Dauda Adenusi | Industrial Automation | Best Researcher Award

Published on by Electrical Engineering

Mr. Dauda Adenusi | Industrial Automation | Best Researcher Award

Lecturer | Atiba University | Nigeria

Dr. Dauda Adeite Adenusi is a Lecturer in the Department of Computing Science at Atiba University, Ibadan, and a cybersecurity scholar with expertise spanning cyber situational awareness, network intrusion detection, applied cryptography, and intelligent security systems. He holds a Bachelor’s degree and a Master’s degree in Computer Science and is currently completing a doctoral program in Computer Science, with advanced training in cybersecurity and data-driven security analytics. Dr. Adenusi has extensive academic and professional experience across universities and polytechnics, where he has served in teaching, research, academic leadership, and administrative roles, including Head of Department, program coordinator, accreditation committee chair, and student project supervisor. The author has received 7 citations across 2 scholarly documents, with an h-index of 1.

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Featured Publications

Information and Communication Technology (ICT) and Rural Development in Nigeria
I. O. Ebo, B. M. Amosa, D. A. Adenusi – International Journal of Science and Advanced Technology (10 citations)

Challenges and Way Out of Cyber Security Issues in Nigeria
D. A. Adenusi, A. U. Adekunle, O. O. Odewale – International Conference of Villanova Polytechnic (6 citations)

ICT in Education Among Higher Education Students
D. A. Adenusi, A. A. Adebayo, B. O. Oni – Villanova Journal of Science, Technology and Management (6 citations)

Development of Cyber Situation Awareness Model
D. Adenusi, B. K. Alese, B. M. Kuboye, A. F. B. Thompson – International Conference on Cyber Situational Awareness (5 citations)

Development of Threats Detection Model for Cyber Situation Awareness
A. D. Adenusi, E. C. Ayeleso, A. K. Kawonise, J. B. Ekuewa, A. A. Adebayo – ICONSEET Proceedings (4 citations)

Qiaoning Yang | Signal & Image Processing | Best Researcher Award

Published on by Electrical Engineering

Assoc. Prof. Dr. Qiaoning Yang | Signal & Image Processing | Best Researcher Award

Associate Professor | Beijing University of Chemical Technology | China

Qiaoning Yang is an Associate Professor at the College of Information Science, Beijing University of Chemical Technology, with expertise spanning control science and engineering, signal and information processing, image processing, deep learning, and computer vision. She earned her doctoral degree with a specialization in control science and engineering and has developed a sustained academic career combining teaching, research, and applied innovation within a leading technological institution. Her contributions have advanced the integration of signal processing, image analysis, and computer vision into real-world engineering solutions across industry and applied technology domains. She is a professional member of the China Society of Image and Graphics and is recognized for her sustained research excellence, interdisciplinary innovation, and commitment to advancing intelligent engineering systems, with a scholarly impact reflected by 436 citations, an h-index of 8, and an i10-index of 7.

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Featured Publications

Deep convolution neural network-based transfer learning method for civil infrastructure crack detection
Q. Yang, W. Shi, J. Chen, W. Lin – Automation in Construction (221 citations)
Human posture recognition and fall detection using Kinect V2 camera
Y. Xu, J. Chen, Q. Yang, Q. Guo – Chinese Control Conference (41 citations)
Real-time comprehensive image processing system for detecting concrete bridges crack
W. Lin, Y. Sun, Q. Yang, Y. Lin – Computers and Concrete (15 citations)

Zhenghua Qian | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Prof. Dr. Zhenghua Qian | Machine Learning | Research Excellence Award

Professor | Nanjing University of Aeronautics and Astronautics | China

Professor Zhenghua Qian is a distinguished scholar in solid mechanics and aerospace engineering, serving as a Professor at the College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, and a core member of the State Key Laboratory of Mechanics and Control of Aerospace Structures, with expertise spanning wave propagation, piezoelectric and smart structures, structural health monitoring, and machine learning–assisted nondestructive evaluation. The candidate’s scholarly impact is evidenced by 1,877 citations across 219 publications, with an h-index of 24.

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Zhengying Cai | Electric Vehicles | Best Researcher Award

Published on by Electrical Engineering

Prof. Zhengying Cai | Electric Vehicles | Best Researcher Award

Prof | China Three Gorges University  | China

Zhengying Cai is a Professor in the College of Computer and Information Technology at China Three Gorges University, specializing in artificial intelligence and quantum computing, with a strong academic and research profile that underpins his nomination for this award. He earned his bachelor’s, master’s, and doctoral degrees from Huazhong University of Science and Technology with advanced specialization in computer and information technologies, establishing a solid foundation for interdisciplinary research. His professional experience encompasses academic leadership, supervision of graduate researchers, and active involvement in high-impact research projects, alongside contributions to curriculum development and institutional research advancement. His research focuses on artificial intelligence methodologies, quantum computing frameworks, and their applications, resulting in a substantial body of scholarly output, including 38 research documents that have received 224 citations and reflect an h-index of 9, demonstrating both productivity and sustained academic impact.

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View Scopus Profile View ORCID Profile

Featured Publications

An Artificial Plant Community Algorithm for Collision-Free Multi-Robot Aggregation
Z. Cai, Q. Yu, Z. Lu, Z. Liu, G. Gong – Applied Sciences (Switzerland)

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications
S. Eggen, T.J. Espe, K. Grude, M. Risstad, R. Sandberg – Journal of Economic Surveys

Forecasting Implied Volatilities of Currency Options with Machine Learning Techniques and Econometric Models
A. Olsen, G. Djupskås, P.E. de Lange, M. Risstad – International Journal of Data Science and Analytics

Semih Beycimen | Robotics & Autonomous Systems | Research Excellence Award

Published on by Electrical Engineering

Mr. Semih Beycimen | Robotics & Autonomous Systems | Research Excellence Award

Professor | International Telecommunication Union | Turkey

Semih Beycimen is a university lecturer at Istanbul Technical University with expertise in robotics, autonomous systems, and AI-driven vehicle technologies, supported by a strong academic background that includes a bachelor’s and master’s degree in mechanical engineering from Bursa Uludag University and a PhD in aerospace from Cranfield University, where he specialized in AI-based control, terrain traversability, and advanced sensing. His professional experience spans roles as a mechanical technology engineering expert, research assistant, and research fellow, contributing to projects involving vibration analysis, image processing, robotic system development, predictive maintenance, digital twin modelling, and autonomous navigation for ground and indoor robotic platforms. He has also played key roles in projects integrating radar and LiDAR sensing, developing indoor navigation algorithms, and advancing autonomous vehicle perceptual frameworks, supported by robust programming skills and extensive training in deep learning, ROS, and computational modelling. His professional profile is strengthened by multiple certifications, strong organizational and communication skills, and active engagement in research dissemination. At least line: 40 citations, 5 documents, and an h-index of 2.

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Featured Publications

 

Dr. Angel Sapena Bano | Modelling Machines for Optimization | Research Excellence Award

Published on by Electrical Engineering

Dr. Angel Sapena Bano | Modelling Machines for Optimization | Research Excellence Award

Associate Professor | Universitat Politecnica de Valencia | Spain

Ángel Sapena Bañó, Profesor Titular at the Universitat Politècnica de València, is a specialist in electrical engineering with expertise in electrical machines, diagnostic methods, numerical modelling, and condition monitoring. He holds degrees in Industrial Engineering, Energy Technology for Sustainable Development, and Secondary Education, complemented by a doctorate in Industrial Engineering focused on advanced diagnostic techniques for electrical machines. His professional trajectory includes roles as Lecturer, Researcher, and Technical Specialist, contributing to major academic initiatives, laboratory modernization, and collaborative research activities. He has participated in multiple competitive and industrial R&D projects, developed fault-diagnosis tools for induction machines and wind-energy systems, and strengthened international cooperation through research stays and Erasmus teaching engagements. His research spans analytical and hybrid modelling, finite-element methods, machine-learning-based diagnostics, and real-time simulation, reflected in numerous high-impact journal articles, conference contributions, book chapters, and patented inventions. He has led and co-led research outputs as first and corresponding author, supervised a wide range of graduate projects, and contributed to organizing scientific conferences and special issues. His distinctions include recognized research merits, invited reviewer roles in indexed journals, participation in prominent research groups, and involvement in impactful national and international scientific initiatives. His scholarly record includes 1,035 citations, 60 documents, and an h-index of 17.

Profiles: Scopus | ORCID

Featured Publications

Ángel Sapena Bañó*, Model-based diagnostic techniques for induction machines under transient operational conditions. Int. J. Electr. Power Energy Syst., Accepted.

Ángel Sapena Bañó*, Hybrid FEM–analytical modelling framework for efficient fault detection in eccentric induction motors. Sensors, 2025, 25, 1–28.

Ángel Sapena Bañó, Deep learning–enhanced condition monitoring strategies for electrical machines operating in variable regimes. Mathematics and Computers in Simulation, 2025, 1–28.

Ms. Keenjhar Ayoob | Reliability Engineering | Best Researcher Award

Published on by Electrical Engineering

Ms. Keenjhar Ayoob | Reliability Engineering | Best Researcher Award

PhD Scholar | National university of sciences and technology | Pakistan

Dr. Keenjhar Ayoob is a PhD Scholar at the National University of Sciences and Technology (NUST), College of Electrical and Mechanical Engineering, specializing in Mechatronics and Robotics. He holds advanced degrees in Mechatronics Engineering with a focus on robotic systems and reliability engineering. His academic and professional experience includes research and collaboration with the National Center of Robotics and Automation (NCRA) and UESTC (China), where he has contributed to projects on robotic manipulator design, reliability modeling, and control optimization. Dr. Ayoob’s research centers on time-dependent reliability analysis, surrogate modeling, and intelligent optimization for enhancing the precision and torque efficiency of robotic systems. He has authored publications in SCI and Scopus-indexed journals including AIP Advances, PLOS ONE, and Engineering Proceedings (MDPI), and serves as a reviewer for the Journal of Mechanical Science and Technology (JMST). An IEEE Student Member, he is recognized for his innovative hybrid MRSM–GWO framework for torque optimization and Gaussian process-based learning models for adaptive robotic control. His ongoing work advances the integration of reliability engineering and machine learning to support adaptive and precise industrial automation applications.

Profile: ORCID

Featured Publications

Keenjhar Ayoob*, Reliability and torque optimization of robotic manipulators using hybrid MRSM–GWO framework. AIP Advances, Accepted.

Keenjhar Ayoob*, Surrogate modeling and intelligent optimization for adaptive trajectory control in robotic systems. PLOS ONE, Published.

Keenjhar Ayoob, Gaussian process-based learning models for time-dependent reliability analysis of robotic manipulators. Engineering Proceedings (MDPI), Published.