Jafar keighobadi | Automated Machine Learning (AutoML) | Best Researcher Award

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Prof. Dr. Jafar keighobadi | Automated Machine Learning (AutoML) | Best Researcher Award

Professor at Tabriz university, Iran

Dr. Jafar Keighobadi is a distinguished professor in the Faculty of Mechanical Engineering at the University of Tabriz, Iran. With a career spanning over two decades, he has made significant contributions to the fields of mechatronics, control systems, signal processing, and artificial intelligence. His expertise extends to the programming and implementation of microcontroller and microprocessor boards, reflecting a profound integration of theoretical knowledge with practical applications. Throughout his tenure, Dr. Keighobadi has been instrumental in advancing research and education, mentoring numerous students, and collaborating on projects that bridge the gap between academia and industry.

Profile

Scopus

Education

Dr. Keighobadi’s academic journey commenced with a Bachelor of Science in Mechanical Engineering, specializing in Applied Design Mechanics, from the University of Tabriz. He furthered his studies at the Amirkabir University of Technology (Tehran Polytechnic), where he earned both his Master of Science and Ph.D. in Mechanical Engineering. His doctoral research focused on “Robust Estimator Design for Stochastic Attitude-Heading Reference System in Accelerated Maneuvers,” a comprehensive study that entailed the development and extensive testing of a low-cost Attitude-Heading Reference System. This academic foundation has been pivotal in shaping his research trajectory and teaching philosophy.

Experience

Dr. Keighobadi’s professional experience is marked by a progressive academic career at the University of Tabriz, where he has served as an Assistant Professor (2008–2013), Associate Professor (2014–2020), and has held the position of full Professor since 2020. In addition to his teaching and research responsibilities, he has been a Patent Examiner at the university since 2009, overseeing the evaluation of innovative technologies and inventions. His commitment to education is further demonstrated through his roles as a lecturer at various institutions, including the Islamic Azad University branches in Khoy, Qazvin, and Maragheh, as well as Zanjan University. These roles have enabled him to disseminate knowledge across a broad spectrum of students and professionals.

Research Interests

Dr. Keighobadi’s research interests are diverse and interdisciplinary, encompassing MEMS sensors and actuators, GNSS, control systems, and Kalman filtering. He has a profound interest in autonomous robots and the design and implementation of intelligent systems. His work delves into robust filtering and control, stochastic nonlinear estimation and control, and the mathematical algorithms of chaos. A significant portion of his research is dedicated to artificial intelligence, including fuzzy logic, artificial neural networks, and deep learning. Moreover, he is adept in FPGA, DSP, and ARM programming, which underscores his commitment to integrating advanced computational techniques with mechanical engineering applications.

Awards

Throughout his illustrious career, Dr. Keighobadi has been the recipient of several accolades that recognize his contributions to research and academia. Notably, he was honored as the Best Young Researcher across all technical departments at the University of Tabriz on November 27, 2011. This award reflects his dedication to advancing engineering knowledge and his impact on the academic community. Additionally, his academic excellence was evident early in his career when he secured the second rank out of 120 candidates in the Ph.D. entrance exam at Amirkabir University of Technology on June 18, 2001. These honors underscore his commitment to excellence and innovation in his field.

Publications

Dr. Keighobadi’s scholarly output includes numerous publications in esteemed journals. A selection of his notable works includes:

“Immersion and Invariance-Based Extended State Observer Design for a Class of Nonlinear Systems,” published in the International Journal of Robust and Nonlinear Control on May 21, 2021.

“Adaptive Neural Dynamic Surface Control of Mechanical Systems Using Integral Terminal Sliding Mode,” featured in Neurocomputing on December 21, 2019.

“Adaptive Inverse Deep Reinforcement Lyapunov Learning Control for a Floating Wind Turbine,” published in Scientia Iranica on January 15, 2023.

“Decentralized INS/GPS System with MEMS-Grade Inertial Sensors Using QR-Factorized CKF,” featured in the IEEE Sensors Journal on June 1, 2017.

“INS/GNSS Integration Using Recurrent Fuzzy Wavelet Neural Networks,” published in GPS Solutions on May 21, 2020.

“Passivity-Based Hierarchical Sliding Mode Control/Observer of Underactuated Mechanical Systems,” featured in the Journal of Vibration and Control on May 19, 2022.

“Extended State Observer-Based Robust Non-Linear Integral Dynamic Surface Control for Triaxial MEMS Gyroscope,” published in Robotica on January 15, 2019.

These publications highlight Dr. Keighobadi’s extensive research in control systems, artificial intelligence, and their applications in mechanical engineering.

Conclusion

Dr. Jafar Keighobadi stands as a prominent figure in mechanical engineering, with a career dedicated to advancing research, education, and practical applications in mechatronics and control systems. His interdisciplinary approach, combining robust theoretical frameworks with hands-on implementation, has significantly impacted both academic circles and industry practices. As a mentor, researcher, and educator, Dr. Keighobadi continues to inspire and lead in the ever-evolving landscape of engineering and technology.

Jamal Raiyn | Deep Learning | Best Researcher Award

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Prof. Dr. Jamal Raiyn | Deep Learning | Best Researcher Award

Lecturer | Technical University of Applied Sciences, Aschaffenburg | Germany

Jamal Raiyn is an accomplished researcher and academic in the field of applied computer science, particularly focusing on areas such as autonomous vehicles, smart cities, data science, and cyber security. With a notable track record of publications in top-tier journals and conferences, Raiyn has established himself as a leader in the intersection of technology, transportation, and urban development. His work has contributed to advancements in intelligent transportation systems, cyber security in autonomous networks, and the integration of machine learning into traffic management.

Profile

Google Scholar

Education

Raiyn’s academic journey is marked by a strong foundation in computer science and related disciplines. He has pursued extensive education and training, equipping himself with the skills needed to address complex issues in transportation networks, autonomous systems, and cyber security. His educational background laid the groundwork for his deep involvement in research and development of cutting-edge technologies, particularly in the context of autonomous vehicles and smart cities.

Experience

Raiyn has accumulated vast experience in both academic and industry settings. Over the years, he has worked with leading researchers and institutions on multiple projects, advancing his expertise in the application of machine learning and data analytics to urban planning and transportation systems. His collaborations have included prominent industry leaders and have led to successful research outcomes, including the development of models for improving traffic safety, congestion management, and autonomous driving behavior.

Research Interests

Raiyn’s primary research interests lie in the domains of autonomous vehicle networks, smart cities, and cyber security. He focuses on the application of advanced computational techniques like machine learning, data science, and neural networks to enhance the safety, efficiency, and sustainability of transportation systems. Raiyn is particularly interested in the study of intelligent transportation systems, traffic anomaly detection, collision avoidance, and the optimization of vehicle communications over wireless networks. His research also addresses cyber security challenges, particularly within the context of autonomous vehicle communications and critical infrastructure.

Awards

Raiyn has been the recipient of numerous accolades for his contributions to applied computer science. His work has garnered recognition from prestigious academic institutions, research organizations, and professional societies. Notably, his research on intelligent traffic management and autonomous vehicle behavior prediction has been recognized with awards at international conferences, highlighting the significant impact of his work on advancing smart city technologies and autonomous transportation solutions.

Publications

Raiyn has published several influential papers in leading academic journals, contributing valuable insights into fields such as transportation, cyber security, and data science. Some of his notable publications include:

Raiyn, J., & Weidl, G. (2025). “Improvement of Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics.” Smart Cities.

Raiyn, J., Chaar, M. M., & Weidl, G. (2025). “Enhancing Urban Livability: Exploring the Impact of On-Demand Shared CCAM Shuttle Buses on City Life, Transport, and Telecommunication.”

Raiyn, J., & Weidl, G. (2024). “Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events.” Smart Cities, 7(1), 460-474.

Raiyn, J. (2024). “Maritime Cyber-Attacks Detection Based on a Convolutional Neural Network.” Computational Intelligence and Mathematics for Tackling Complex Problems, 5, Springer, pp. 115-122.

Raiyn, J., & Rayan, A. (2023). “Identifying Safety-Critical Events in Data from Naturalistic Driving Studies.” International Journal of Simulation Systems, Science & Technology, 24(1).

Raiyn, J. (2022). “Detection of Road Traffic Anomalies Based on Computational Data Science.” Discover Internet of Things, 2(6).

Raiyn, J. (2022). “Using Dynamic Market-Based Control for Real-Time Intelligent Speed Adaptation Road Networks.” Advances in Science, Technology and Engineering Systems Journal, 7(4), 24-27.

These papers have been cited by a variety of studies, underlining the relevance and impact of his research in the fields of intelligent transport, autonomous systems, and cyber security.

Conclusion

Jamal Raiyn’s research continues to push the boundaries of knowledge in the field of applied computer science, particularly within the context of transportation systems and autonomous vehicle technologies. His work has not only contributed to theoretical advancements but has also provided practical solutions to real-world challenges, including traffic safety, cyber security in autonomous networks, and the development of smart city infrastructure. Raiyn’s dedication to advancing technology for the betterment of society is evident in his continued contributions to the scientific community. His work is a testament to the profound impact that interdisciplinary research can have on shaping the future of urban living and transportation systems.