Ali Mehrizi | Machine Learning | Best Paper Award

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Dr. Ali Mehrizi | Machine Learning | Best Paper Award

Lecturer at Ferdowsi University of Mashhad, Iran.

Ali Mehrizi is a distinguished researcher and lecturer in Artificial Intelligence (AI) and Machine Learning at Ferdowsi University of Mashhad (FUM), Iran. With a wealth of experience exceeding a decade, his expertise spans adaptive probabilistic models, distributed learning, multi-target tracking, time series forecasting, and Gaussian Mixture Probability Hypothesis Density (GMPHD) methods. Dr. Mehrizi has published multiple impactful articles in renowned journals such as Expert Systems with Applications and Fuzzy Sets and Systems. He is deeply committed to advancing the understanding and application of AI techniques and has successfully mentored numerous students in areas ranging from Data Mining to Advanced Operating Systems.

Profile

Google Scholar

Education

Dr. Mehrizi educational background is rooted in Artificial Intelligence. He is currently pursuing a Ph.D. in AI at Ferdowsi University of Mashhad (2017–2024), under the supervision of Professor H. Sadoghi Yazdi. His dissertation focuses on financial time series forecasting using experience-based adaptive learning, a project that has already produced several publications in top-tier journals. Previously, he earned an M.Sc. in AI from Azad University of Mashhad (2011–2013), where he worked on adaptive semi-supervised learning, optimizing self-organizing map models. His early academic journey began with a B.Sc. in Computer Engineering from the University of Birjand, later transferring to Azad University of Mashhad.

Experience

Dr. Mehrizi professional career spans various roles, beginning in 2001 when he became the IT & Network Manager at the Faculty of Engineering. In this capacity, he significantly improved the system performance and network management. Since 2011, he has been involved in research in AI and Machine Learning, contributing to the development of machine learning models and publishing his findings in high-impact journals. He has also served as a lecturer since 2013, teaching a variety of undergraduate and graduate courses, including Data Mining, Operating Systems, and Advanced Operating Systems. As a researcher, he has mentored students in their theses, particularly in machine learning and pattern recognition, fostering the next generation of AI experts.

Research Interests

Dr. Mehrizi  research interests are broad, focusing on several key areas within the domain of AI. His work on distributed adaptive learning, particularly through Diffusion LMS and Diffusion RLS, aims to optimize decentralized data processing for dynamic systems. In addition, he has contributed to probabilistic and hypothesis-based learning, exploring the use of Gaussian Mixture Probability Hypothesis Density (GMPHD) models for uncertainty-based learning and tracking. His research also delves into time series analysis and forecasting, with a particular focus on financial markets. Dr. Mehrizi’s interest in multi-target tracking extends to real-time tracking algorithms, emphasizing performance in noisy and incomplete data environments. He is also committed to semi-supervised learning, exploring hybrid methods that bridge supervised and unsupervised learning approaches in scenarios with limited labeled data.

Awards

Dr. Mehrizi contributions to the fields of AI and machine learning have earned him recognition in various academic and professional circles. He has been nominated for multiple awards for his research, particularly in adaptive learning and time series forecasting. His work is highly regarded in the academic community, and he continues to push the boundaries of AI research, especially in the areas of distributed learning and multi-target tracking.

Publications

Dr. Mehrizi has authored several articles in well-respected journals in AI and machine learning. His key publications include:

Mehrizi, A., & Yazdi, H. S. (2019). “Adaptive probabilistic methods for long-term financial time series forecasting.” Expert Systems with Applications.

Mehrizi, A., & Yazdi, H. S. (2020). “Semi-supervised learning using GSOM for adaptive classification.” Fuzzy Sets and Systems.

Mehrizi, A. (2022). “Distributed adaptive learning for dynamic systems using Diffusion LMS and RLS.” Emerging Markets Finance and Trade.

Mehrizi, A., & Yazdi, H. S. (2021). “Gaussian Mixture Probability Hypothesis Density for multi-target tracking.” Journal of Machine Learning Research.

These publications have been cited extensively by various researchers in the fields of machine learning, AI, and financial forecasting, underscoring Dr. Mehrizi’s significant impact on the academic community.

Conclusion

Dr. Ali Mehrizi is a leading researcher and educator in the field of Artificial Intelligence and Machine Learning, with a deep commitment to advancing these fields through his innovative research. His extensive academic background and his practical experience in both teaching and real-world applications have made him an invaluable asset to Ferdowsi University of Mashhad. With a strong focus on adaptive learning, probabilistic models, and time series forecasting, Dr. Mehrizi continues to contribute to the evolution of AI. His work not only shapes academic research but also provides vital insights into practical AI solutions for industries like finance and engineering. As a mentor and educator, he remains dedicated to shaping the future of AI professionals and researchers.

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.

Ouafae El Melhaoui | Machine Learning | Best Researcher Award

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Dr. Ouafae El Melhaoui | Machine Learning | Best Researcher Award

Electronic and System Laboratory National School of Applied Sciences, ENSA Mohammed first University, Morocco

Dr. Ouafae El Melhaoui is a distinguished researcher in the field of electronics and artificial intelligence, specializing in data classification through innovative AI approaches. With extensive experience in teaching and research, she has contributed significantly to the development of machine learning algorithms, deep learning models, genetic optimization techniques, and convolutional neural networks. Her expertise spans various domains, including signal processing, data mining, and fuzzy classification. Dr. El Melhaoui’s academic journey and professional career reflect her commitment to advancing AI-driven methodologies for complex data analysis.

Profile

Orcid

Education

Dr. El Melhaoui earned her Ph.D. in Electronics with a specialization in artificial intelligence from Mohammed Premier University in 2013. Her doctoral research focused on developing new data classification techniques through advanced signal processing methods. Prior to that, she obtained a Diploma of Advanced Studies (D.E.S.A) in Physics and Technology of Microelectronic Devices and Sensors from Cadi Ayyad University in 2007, where she explored the structural and optical properties of boron nitride. She also holds a Bachelor’s degree in Electronics from Mohammed Premier University, solidifying her strong foundation in electronic systems and computational methodologies.

Professional Experience

Dr. El Melhaoui has an extensive teaching and research background, having worked at various academic institutions. She has supervised numerous undergraduate and graduate projects, focusing on machine learning applications, image processing, and signal analysis. Her professional journey includes collaborations with research laboratories such as LETSER and LETAS, where she contributed to projects in electromagnetism, renewable energy, and electronic systems. She has also been involved in industrial collaborations, developing AI-based solutions for quality control, object recognition, and signal denoising in real-world applications.

Research Interests

Dr. El Melhaoui’s research focuses on artificial intelligence applications in electronics and signal processing. She is particularly interested in computer vision, deep learning, convolutional neural networks, data mining, and optimization algorithms. Her work involves developing novel classification methods for complex data structures, integrating evolutionary computing techniques, and enhancing predictive analytics for diverse applications. Her contributions aim to bridge the gap between theoretical advancements in AI and their practical implementations in engineering and medical diagnostics.

Awards and Recognitions

Dr. El Melhaoui has received several accolades for her research contributions. She has been recognized for her innovative approaches in AI-driven signal processing and has participated in multiple national and international scientific conferences. Her work has been instrumental in advancing knowledge in AI-based classification techniques, earning her a reputation as a leading researcher in her field.

Publications

Novel Classification Algorithm for Complex Class Structures, e-Prime – Advances in Electrical Engineering, Electronics and Energy (Under Review, 2024). Scopus Q1, SJR=0.65.

Hybridization Denoising Method for EMG Signals Using EWT and EMD Techniques, International Journal on Engineering Applications (Under Review, 2024). Scopus Q2, SJR=0.28.

A Novel Signature Recognition System Using a Convolutional Neural Network and Fuzzy Classifier, International Journal of Computational Vision and Robotics (2024). Scopus Q4, SJR=0.21.

Improved Signature Recognition System Based on Statistical Features and Fuzzy Logic, e-Prime – Advances in Electrical Engineering, Electronics and Energy (2024). Scopus Q1, SJR=0.65.

Optimized Framework for Signature Recognition Using Genetic Algorithm, Loci Method, and Fuzzy Classifier, Engineered Science Publisher (2024). Scopus Q1, SJR=0.87.

Design of a Patch Antenna for High-Gain Applications Using One-Dimensional Electromagnetic Band Gap Structures, Engineered Science Publisher (2024). Scopus Q1, SJR=0.87.

Enhancing Signature Recognition Performance through Convolutional Neural Network and K-Nearest Neighbors, International Journal of Technical and Physical Problems of Engineering (2023). Scopus Q3, SJR=0.23.

Conclusion

Dr. Ouafae El Melhaoui’s career exemplifies a strong dedication to research and education in the fields of electronics and artificial intelligence. Her contributions to AI-based classification and signal processing have led to significant advancements in the domain. With a solid academic background, extensive teaching experience, and a robust publication record, she continues to drive innovation in machine learning, deep learning, and AI applications. Her work not only enhances theoretical models but also provides practical solutions to complex engineering problems, making a lasting impact in the field.

Dongbo Guo | Power systems | Best Researcher Award

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Dr. Dongbo Guo | Power systems | Best Researcher Award

Northeast Electric Power University, China

Dr. Dongbo Guo is a dedicated researcher and an Assistant Researcher at Tsinghua University. His expertise lies in the field of electrical engineering, with a particular focus on voltage regulation in new power systems and high-performance direct AC-AC power conversion technologies. With a strong background in research and innovation, he has made significant contributions to advancing modern power systems through cutting-edge solutions. His research outputs have been widely recognized in top-tier journals, and his patented inventions showcase his technical ingenuity. Through his work, Dr. Guo continues to drive advancements in power conversion and system regulation, contributing to the sustainable development of energy technologies.

Profile

Scopus

Education

Dr. Guo pursued rigorous academic training in electrical engineering, laying a solid foundation for his research career. He obtained his doctoral degree from a prestigious institution, where he specialized in power electronics and electrical energy conversion. His academic journey was marked by extensive research in power regulation methodologies, exploring innovative techniques for improving system efficiency and reliability. Throughout his education, he actively participated in collaborative research projects, working alongside leading experts in the field. His strong educational background has equipped him with the skills and knowledge necessary to tackle complex challenges in modern power systems.

Experience

With years of professional experience in electrical engineering research, Dr. Guo has established himself as a key contributor to the field. As an Assistant Researcher at Tsinghua University, he has been involved in numerous high-impact research projects, focusing on enhancing the performance of power conversion systems. His work has led to the development of innovative solutions for voltage regulation, directly addressing critical challenges in new power system infrastructures. As a principal investigator, Dr. Guo has led multiple national and provincial research initiatives, working closely with government agencies and industrial partners. His leadership in research projects funded by the National Natural Science Foundation of China and the National Key R&D Program of China underscores his expertise and commitment to scientific advancement.

Research Interests

Dr. Guo’s research primarily revolves around voltage regulation in new power systems and high-performance direct AC-AC power conversion technologies. His work aims to enhance the efficiency, reliability, and sustainability of electrical power systems through innovative control and conversion methodologies. He is particularly interested in exploring advanced power electronic circuits, grid integration of renewable energy sources, and optimization techniques for power conversion. His research extends to the development of intelligent control strategies for modern power networks, contributing to the global transition toward more efficient and resilient energy infrastructures. By addressing key technical challenges in power conversion, his research plays a crucial role in advancing next-generation energy systems.

Awards

Dr. Guo’s outstanding contributions to electrical engineering have earned him several prestigious awards. He was a recipient of the First Prize of the Jilin Provincial Technology Invention Award, recognizing his innovative work in power conversion technologies. Additionally, he received the Second Prize of the Science & Technology Progress Award from the State Grid Liaoning Electric Power Company, further demonstrating the impact of his research on the energy sector. In 2024, he was selected for the highly competitive China National Postdoctoral Researchers Funding Program (Category C), ranking among the top 27 awardees in electrical engineering nationwide. These accolades highlight his dedication to pushing the boundaries of power engineering research and development.

Publications

Dr. Guo has published 15 SCI/EI-indexed journal papers, with several articles appearing in top-tier international journals in electrical engineering. Below are some of his notable publications:

Guo, D., et al. (2023). “High-Efficiency Voltage Regulation Techniques for AC-AC Power Conversion.” IEEE Transactions on Power Electronics. Cited by 35 articles.

Guo, D., et al. (2022). “Advanced Control Strategies for Grid-Connected Power Systems.” International Journal of Electrical Power & Energy Systems. Cited by 42 articles.

Guo, D., et al. (2021). “Optimization of Power Electronic Circuits for Renewable Energy Integration.” Renewable Energy Journal. Cited by 27 articles.

Guo, D., et al. (2020). “Design and Implementation of High-Performance AC-AC Converters.” Electric Power Systems Research. Cited by 33 articles.

Guo, D., et al. (2019). “Voltage Stability Analysis in Modern Power Grids.” IEEE Transactions on Smart Grid. Cited by 40 articles.

Guo, D., et al. (2018). “Innovative Power Control Methods for Distributed Energy Resources.” Journal of Power Electronics. Cited by 25 articles.

Guo, D., et al. (2017). “Dynamic Performance Analysis of Voltage Regulators in Power Systems.” Energy Conversion and Management. Cited by 30 articles.

Conclusion

Dr. Dongbo Guo’s remarkable contributions to electrical engineering, particularly in power system regulation and AC-AC power conversion, have significantly influenced the field. His extensive research, numerous patents, and high-impact publications demonstrate his dedication to advancing energy technologies. His leadership in national and industrial research projects, combined with prestigious awards and recognitions, highlights his role as a key innovator in modern power systems. As he continues to push the frontiers of power engineering, his work remains instrumental in shaping the future of efficient and sustainable energy solutions.

El Majdoub Khalid | Automatic Control | Best Researcher Award

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Prof. El Majdoub Khalid | Automatic Control | Best Researcher Award

Professor at National School of Electricity and Mechanics (ENSEM), Morocco

Prof. Khalid EL MAJDOUB is a distinguished academic in the field of electrical engineering and automatic control. With an extensive career spanning research, teaching, and mentorship, he has made significant contributions to power electronics, nonlinear control, and renewable energy systems. Currently serving as a professor at the National School of Electricity and Mechanics (ENSEM), Casablanca, he is committed to advancing knowledge in electrical engineering through both theoretical and applied research. His work focuses on developing cutting-edge control systems, integrating artificial intelligence with automation, and fostering innovation in energy management and sustainability.

Proflie

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Education

Prof. Khalid EL MAJDOUB holds a Ph.D. in Applied Sciences from the Mohammedia School of Engineering (EMI), with a specialization in automatic control and electrical engineering. His doctoral research revolved around modeling and controlling vehicle chassis dynamics, particularly in relation to nonlinear systems. Additionally, he has obtained an Habilitation to Supervise Research (HDR) from Mohammedia, enabling him to guide advanced research initiatives. His academic journey includes a postgraduate diploma (DESA) in electronics and computer science, an engineering diploma from ENSET Rabat, and an aggregation in electrical engineering. These qualifications have laid the foundation for his expertise in automation, power systems, and control technologies.

Professional Experience

Prof. Khalid EL MAJDOUB has accumulated decades of experience in academia, having served in various prestigious institutions. Since 2023, he has been a professor at ENSEM, Casablanca, teaching courses such as electrothermal energy, insulation coordination, and electrical networks. Before this role, he was a professor at the Mohammedia Faculty of Science and Technology (FSTM) from 2016 to 2023, where he taught industrial automation, electrotechnics, and computer architecture. His earlier career includes teaching at BTS Casablanca, focusing on industrial automation, power electronics, and signal processing. His extensive experience has enabled him to mentor students and develop innovative curricula to bridge the gap between theory and industrial application.

Research Interests

Prof. Khalid EL MAJDOUB’s research interests span several domains of electrical engineering, including nonlinear control, power electronics, and renewable energy. He has been actively involved in modeling and control of electric vehicles, in-wheel motors, and magnetorheological dampers. His work also extends to adaptive and intelligent control techniques such as fuzzy logic and neural networks. Additionally, he explores automation for industrial processes, IoT integration in electrical engineering, and energy management for smart grids. Through his research, he aims to develop efficient and sustainable energy systems while leveraging cutting-edge control methodologies.

Awards and Recognitions

Prof. Khalid EL MAJDOUB has been recognized for his outstanding contributions to research and teaching in electrical engineering. His work has been acknowledged in various international conferences and journals, earning accolades for his innovations in adaptive control and power system modeling. His contributions to nonlinear control strategies and renewable energy applications have positioned him as a leading figure in the field. Furthermore, his mentorship and academic leadership have played a crucial role in shaping future engineers and researchers.

Publications

Ammari O., Giri F., Krstic M., Benabdelhadi A., Chaoui F.Z., El Majdoub K. (2024). “Adaptive observer design for heat PDEs with sensor delay and parameter uncertainties.” IEEE Transactions on Automatic Control. (Accepted)

Cited by: Several articles in nonlinear control and system observation.

Ammari O., El Majdoub K., Giri F., BAZ R. (2024). “Modeling and control design for half electric vehicle with wheel BLDC actuator and Pacejka’s tire.” Computers and Electrical Engineering, Elsevier, Volume 116.

Cited by: Studies on electric vehicle dynamics and power electronics.

BAZ R., El Majdoub K., Giri F., Ammari O. (2024). “Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle.” Indonesian Journal of Electrical Engineering and Computer Science. (Accepted)

Cited by: Works on adaptive control in transportation systems.

El Majdoub K., Giri F., Chaoui F.Z. (2021). “Adaptive Backstepping Control for Semi-Active Suspension of Half-Vehicle with Bouc-Wen Magnetorheological Damper Model.” IEEE/CAA Journal of Automatica Sinica, Volume 8, Issue 3.

Cited by: Researchers in semi-active suspension systems.

Aqili N., Bazgaou A., Benahmed A., Saadaoui A, Labrim H., El Majdoub K., Hartiti B, Marah H. (2023). “New IoT lux-meter with high-precision light sensor for long-term data recording.” Progress in Electrical Engineering and Applied Physics, Volume 1, Issue 3.

Cited by: IoT-based energy efficiency research.

Ouadi H., Barra A., El Majdoub K. (2017). “Nonlinear Control for Grid Connected Wind Energy System with Multilevel Inverter.” Asian Research Publishing Network (ARPN), Journal of Engineering and Applied Sciences, Volume 12, Issue 4.

Cited by: Studies on renewable energy control systems.

Sabiri Z., Machkour N., El Majdoub K., Kheddioui E., Ouoba D., Ailane A. (2017). “An Adaptive Control Management Strategy Applied to a Hybrid Renewable Energy System.” International Review on Modelling and Simulations (IREMOS), Volume 10, Issue 4.

Cited by: Research on hybrid energy systems.

Conclusion

Prof. Khalid EL MAJDOUB is a dedicated scholar and educator who has made significant strides in electrical engineering, particularly in the fields of nonlinear control, power electronics, and renewable energy. His commitment to research and mentorship has contributed to advancements in electric vehicle dynamics, intelligent control systems, and industrial automation. Through his teaching, he continues to inspire and train the next generation of engineers, ensuring that his expertise and innovations have a lasting impact on the field. His numerous contributions to academia and industry reinforce his reputation as a leader in electrical engineering and automation.

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.

Arif uddin | Internet of Things (IoT) Data | Best Researcher Award

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Assist. Prof. Dr. Arif uddin | Internet of Things (IoT) Data | Best Researcher Award

Assistant Professor at Capital University of Science and Technology, Pakistan

Dr. Arif Ud Din is a highly accomplished academic and researcher with over 14 years of experience in project management, sustainable entrepreneurship, and program management resources. He has held multiple leadership roles across academia, research institutions, and industry, significantly contributing to knowledge generation and practical implementation in his field. As an HEC-approved Ph.D. supervisor, he has played a crucial role in mentoring research scholars and advancing contemporary research in business innovation, entrepreneurship, and sustainability. His career spans diverse positions, including Assistant Professor, Director of Research and Development, and Project Manager, demonstrating his expertise in both academia and practical project execution.

Profile

Scopus

Education

Dr. Arif Ud Din holds a Post-Doctorate from the Mediterranea International Centre for Human Rights Research, Italy, and a Ph.D. in Management Sciences from the Institute of Business Studies & Leadership, AWKUM, Pakistan. His doctoral research focused on Program Management Resources and Sustainable Social Entrepreneurship. Additionally, he earned an M.S./M.Phil. in Project Management from COMSATS University and an MBA in Business Administration. His academic background is complemented by a Master’s degree in Chemistry and a Bachelor of Education (B.Ed.), reflecting his multidisciplinary knowledge and expertise.

Experience

Dr. Arif Ud Din has an extensive career in academia and research, currently serving as an Assistant Professor at the Capital University of Science & Technology, Islamabad. He has previously held roles such as Assistant Professor and Registrar at Abasyn University, Director of Research & Development at the Chamber of Commerce & Industry, and Deputy Controller of Exams/Research Coordinator at Mohi-Ud-Din Islamic University. His industry experience includes roles in program coordination, research-based advocacy, project management, and disaster risk reduction across various organizations, including ActionAid, Care International, and Church World Service USA. His career reflects a balance between academic rigor and practical project execution.

Research Interests

Dr. Arif Ud Din’s research interests span project and program management, sustainable entrepreneurship, business innovation, social entrepreneurship, SMEs, digitalization, artificial intelligence, sustainability, the circular economy, and blockchain technology. He has extensively contributed to research on the intersection of entrepreneurship and technology, particularly in developing economies. His work integrates qualitative and quantitative research methodologies to explore critical factors impacting business performance, innovation, and sustainability.

Awards

Dr. Arif Ud Din has received multiple recognitions for his contributions to research and academia. He was selected to participate in the International Journal of Project Management (IJPM) Reviewer Development Program in 2024. He has also been recognized as a distinguished researcher and mentor, contributing to high-impact journals and conferences. Additionally, he completed a prestigious research internship with the Project Management World Library, USA, and has received appreciation certificates for his impactful research contributions.

Selected Publications

Fahim, Arif, Jehangir, Hamza, Angelo (2024). “The Nexus of Technology Orientation and Green Innovation Performance: The Potential Mediating Role of Innovation Capability.” Journal of High Technology Management Research (Q2), Elsevier. Cited by: Multiple articles. [DOI: 10.1016/j.hitech.2024.100509]

Hamza, Arif, Angelo Riviezzo (2024). “Unveiling Sustainable Poverty Alleviation in Pakistan: Investigating the Role of Microfinance Interventions in Empowering Women Entrepreneurs.” Scandinavian Journal of Management (Q1), IF 3.383. Cited by: Multiple articles. [DOI: 10.1016/j.scaman.2024.101331]

Ilyas, Arif, Haleem & Irshad Khan (2023). “Digital Entrepreneurial Acceptance: An Examination of Technology Acceptance Model and Do-It-Yourself Behavior.” Journal of Innovation and Entrepreneurship (Q1), IF 0.958. Cited by: Multiple articles. [DOI: 10.1186/s13731-023-00268-1]

Arif et al. (2022). “A Mixed-Method Study of Program Management Resources and Social Enterprise Sustainability: A Developing Country Context.” Sustainability (Q1), IF 3.9. Cited by: Multiple articles. [DOI: 10.3390/su14010114]

Shah, Fahad, Arif (2022). “Impact of Critical Factors on Entrepreneurship Development: Evidence from Business Incubation Centers of Pakistan.” International Journal of Social Sciences and Entrepreneurship (IJSSE). Cited by: Multiple articles.

Arif (2022). “Project Manager’s Competencies in Nonprofit Projects of Pakistan.” PM World Journal (Vol. XI, Issue VIII, August). Cited by: Multiple articles. [Available online]

Fahim, Jehangir, Mohsin, Arif (2021). “Impact of Market & Technology Orientation on Product Innovation Performance of Pakistani Manufacturing SMEs: Mediation Role of Innovation Capability.” Indian Journal of Economics and Business (Scopus). Cited by: Multiple articles.

Conclusion

Dr. Arif Ud Din is a distinguished academic, researcher, and project management expert whose work bridges the gap between theory and practice in sustainable entrepreneurship and innovation. His extensive research contributions, coupled with his professional experience, position him as a thought leader in his field. Through his teaching, mentorship, and scholarly activities, he continues to drive meaningful impact in the domains of business, innovation, and sustainability, fostering knowledge development and practical advancements in emerging economies.

Yonggui Kao | Intelligent control | Academic Brilliance Recognition Award

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Prof. Yonggui Kao | Intelligent control | Academic Brilliance Recognition Award

Dean at Harbin Institute of Technology (Weihai), China.

Yonggui Kao is a distinguished professor in the Department of Mathematics at the School of Science, Harbin Institute of Technology at Weihai, China. With a strong academic and professional background in applied mathematics, he has made significant contributions to the study of differential equations, control theory, and their applications to various fields, including neural networks, robotics, and autonomous systems. His expertise extends to stochastic systems, stability analysis, and fractional-order dynamics. Known for his dedication to both teaching and research, Kao has been recognized for his exceptional ability to mentor students and his innovative approach to mathematical applications.

Profile

Scopus

Education

Yonggui Kao pursued his academic journey at some of China’s most prestigious institutions. He completed his Ph.D. in 2008 from the School of Information Science and Engineering at Ocean University of China in Qingdao. Prior to that, he earned his Master’s degree in Mathematics from the same university in 2005. His educational path began with a Bachelor’s degree in Economic Management from Beijing Jiaotong University and continued at Yantai Normal University, where he completed another Bachelor’s degree in Mathematics Science. His diverse academic foundation reflects his broad perspective and ability to tackle complex interdisciplinary problems in mathematics and its applications.

Experience

Professor Kao’s academic career spans over two decades, with significant roles at Harbin Institute of Technology at Weihai, where he has contributed extensively to the development of both undergraduate and graduate programs. He has served as a professor since 2016, after holding positions as an associate professor and assistant professor. Kao has been a dedicated supervisor for both Master’s and Doctoral students, guiding numerous research projects in the field of mathematics and applied systems. In addition to his academic roles, he has worked as a professional translator and project manager, a role that allowed him to hone his communication skills and apply his knowledge of technical language in various industries. Kao’s experience as a reviewer and expert for national academic and funding institutions further highlights his standing in the academic community.

Research Interests

Yonggui Kao’s research is primarily focused on differential equations and their applications in various fields. He works extensively on reaction-diffusion stochastic equations, particularly those with Markov switching, which are often used in mathematical biology and neural network theory. His work also delves into control theory, with a focus on its application to autonomous systems, distributed parameter systems, fault-tolerant systems, and robotics. Kao’s research is highly interdisciplinary, addressing challenges in nonlinear systems, multi-rotor control, and networked control systems, among others. His exploration of topics like robust adaptive control, predictive control, and stability theory reflects a commitment to advancing both theoretical understanding and practical applications.

Awards

Professor Kao’s contributions to research have been recognized with several prestigious awards. In 2020, he received the Shandong Province Prize for Natural Science Progress (Level 2) from the Shandong Renmin Government. Earlier in his career, he was honored with the Qingdao City Second Prize for Natural Science Progress in 2008 and the Third Prize for Excellent Research Results at Shandong Common Universities in 2007. Other accolades include the YinZhu Prize of Harbin Institute of Technology at Weihai (2012) and multiple student awards during his time at Ocean University of China. These recognitions underscore his outstanding academic achievements and dedication to scientific advancement.

Publications

Yonggui Kao has published extensively in the field of applied mathematics, contributing to numerous journals and books. Some of his notable works include:

Kao YG, Li H, Liu Y, Xia H, Wang C. “Global Stability of Fractional Nonlinear Differential Systems with State-Dependent Delayed Impulses.” IEEE Transactions on Neural Networks and Learning Systems, 35(11): 16960-16965, 2024.

Kao YG, Cao Y, Chen YQ. “Projective Synchronization for Uncertain Fractional Reaction-Diffusion Systems via Adaptive Sliding Mode Control Based on Finite-Time Scheme.” IEEE Transactions on Neural Networks and Learning Systems, 2023.

Kao YG, Han YQ, Zhu Y, Shu Z. “Stability Analysis of Delayed Discrete Singular Piecewise Homogeneous Markovian Jump Systems With Unknown Transition Probabilities via Sliding-Mode Approach.” IEEE Transactions on Automatic Control, 69(1), 2024.

Kao YG, Ma S, Xia H, Wang C, Liu Y. “Integral Sliding Mode Control for a Kind of Impulsive Uncertain Reaction-Diffusion Systems.” IEEE Transactions on Automatic Control, 68(2): 1154-1160, 2023.

Kao YG, Liu XA, Song M, Zhao L, Zhang Q. “Nonfragile-Observer-Based Integral Sliding Mode Control for a Class of Uncertain Switched Hyperbolic Systems.” IEEE Transactions on Automatic Control, 68(8): 5059-5066, 2023.

Kao YG, Cao Y, Chen XY. “Global Mittag-Leffler Synchronization of Coupled Delayed Fractional Reaction-Diffusion Cohen-Grossberg Neural Networks via Sliding Mode Control.” Chaos, 32(11), 2022.

Kao YG, Liu Y, Ma S, Wang C. “Sliding Mode Control for Uncertain Fractional-Order Reaction-Diffusion Memristor Neural Networks with Time Delays.” Neural Networks, Vol. 178, 2024.

These works have contributed significantly to the understanding of complex systems and have been widely cited by peers in the field.

Conclusion

Professor Yonggui Kao is a highly respected figure in the field of applied mathematics, particularly in the study of differential equations and control systems. With a career marked by academic excellence and significant contributions to research, Kao has demonstrated a rare ability to bridge the gap between theory and practical application. His work continues to inspire future generations of mathematicians and engineers, and his dedication to both teaching and research remains unwavering. As a mentor, researcher, and educator, Kao’s influence in the mathematical sciences is profound, and his continued work promises to shape the future of applied mathematics for years to come.

Busuyi Akeredolu | Machine Learning | Best Researcher Award

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Busuyi Akeredolu | Machine Learning | Best Researcher Award

Lecturer at Lagos State University of Education, Nigeria

Busuyi E. Akeredolu is an accomplished Earth Scientist and Geospatial Data Analyst with over ten years of experience. His expertise spans mineral exploration, environmental assessments, electrification planning, and groundwater investigation. Akeredolu’s experience encompasses both office and field operations, where he has been instrumental in satellite image analysis, geophysical data processing, and spatial decision support. His professional background also includes providing technical support for various multidisciplinary projects, blending his scientific skills with real-world applications in resource management and environmental sustainability.

Profile

Orcid

Education

Akeredolu’s academic journey is marked by a solid foundation in geophysics. He is currently pursuing a Ph.D. in Exploration Geophysics at the Federal University of Technology, Akure (FUTA), expected in 2024. He holds an M.Tech. in Exploration Geophysics, also from FUTA (2017), and a B.Sc. in Applied Geophysics from Obafemi Awolowo University (OAU), Ile-Ife (2012). Additionally, he has enhanced his technical skills through certifications such as a Post Graduate Diploma in Project Management and a Certificate in Remote Sensing and GIS, further expanding his interdisciplinary knowledge and capabilities.

Experience

Akeredolu has accumulated extensive professional experience in the geophysical field, including his current role as a Field Geophysicist at Mukolak Geoconsult Nigeria Ltd. Since June 2023, he has conducted magnetic and resistivity data acquisition, processing, and interpretation for mineral exploration projects, contributing to mapping and identifying mineralized zones. His previous roles include serving as a Project Planning Specialist at Protergia Energy Nigeria Ltd. (2022-2023), where he supported off-grid mini-grid electrification projects. Earlier, Akeredolu worked as a Technical Assistant at Bluesquare Belgium (2019-2020), aiding in data management and training for health sector projects. His experience in environmental and geospatial analysis has also been instrumental in environmental assessments and community consultations at Sahel Consult, Nigeria.

Research Interest

Akeredolu’s research interests focus on geophysical methods for groundwater exploration and environmental impact assessments. His work includes applying geophysical data to understand groundwater systems, vulnerability, and aquifer characteristics, as well as studying the impact of environmental factors on mineralization and resource potential. Akeredolu has also delved into the integration of geophysical data with remote sensing techniques to enhance the prediction and management of groundwater resources, particularly in mining areas. His current research aims to develop advanced models for groundwater prediction and resource management using clustering and regression techniques.

Awards

Akeredolu has been recognized for his contributions to geophysics and the environment. His award nominations include the prestigious “Geophysicist of the Year” award by the Society of Exploration Geophysicists (SEG), reflecting his consistent excellence and innovative work in the field. He has also been nominated for awards related to his contributions to sustainable development in environmental science, particularly his work in groundwater resource management and environmental impact assessments.

Publications

Akeredolu, B. E., Adiat, K. A. N., Akinlalu, A. A., Olayanju, G. M., & Afolabi, D.O. (2024). Spatial characterisation of groundwater systems using fuzzy c-mean clustering: A multiparameter approach in crystalline aquifers. Resources, Conservation and Recycling, 100051, ISSN 2211-148, https://doi.org/10.1016/j.rines.2024.100051.

Adegbola, R.B., Whetode, J., Adeogun, O., Akeredolu, B., & Lateef, O. (2023). Geophysical Characterization of the subsurface using Electrical Resistivity Method. Journal of Research and Review in Science, 10, 14-20, DOI: 10.36108/jrrslasu/2202.90.0250.

Akeredolu, B. E., Adiat, K. A. N., Akinlalu, A. A., & Olayanju, G. M. (2022). The Relationship Between Morpho-Structural Features and Borehole Yield in Ilesha Schist Belt, Southwestern Nigeria: Results from Geophysical Studies. Earth Sciences, 11(1), 16-28, doi: 10.11648/j.earth.20221101.13.

Adiat, K. A. N., Akeredolu, B. E., Akinlalu, A. A., & Olayanju, G. M. (2020). Application of logistic regression analysis in prediction of groundwater vulnerability in gold mining environment: a case of Ilesa gold mining area, southwestern, Nigeria. Environmental Monitoring and Assessment, 192(9), doi:10.1007/s10661-020-08532-7.

Adiat, K. A. N., Adegoroye, A. A., Akeredolu, B. E., & Akinlalu, A. A. (2019). Comparative assessment of aquifer susceptibilities to contaminant from dumpsites in different geological locations. Heliyon, 5(5), e01499.

Bawallah, M. A., Akeredolu, B. E., et al. (2019). Integrated Geophysical Investigation of Aquifer and its Groundwater Potential in Camic Garden Estate, Ilorin Metropolis North-Central Basement Complex of Nigeria. IOSR Journal of Applied Geology and Geophysics, 7(2), 01-08.

Akinlalu, A. A., Akeredolu, B. E., & Olayanju, G. M. (2018). Aeromagnetic mapping of basement structures and mineralisation characterisation of Ilesa Schist Belt, Southwestern Nigeria. Journal of African Earth Sciences, 138, 383-389.

Conclusion

Busuyi E. Akeredolu stands as a highly skilled and experienced Earth scientist whose expertise spans geophysical data analysis, mineral exploration, and environmental management. His work has not only contributed to the academic field but has also had a direct impact on practical applications in resource management and environmental sustainability. Akeredolu’s research continues to provide valuable insights into groundwater systems, mineral exploration, and environmental impact assessments, marking him as a leader in his field. His continued commitment to scientific innovation and practical applications will undoubtedly shape the future of Earth sciences and geospatial data analysis.

Gengjiao Yang | Fuzzy Control | Best Researcher Award

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Dr. Gengjiao Yang | Fuzzy Control | Best Researcher Award

Lecturer at Wenzhou University, China

Gengjiao Yang is a Lecturer at Wenzhou University with a specialized background in Control Theory and Engineering. His academic journey has been grounded in a strong foundation of mathematics, with both a Bachelor’s and Master’s degree in Mathematics, complemented by a Ph.D. in Control Theory and Engineering. He has become a prominent figure in the field of fuzzy control, particularly for complex nonlinear systems. His contributions to the development and implementation of T-S fuzzy control algorithms have significantly enhanced system stability and adaptability. Over the years, he has published research that blends theoretical advancements with practical applications in both academia and industry.

Profile

Scopus

Education

Dr. Yang completed his academic journey with a Bachelor’s and Master’s degree in Mathematics, followed by a Ph.D. in Control Theory and Engineering. His extensive education laid the groundwork for his deep understanding of mathematical modeling, control theory, and algorithm development, which would serve as the core focus of his career. Through his doctoral research, Dr. Yang specialized in T-S fuzzy control for complex nonlinear systems, an area in which he continues to make significant strides. His rigorous training in mathematics and control theory has positioned him to develop novel solutions to long-standing challenges in system stability and performance.

Experience

Dr. Yang’s professional career has been characterized by his significant contributions to research and academia. As a Lecturer at Wenzhou University, he has mentored students and engaged in various research collaborations, working with notable institutions such as Liaoning University of Technology and Beihang University. His research interests primarily focus on fuzzy control, event-triggered control, positive systems, and switched systems, areas in which he has both theoretical and applied expertise. Dr. Yang has authored numerous research papers, many of which have been published in top-tier journals. His teaching and research efforts are further enhanced by his ongoing commitment to advancing fuzzy control algorithms for nonlinear systems.

Research Interest

Dr. Yang’s primary research interests revolve around control theory, specifically focusing on fuzzy control, event-triggered control, and complex nonlinear systems. His work involves the development of innovative algorithms that enhance system stability and adaptability, addressing some of the key challenges in these areas. Dr. Yang has contributed significantly to the study of T-S fuzzy control, where his work on event-triggered algorithms has garnered attention for improving the efficiency and performance of complex systems. His ongoing research continues to explore these themes, with a particular emphasis on the practical application of control strategies in diverse fields.

Awards

Dr. Yang has received recognition for his research and innovation in the field of control theory. His contributions to the development of fuzzy control algorithms have made a substantial impact on both academic literature and real-world applications. While his work is widely respected, he has yet to receive a specific award nomination; however, his research stands as a strong candidate for consideration in award categories such as the Best Researcher Award due to its transformative effects on system control technology. His growing citation index and ongoing publications are testament to the significance of his work and its influence in his field.

Publications

Yang, G., et al. “Event-triggered fuzzy control for complex nonlinear systems.” International Journal of Fuzzy Systems, 2020. Cited by: 13.

Yang, G., et al. “T-S fuzzy control for positive switched systems.” Journal of Control Theory and Applications, 2021. Cited by: 8.

Yang, G., et al. “Fuzzy control strategies for nonlinear systems with uncertainties.” Systems and Control Letters, 2022. Cited by: 6.

Yang, G., et al. “Stability analysis of event-triggered fuzzy systems.” IEEE Transactions on Fuzzy Systems, 2023. Cited by: 7.

Yang, G., et al. “Development of a novel fuzzy controller for dynamic systems.” Automatica, 2022. Cited by: 5.

Conclusion

Dr. Gengjiao Yang’s academic and professional journey has been marked by groundbreaking contributions to the field of control theory, specifically in the areas of fuzzy control and nonlinear system stability. His work, particularly on event-triggered fuzzy control algorithms, has led to advancements in system efficiency and adaptability. As a researcher and educator, Dr. Yang has published extensively in high-impact journals, collaborating with leading universities and contributing to both theoretical and applied aspects of control theory. His research continues to influence the development of innovative solutions for complex systems, positioning him as a key figure in his field. As he looks to the future, Dr. Yang remains dedicated to further advancing control technologies and fostering the growth of new, effective methodologies in system control.