Bin Zhang l Semantic Segmentation for Autonomous Driving | Research Excellence Award

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Assoc. Prof. Dr. Bin Zhang l Semantic Segmentation for Autonomous Driving | Research Excellence Award

Kanagawa University | Japan

Assoc. Prof. Dr. Bin Zhang is an associate professor and principal investigator specializing in intelligent machines, robotics, and AI-driven systems. Education includes a Ph.D. and M.S. in Mechanical and Intelligent Systems Engineering from The University of Electro-Communications and a B.S. in Automation. Professional experience spans academia and industry, with roles in mechanical engineering education and robotics research. Research interests focus on service robots, autonomous navigation, human–robot interaction, perception, deep learning, and assistive systems. Research skills include robot design, control, SLAM, computer vision, sensor fusion, and machine learning. Awards and honors recognize excellence in robotics, AI applications, and assistive technologies. Overall, Assoc. Prof. Dr. Bin Zhang contributes impactful research and education advancing intelligent robotic systems for society.

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Featured Publications

A Study on Improving the Accuracy of Semantic Segmentation for Autonomous Driving
Computers, Materials & Continua, 2026 · Open Access
A Quantitative Diagnosis Method for Assessing the Severity of Intake Filter Blockage of Heavy-Duty Gas Turbines Based on the Fusion of Physical Mechanisms and Deep Learning
Applied Thermal Engineering, 2025

A Performance Diagnosis Method for Full Gas-Path Components of Heavy-Duty Gas Turbines Based on Model- and Data-Hybrid Drive

Proceedings of the Chinese Society of Electrical Engineering, 2025

An End-to-End Pillar Feature-Based Neural Network Improved by Attention Modules for Object Detection of Autonomous Vehicles

IEEJ Transactions on Electrical and Electronic Engineering, 2025

2D Mapping Considering Potential Occupancy Space of Mobile Objects for a Guide Dog Robot

IEEJ Transactions on Electronics, Information and Systems, 2025

Ammar Khaleel l Reinforcement Learning | Research Excellence Award

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Mr. Ammar Khaleel l Reinforcement Learning | Research Excellence Award

Széchenyi István Egyetem | Hungary

Mr. Ammar Khaleel PhD-level researcher in computer science with ongoing doctoral training, focusing on reinforcement learning–based decision making for autonomous vehicles. Experience includes designing, training, and evaluating deep reinforcement learning and control algorithms for autonomous driving, particularly lane-changing, within large-scale traffic simulations using SUMO and the TraCI Python API. Research interests span reinforcement learning, deep learning, model predictive control, intelligent transportation systems, and traffic modeling. Technical expertise covers Python, C/C++, simulation frameworks, and reproducible research workflows. Academic contributions emphasize simulation-driven experimentation and algorithmic innovation; no formal awards are listed. Overall, the work aims to advance safe, efficient, and intelligent mobility systems.

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Featured Publications


A Multi-Levels RNG Permutation

– Indonesian Journal of Electrical Engineering and Computer Science, 2019

A New Permutation Method for Sequence of Order 28

– Journal of Theoretical and Applied Information Technology, 2019

N/A and Signature Analysis for Malwares Detection and Removal

– Indian Journal of Science and Technology, 2019

Gulshan Sharma l Machine Learning and AI Applications | Research Excellence Award

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Prof. Gulshan Sharma l Machine Learning and AI Applications | Research Excellence Award

University of Johannesburg | South Africa

Prof. Gulshan Sharma is an experienced academic at the University of Johannesburg with extensive expertise in electrical engineering and power systems. She has attended multiple faculty development programs on PLC, SCADA, industrial automation, smart grids, and modern power system operations. Her research interests include renewable energy technologies, advanced power system control, machine learning applications in power systems, and automation. She has guided projects and internships on SCADA systems and industrial drives. Prof. Sharma is proficient in both undergraduate and postgraduate subjects, ranging from electrical machines and control systems to robotics and non-conventional energy sources. Her work contributes to advancing modern power engineering education and research.

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Huiming Wang | Nonlinear Control | Research Excellence Award

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Assoc. Prof. Dr. Huiming Wang | Nonlinear Control | Research Excellence Award

Chongqing University of Posts and Telecommunications | China

Assoc. Prof. Dr. Huiming Wang is an Associate Professor at the School of Automation, Chongqing University of Posts and Telecommunications, China. He earned his Ph.D. in Measurement Technique and Automation Equipment from Southeast University, following an M.S. in Control Theory and Control Engineering and a B.S. in Automation. Assoc. Prof. Dr. Huiming Wang has professional experience as a postdoctoral researcher at Nanyang Technological University, Singapore, and serves as deputy director of key laboratories and departments. His research interests focus on anti-disturbance control, robotics, servo systems, and power electronics. His research skills include robust control, sliding-mode control, disturbance observers, and intelligent electromechanical systems. Assoc. Prof. Dr. Huiming Wang has received national and international awards for innovation and best papers, reflecting his strong academic impact and leadership in automation research.

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Featured Publications

Generalized Disturbance Estimation Based Continuous Integral Terminal Sliding Mode Control for Magnetic Levitation Systems

IEEE Transactions on Automation Science and Engineering, 2025 · 20 Citations
Current-Constrained Finite-Time Control Scheme for Speed Regulation of PMSM Systems With Unmatched Disturbances

IEEE Transactions on Automation Science and Engineering, 2025 · 3 Citations
Active Disturbance Rejection with Continuous Sliding Mode Control for Lower Limb Rehabilitation Exoskeleton

Conference Paper · 0 Citations
A Current-Constrained Finite-Time Disturbance Rejection Control Scheme for Buck Converter

Conference Paper · 0 Citations
Sampled-Data Safety-Critical Control of Nonlinear Systems with Input Delay

Conference Paper · 0 Citations

Bushra Abro | Artificial Intelligence | Research Excellence Award

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Ms. Bushra Abro | Artificial Intelligence | Research Excellence Award

National Centre Of Robotics And Automation | Pakistan

Ms. Bushra Abro is a Computer and Information Engineer with a strong foundation in Electronic Engineering. She holds a Master’s degree in Computer and Information Engineering and a Bachelor’s in Electronic Engineering from Mehran University of Engineering & Technology, Pakistan, graduating top of her class. With professional experience as a Research Associate and Assistant at the National Centre of Robotics and Automation, she has contributed to projects in deep learning, computer vision, federated learning, and real-time condition monitoring. Her work has earned multiple publications and awards, including a gold medal at the All Pakistan IEEEP Student Seminar. She is passionate about advancing AI-driven technological innovation.

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Featured Publications


Towards Smarter Road Maintenance: YOLOv7-Seg for Real-Time Detection of Surface Defects

– Book Chapter, 2025Contributors: Bushra Abro; Sahil Jatoi; Muhammad Zakir Shaikh; Enrique Nava Baro; Bhawani Shankar Chowdhry; Mariofanna Milanova


Federated Learning-Based Road Defect Detection with Transformer Models for Real-Time Monitoring

– Computers, 2025Contributors: Bushra Abro; Sahil Jatoi; Muhammad Zakir Shaikh; Enrique Nava Baro; Mariofanna Milanova; Bhawani Shankar Chowdhry


Harnessing Machine Learning for Accurate Smog Level Prediction: A Study of Air Quality in India

– VAWKUM Transactions on Computer Sciences, 2025Contributors: Sahil Jatoi; Bushra Abro; Sanam Narejo; Yaqoob Ali Baloch; Kehkashan Asma


Learning Through Vision: Image Recognition in Early Education

– ICETECC Conference, 2025Contributors: Sahil Jatoi; Bushra Abro; Shafi Jiskani; Yaqoob Ali Baloch; Sanam Narejo; Kehkashan Asma


From Detection to Diagnosis: Elevating Track Fault Identification with Transfer Learning

– ICRAI Conference, 2024Contributors: Sahil Jatoi; Bushra Abro; Noorulain Mushtaq; Ali Akbar Shah Syed; Sanam Narejo; Mahaveer Rathi; Nida Maryam

Chao Li | Data Processing | Research Excellence Award

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Dr. Chao Li | Data Processing | Research Excellence Award

Qingdao Technical College | China

Dr. Chao Li is an engineering scholar and lecturer whose work bridges professional education and applied research in advanced sensing technologies. He holds a doctoral degree in engineering and completed his undergraduate studies in industrial equipment and control engineering, where he built a strong foundation in intelligent systems. Since 2019, he has focused extensively on indoor mapping and positioning, integrating theoretical innovation with engineering-driven problem-solving. His research experience includes serving as a core contributor to multiple provincial key R&D initiatives and collaborations with major technology enterprises, where he helped develop applied solutions for real-world industrial environments. He has published several SCI-indexed journal articles as a first or corresponding author and holds an invention patent that reflects the practical impact of his work. In addition to research, he is dedicated to teaching and curriculum development in professional courses, promoting hands-on learning and interdisciplinary thinking. His academic achievements demonstrate a commitment to advancing positioning technologies, enhancing industry–academia collaboration, and addressing emerging challenges in smart manufacturing and intelligent monitoring. Looking ahead, he aims to continue deepening his contributions to indoor mapping and positioning, driving innovation that supports both scientific development and technological progress.

Profile : Orcid

Featured Publications

Li, C., Chai, W., Zhang, M., Sun, Z., Shao, G., & Li, Q. (2023). “A novel visual-aided method to enhance the inertial navigation system of an intelligent vehicle in indoor environments.” IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2023.3293884

Chai, W., Li, C., & Li, Q. (2023). “Multi-sensor fusion-based indoor single-track semantic map construction and localization.” IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2022.3226821

Li, C., Chai, W., Wu, Q., Li, J., Lin, F., Li, Z., & Li, Q. (2022). “A graph optimization enhanced indoor localization method.” In 2022 International Conference on Computers, Information Processing and Advanced Education (CIPAE). https://doi.org/10.1109/cipae55637.2022.00055

Li, C., Chai, W., Yang, X., & Li, Q. (2022). “Crowdsourcing-based indoor semantic map construction and localization using graph optimization.” Sensors. https://doi.org/10.3390/s22166263

Chai, W., Li, C., Zhang, M., Sun, Z., Yuan, H., Lin, F., & Li, Q. (2021). “An enhanced pedestrian visual-inertial SLAM system aided with vanishing point in indoor environments.” Sensors. https://doi.org/10.3390/s21227428

Samaneh Saeedinia | Engineering | Best Researcher Award

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Prof.Dr. Samaneh Saeedinia | Engineering | Best Researcher Award

Iran University of Science and Technology | Iran

Dr. Samaneh Alsadat Saeedinia is an accomplished researcher and engineer specializing in Control Electrical Engineering, Computational Neuroscience, Robotics, and Artificial Intelligence. She earned her Ph.D. from Iran University of Science and Technology (IUST), where her thesis focused on designing and stabilizing intelligent decision-making systems to support the treatment of adult focal epilepsy, receiving an excellent grade. She also holds a Master’s degree in Control Electrical Engineering from IUST and a Bachelor’s degree from Imam Khomeini International University. Dr. Saeedinia has extensive experience in signal processing, machine learning, spiking neural networks, EEG and MRI data analysis, and adaptive control systems. She has contributed to numerous high-impact publications in IEEE Transactions, Scientific Reports, and other international journals. Her research interests include computational modeling, intelligent control, data fusion, robotics path planning, and neuroinformatics. Beyond academia, she has led industrial projects in electrical power systems, automation, and instrumentation. She has received multiple awards, including the Khwarizmi International Award and top ranks in academic performance. Dr. Saeedinia is also an experienced educator and mentor, guiding students in advanced control systems, neural networks, and data analysis. Her work bridges engineering, neuroscience, and artificial intelligence, aiming to develop innovative solutions for healthcare and robotic applications.

Profile : Google Scholar

Featured Publications

Saeedinia, S.A., Jahed-Motlagh, M.R., Tafakhori, A., & Kasabov, N.K. (2024). “Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine” in Scientific Reports, 14(1), 10667.

Mohaghegh, M., Saeedinia, S.A., & Roozbehi, Z. (2023). “Optimal predictive neuro-navigator design for mobile robot navigation with moving obstacles” in Frontiers in Robotics and AI, 10, 1226028.

Roozbehi, Z., Narayanan, A., Mohaghegh, M., & Saeedinia, S.A. (2024). “Dynamic-structured reservoir spiking neural network in sound localization” in IEEE Access, 12, 24596–24608.

Saeedinia, S.A., & Tale Masouleh, M. (2022). “The synergy of the multi-modal MPC and Q-learning approach for the navigation of a three-wheeled omnidirectional robot based on the dynamic model with obstacle collision” in Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

Roozbehi, Z., Narayanan, A., Mohaghegh, M., & Saeedinia, S.A. (2025). “Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks” in IEEE Access.

Decheng Li | Engineering | Best Researcher Award

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Mr. Decheng Li | Engineering | Best Researcher Award

Lanzhou University of Technology | China

Dr. Decheng Li is a dedicated scholar and researcher at the School of Automation and Electrical Engineering, Lanzhou University of Technology, China. He obtained his academic training in electrical engineering and automation, focusing on intelligent control systems, robotics, and power electronics. With extensive teaching and research experience, Dr. Li has contributed significantly to the advancement of automation technologies and intelligent systems applications in industrial environments. His research interests encompass intelligent control theory, optimization algorithms, renewable energy integration, and advanced signal processing techniques for control systems. Dr. Li has authored and co-authored numerous papers in leading international journals and conferences, reflecting his commitment to academic excellence and technological innovation. He has been involved in several national and provincial research projects, fostering collaboration between academia and industry. In recognition of his contributions, Dr. Li has received multiple academic awards and honors for his outstanding research and teaching performance. He continues to mentor graduate students and promote interdisciplinary research to solve real-world engineering challenges. Dr. Li remains committed to advancing automation and intelligent systems for sustainable industrial development and the future of smart technologies.

Profile: Orcid

Featured Publication

Liu, J., Li, D., & Chen, H. (2025). “Robust hybrid decentralized controller design for Voice Coil Actuator-Fast Steering Mirror system in high-precision optical measurements.

Somnath Nandi | Data-informed Decision Making | Best Researcher Award

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Mr. Somnath Nandi | Data-informed Decision Making | Best Researcher Award

Jadavpur University | India

Mr. Somnath Nandi is a PhD Research Scholar at the Additive Manufacturing Research Group (AMRG), CSIR-Central Mechanical Engineering Research Institute (CMERI), India, affiliated with the Academy of Scientific and Innovative Research (AcSIR). His research focuses on functionally graded material (FGM) development, mechanical and tribological characterization, and process optimization in Wire Arc Additive Manufacturing (WAAM). He has contributed to several funded projects, including the design of SS-Ni transition metals-based electrodes for green hydrogen generation, bimetallic structure development for Tata Steel, and remanufacturing of railway brake shoes. Mr. Nandi holds an M.E. in Production Management from Jadavpur University and a B.Tech in Mechanical Engineering from the Government College of Engineering and Textile Technology, Berhampore. His published works in reputed SCI-indexed journals such as Progress in Additive Manufacturing, Materials Letters, and Journal of Materials Engineering and Performance highlight his expertise in optimization, digital twins, and sustainable manufacturing. His research interests span metal additive manufacturing, tribology, hybrid manufacturing, and digital monitoring systems. A GATE-qualified engineer (2021, 2022), he is an active member of professional societies including IIM and IAENG. Mr. Nandi has authored 10+ scientific documents, achieving an h-index of 3 with over 45 citations, reflecting his growing impact in the field of advanced manufacturing.

Profiles : Orcid | Google Scholar

Featured Publications

Biswas, S., Nandi, S., Hussain, M. S., Mandal, A., & Mukherjee, M. (2025). “Influence of heat input on the interfacial characteristics of SS316L–In718 bi-metallic deposition for wire arc additive manufacturing.” Materials Letters.

Nandi, S., Biswas, S., & Mukherjee, M. (2025). “Optimization of Wire Arc Additive Manufacturing Parameters for Steel–Aluminum Bimetallic Interface: A Comparative Study of Metaheuristic and Machine Learning Approaches.” Journal of Materials Engineering and Performance, 35(6).

Ahammed, A. A. C. P., Nandi, S., Paul, A. R., Sreejith, B., MJ, J., & Mukherjee, M. (2025). “On the Characteristic Evaluation of Bimetallic Interface of Carbon Steel (EN31) and Aluminium (AA4043) for Wire Arc Additive Manufacturing.” Metals and Materials International, 1–18.

Pendokhare, D., Nandi, S., & Chakraborty, S. (2025). “A comparative analysis on parametric optimization of abrasive water jet machining processes using foraging behavior-based metaheuristic algorithms.” OPSEARCH, 1–42.

Syed Abrar Ahmed | Optical Communication | Best Researcher Award

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Mr. Syed Abrar Ahmed | Optical Communication | Best Researcher Award

Presidency University | India

Mr. Syed Abrar Ahmed is an Assistant Professor in the Department of Electronics and Communication Engineering at Presidency University, Bangalore, with over 14 years of academic experience. He holds an M.Tech in VLSI Design and Embedded Systems from Dr. Ambedkar Institute of Technology, Bangalore, under Visvesvaraya Technological University (VTU), and a B.E. in Electronics and Communication Engineering from Ghousia College of Engineering, Ramanagaram. His teaching expertise spans Network Analysis, Embedded System Design, Microcontrollers, Microprocessors, and Analog and Digital Electronics. His research contributions focus on embedded systems, Internet of Things (IoT), machine learning applications in cognitive radio, and assistive technologies for visually impaired individuals. He has published conference papers, journal articles, and a book chapter, including contributions to Scopus-indexed proceedings and reputed journals such as the International Journal of Recent Technology and Engineering. He has guided numerous undergraduate projects in microcontrollers, robotics, IoT, GSM/GPS systems, and FPGA-based designs, and has actively participated in faculty development programs, workshops, and seminars on advanced topics. His career is distinguished by dedication to academic excellence, innovation in teaching, and contributions to emerging fields in electronics and communication engineering.

Profiles : Scopus | Orcid

Featured Publications

“QML-Based Abstraction Techniques for Predictive Modeling in Banking”

“Performance analysis of a PPM-OOK based hybrid modulation scheme for Satellite-to-Underwater FSO communication”

“Decision Tree Based 5modular Redundancy Algorithm for Fault Identification in Full Adder Circuit”

“5G’s Impact on the Evolution of Smart Cities”

“Multifunctional Frequency Management With 5g Cognitive Networking”

“A Glimpse of the 5G-Enabled Modern City”

“Third Eye A Writing Aid for Visually Impaired People”