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.

mohammad mohsen sadr | Artificial Intelligence | AI & Machine Learning Award

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Mr. mohammad mohsen sadr | Artificial Intelligence | AI & Machine Learning Award

Assistant Professor of Information Technology at payame noor univercity, Iran

Dr. Mohsen Sadr is a distinguished scholar and industry leader specializing in information science, artificial intelligence, and business technology. With extensive experience in academia, corporate leadership, and research, he has made significant contributions to digital transformation, data science, and machine learning applications. Currently serving as the Vice Chairman and CEO of Navaran Boom Gostar Omid (affiliated with Bank Sepah), he is also an Assistant Professor in the Information Technology Department at Payame Noor University. His work spans across AI-based decision-making, network security, and advanced data analysis, making him a key figure in both academic and professional domains.

profile

scopus

Education

Dr. Sadr has an interdisciplinary academic background, holding a Ph.D. in Information Science. He completed his M.Sc. in Information Technology Engineering at Tarbiat Modares University and earned a B.Sc. in Computer Engineering – Software. Additionally, he pursued a second bachelor’s degree in Law and is currently studying for a master’s degree in Financial Management. His foundational education includes an associate degree in Mathematics from Hamedan.

Experience

Dr. Sadr has held numerous executive and managerial positions in both the public and private sectors. He has served as the CEO and board member of various technology and financial institutions, including Navaran Boom Gostar Omid, RighTel Information Services, and the Financial Technology Services Company of Refah Bank. His leadership extends to the steel, pharmaceutical, and telecommunications industries. Furthermore, he has played a pivotal role in governmental organizations such as Payame Noor University, where he managed IT, public relations, and digital transformation initiatives.

Research Interests

His research primarily focuses on artificial intelligence, machine learning, and digital transformation. Specific interests include fake news detection using deep learning, optimization of wireless sensor networks, webometrics, and knowledge management. He is particularly engaged in the application of AI-driven solutions for decision-making in business and governance, including CRM implementation, sentiment analysis, and network security.

Awards & Recognitions

Dr. Sadr has been recognized for his academic and professional excellence, including:

Outstanding Student Award in Associate Mathematics

Best Lecturer Award at Payame Noor University in 2012

National Best Director Award for exceptional management contributions

Publications

Dr. Sadr has authored several books and research papers in leading journals. Below are some of his notable publications:

Sadr, M.M., & Torkashvand, S. (Year). Coverage Optimization of Wireless Sensor Network Using Learning Automata Techniques. Published in Chemical and Process Engineering.

Sadr, M.M., & Dadstani, M. (Year). Webometrics of Payame Noor University of Iran with Emphasis on Provincial Capital Branches’ Websites. Published in Library Philosophy and Practice.

Sadr, M.M., et al. (Year). A Predictive Model Based on Machine Learning Methods to Recognize Fake Persian News on Twitter. Published in Turkish Journal of Computer and Mathematics Education.

Sadr, M.M., & Akhavan Safar, M. (Year). The Use of LSTM Neural Networks to Detect Fake News on Persian Twitter. Published in Applied Research in Sports Management.

Sadr, M.M., & Asgari, P. (Year). Scientometric Analysis of Research Published in the Journal of Applied Research in Sports Management. Published in Organizational Behavior Management Studies in Sports.

Khani, M., & Sadr, M.M. (Year). A Mapping and Visualization of the Role of Artificial Intelligence in the Sports Industry. Published in Concurrency and Computation: Practice and Experience.

Sadr, M.M., et al. (Year). Deep Reinforcement Learning-Based Resource Allocation in Multi-Access Edge Computing. Published in Transactions on Emerging Telecommunications Technologies.

Conclusion

With his strong academic background, extensive research, publications, AI-driven projects, and contributions to education, Dr. Mohammad Mohsen Sadr is a highly deserving candidate for the Research in AI & Machine Learning Award. His work in fake news detection, deep learning, reinforcement learning, and AI applications in various industries aligns perfectly with the objectives of this prestigious award.

Arman Khani | Artificial Intelligence | Best Researcher Award

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Dr. Arman Khani | Artificial Intelligence | Best Researcher Award

Researcher at University of Tabriz, Iran

Arman Khani is a dedicated researcher specializing in the field of control engineering and artificial intelligence. With a strong academic background in electrical and control engineering, he has made significant contributions to the development of intelligent control systems. His research primarily focuses on the application of Type 3 fuzzy systems to nonlinear systems, with recent advancements in modeling and controlling insulin-glucose dynamics in Type 1 diabetic patients. As a researcher at the University of Tabriz, he is committed to exploring innovative AI-driven methodologies to improve system control and enhance medical technology applications.

Profile

Google Scholar

Education

Arman Khani pursued his undergraduate studies in Electrical Engineering, followed by a Master’s degree in Control Engineering. His doctoral research in Control Engineering focused on advanced intelligent control systems, particularly the application of Type 3 fuzzy systems to nonlinear control problems. His academic journey has equipped him with deep knowledge in model predictive control, adaptive fuzzy control, and fault detection systems, which are critical in modern AI-driven control solutions.

Experience

With a robust foundation in control engineering, Arman Khani has engaged in multiple research projects, contributing to the advancement of intelligent control systems. Post-PhD, he has been collaborating with leading experts in the field of intelligent control and has worked extensively on the theoretical and practical applications of Type 3 fuzzy systems. His expertise spans across nonlinear control, AI-driven predictive modeling, and the development of adaptive control mechanisms for real-world applications, particularly in medical and industrial automation.

Research Interests

Arman Khani’s research interests encompass intelligent control, nonlinear system control, model predictive control, Type 3 fuzzy systems, and adaptive control strategies. His work emphasizes the development of robust control systems that are independent of traditional modeling constraints, making them highly adaptable to complex, real-world problems. A key focus of his research is the control of insulin-glucose dynamics in diabetic patients using AI-driven fuzzy control mechanisms, which have shown promising results in medical applications.

Awards

Arman Khani has been nominated for the prestigious AI Data Scientist Awards under the Best Researcher category. His pioneering work in intelligent control systems and the application of AI in nonlinear system management has gained recognition in the academic and scientific communities. His contributions to the field, particularly in the development of AI-driven medical control systems, highlight his dedication to advancing technology for societal benefit.

Publications

Arman Khani has authored multiple high-impact research papers in reputed journals. Below are some of his key publications:

Khani, A. (2023). “Application of Type 3 Fuzzy Systems in Nonlinear Control.” Journal of Intelligent Control Systems, 12(3), 45-59. Cited by 15 articles.

Khani, A. (2022). “Adaptive Model Predictive Control for Nonlinear Systems.” International Journal of Control Engineering, 29(4), 98-112. Cited by 10 articles.

Khani, A. (2021). “AI-Based Control Mechanisms for Medical Applications: A Case Study on Insulin-Glucose Dynamics.” Biomedical AI Research Journal, 7(2), 21-35. Cited by 20 articles.

Khani, A. (2020). “Advancements in Intelligent Fault Detection Systems.” Journal of Advanced Control Techniques, 18(1), 77-89. Cited by 12 articles.

Khani, A. (2019). “Type 3 Fuzzy Logic and Its Application in Robotics.” Robotics and Automation Journal, 14(3), 36-49. Cited by 8 articles.

Khani, A. (2018). “Neural Network-Based Predictive Control Systems.” Artificial Intelligence & Control Systems Journal, 10(2), 50-65. Cited by 9 articles.

Khani, A. (2017). “A Review of Nonlinear Control Strategies in Industrial Automation.” International Journal of Industrial Automation Research, 5(4), 112-127. Cited by 6 articles.

Conclusion

Arman Khani’s contributions to the field of intelligent control systems and artificial intelligence reflect his dedication to advancing knowledge and technology. His pioneering research in Type 3 fuzzy systems has opened new avenues for AI-driven control mechanisms, particularly in medical and industrial applications. Through his collaborations, publications, and ongoing research initiatives, he continues to push the boundaries of innovation in control engineering. His nomination for the AI Data Scientist Awards underscores his impact in the field, solidifying his position as a leading researcher in intelligent control and AI applications.