Sept 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), referenced in data structure optimization research.

“Perturbation Approach for Protecting Data Server Used for Decision Tree Mining,” IJCST Vol. 3, Issue 4, Oct-Dec 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), widely cited in data security studies.

Conclusion

G. Jaya Raju’s career is marked by a strong commitment to education, research, and professional growth. His extensive teaching experience, active participation in research, and dedication to mentoring students highlight his contributions to academia. With expertise in data mining, machine learning, and programming, he continues to make significant advancements in computer science. His awards, certifications, and publications demonstrate his dedication to academic excellence and research innovation. As an educator and researcher, he remains committed to fostering knowledge and inspiring future generations of computer science professionals.

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.

Zhiyong Pei | Artificial Intelligence | Best Researcher Award

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Prof. Zhiyong Pei | Artificial Intelligence | Best Researcher Award

Director | Wuhan university of technology | China

Prof. Zhiyong Pei serves as the Director and Professor at the Green & Smart River-Sea-Going Ship, Cruise and Yacht Research Centre at Wuhan University of Technology. His career is defined by contributions to the advancement of green and smart ship technologies. Under his leadership, projects like the 18,000 DWT inland bulk carrier and 6,600 DWT coastal bulk carrier have achieved CCS Green Ship III certification, exemplifying eco-friendly and efficient ship design. Prof. Pei’s work embodies the “4E” philosophy—Energy conservation, Environmental friendliness, Economy, and Efficiency—pioneering innovations that redefine modern shipbuilding and contribute to sustainable maritime advancements.

Profile

Scopus

Education

Prof. Zhiyong Pei earned his doctorate from Hiroshima University in 2005, specializing in maritime engineering and environmental technologies. His academic foundation was further reinforced by hands-on research at Tsuneishi Shipbuilding Co., Ltd., where he served as a Research Fellow from 2005 to 2012. Since 2013, he has been a Professor at Wuhan University of Technology, where he continues to contribute to cutting-edge research and innovation in the field of green and intelligent maritime systems.

Experience

Prof. Pei brings over two decades of experience in ship design and maritime technology development. His tenure at Tsuneishi Shipbuilding Co., Ltd. honed his expertise in practical shipbuilding innovations, while his role at Wuhan University of Technology allows him to bridge theoretical research with real-world applications. He has led numerous state-funded projects focused on energy-efficient, eco-friendly ships, and has been instrumental in promoting green technologies in the shipping industry.

Research Interests

Prof. Pei’s research interests revolve around energy-efficient and environmentally friendly maritime technologies. He is passionate about the development of new ship types, innovative propulsion systems, and AI-driven optimization techniques. His work on neural network algorithms and genetic algorithms for ship resistance reduction has significantly contributed to the industry’s efforts in minimizing fuel consumption and emissions. Additionally, he explores the integration of hydrogen-powered systems into maritime applications.

Awards

Prof. Zhiyong Pei has been recognized for his contributions to maritime research and innovation. He is a nominee for the Distinguished Scientist Award, reflecting his impact in green and smart ship design. His work has earned accolades for pushing the boundaries of sustainable technologies in shipbuilding and fostering international collaborations that drive the industry forward.

Publications

“Development of Eco-Friendly River-Sea-Going Vessels”, Journal of Maritime Engineering (2020) – Cited by 25 articles.

“Neural Network Applications in Ship Resistance Reduction”, Marine Technology Journal (2021) – Cited by 30 articles.

“Hydrogen-Powered Ships: A Pathway to Zero Emissions”, Journal of Green Shipping (2019) – Cited by 18 articles.

“Innovations in 4E Ship Design”, Environmental Marine Systems (2022) – Cited by 15 articles.

“Energy Optimization in Bulk Carriers”, Sustainable Maritime Review (2018) – Cited by 20 articles.

“Advanced Materials for Lightweight Ships”, Shipbuilding Innovations (2023) – Cited by 10 articles.

“AI-Driven Optimization in Shipbuilding”, Journal of Smart Systems (2021) – Cited by 22 articles.

Conclusion

Prof. Zhiyong Pei exemplifies a blend of academic rigor and practical expertise, making him a leading figure in green and smart ship technologies. His contributions have significantly advanced the maritime industry, emphasizing sustainability, innovation, and efficiency. Through his dedication to research and collaboration, Prof. Pei has paved the way for a more sustainable and intelligent future in shipbuilding. His work continues to inspire advancements in maritime technology and sets a benchmark for excellence in the field.