Yingchun Niu l Data Processing | Research Excellence Award

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Mr. Yingchun Niu l Data Processing | Research Excellence Award

Hebei University | China

Mr. Yingchun Niu is a researcher in intelligent information processing with a doctoral background in Control Science and Engineering and strong expertise in artificial intelligence and computer vision. He holds a Ph.D., M.S., and B.S. in engineering and computing-related disciplines and has professional experience as a researcher and faculty member in cyberspace security and computer science. His research interests include point cloud semantic segmentation, uncertainty-aware learning, big data analytics, and visual understanding. His research skills cover deep learning, weakly supervised learning, 3D vision, model uncertainty estimation, and SCI journal publishing, with scholarly recognition demonstrated through high-impact international publications contributing to trustworthy and efficient intelligent perception systems.

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Featured Publications

Weakly Supervised Point Cloud Semantic Segmentation with the Fusion of Heterogeneous Network Features

– Image and Vision Computing, 2024
Beyond Accuracy: More Trustworthy Weakly Supervised Point Cloud Semantic Segmentation with Primary–Auxiliary Structure

– Computers & Electrical Engineering, 2024
Weakly Supervised Point Cloud Semantic Segmentation Based on Scene Consistency

– Applied Intelligence, 2024
Neighborhood Spatial Aggregation MC Dropout for Efficient Uncertainty-Aware Semantic Segmentation in Point Clouds

– IEEE Transactions on Geoscience and Remote Sensing, 2023
Beyond-Skeleton: Zero-Shot Skeleton Action Recognition Enhanced by Supplementary RGB Visual Information

– Expert Systems with Applications, 2025

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.

Citation Metrics (Scopus)

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328

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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

Sibel Cevik Bektas l Energy Management/Optimization | Research Excellence Award

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Dr. Sibel Cevik Bektas l Energy Management/Optimization | Research Excellence Award

Karadeniz Technical University | Turkey

Dr. Sibel Cevik Bektas is an electrical and electronics engineering researcher specializing in energy systems and renewable integration. Education includes undergraduate, postgraduate, and doctoral degrees in electrical engineering from Karadeniz Technical University. Professional experience centers on academic research, peer-reviewed publications, conference contributions, and leadership of a TUBITAK-funded project. Research interests cover optimal energy management, power systems, load forecasting, solar irradiance prediction, and data-driven optimization. Research skills include machine learning, deep learning, time-series forecasting, optimization, MATLAB modeling, and power system analysis. Awards and honors include competitive national research funding. Overall, Dr. Sibel Cevik Bektas contributes impactful, applied solutions advancing energy systems.

Citation Metrics (Scopus)

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Featured Publications

Scenario-Based Data-Driven Approaches for Short-Term Load Forecasting
Energy and Buildings, 2026

Helmi Ayari | Data processing | Research Excellence Award

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Mr. Helmi Ayari | Data processing | Research Excellence Award

Université Ibn khaldoun | Tunisia

Helmi Ayari is a doctoral researcher in Artificial Intelligence at the École Polytechnique de Tunisie, focusing on machine learning, deep learning, and intelligent imaging systems, with a research track record that includes 3 published documents, an h-index of 1, and citations from 33 documents. He holds a Master of Research in Intelligent Systems for Imaging and Computer Vision and a fundamental Bachelor’s degree in Computer Science. His work includes contributions to medical image analysis, explainable AI, and optimization techniques, with notable publications such as a comparative study of classical versus deep learning-based computer-aided diagnosis systems published in Knowledge and Information Systems (Q2), and a study integrating genetic algorithms with ensemble learning for enhanced credit scoring presented at the International Conference on Business Information Systems (Class B). Professionally, he has served as a Maître Assistant and teaching assistant, delivering courses in machine learning, deep learning, Python, R, computer architecture, and automata theory, while supervising Master’s research, coordinating academic programs, and contributing to hackathons and university events. His research interests span AI-driven decision systems, interpretable machine learning, evolutionary optimization, and applied deep learning. Recognized for both academic and pedagogical engagement, he continues advancing impactful AI research and education.

Profile : Scopus

Featured Publications

Computer-Aided Diagnosis Systems: A Comparative Study of Classical Machine Learning Versus Deep Learning-Based Approaches. Knowledge and Information Systems, published May 23, 2023. https://doi.org/10.1007/s10115-023-01894-7

Integrating Genetic Algorithms and Ensemble Learning for Improved and Transparent Credit Scoring. International Conference on Business Information Systems, June 25, 2025. (Class B)

Aditta Chowdhury | IoT (Internet of Things) Analytics | Research Excellence Award

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Mr. Aditta Chowdhury | IoT (Internet of Things) Analytics | Research Excellence Award

Chittagong University of Engineering and Technology | Bangladesh

Aditta Chowdhury is an electrical and electronic engineering researcher and academic whose work focuses on advancing biomedical signal processing, embedded systems, and sustainable energy technologies. He holds both a Bachelor of Science and a Master of Science in Electrical and Electronic Engineering from the Chittagong University of Engineering and Technology, graduating with top academic distinction. His professional journey includes teaching and research roles in reputed engineering institutions, where he has contributed to curriculum development, laboratory instruction, and collaborative research. His research experience spans FPGA-based biomedical signal processing, photoplethysmogram-driven cardiovascular and metabolic disease detection, multimodal physiological signal analysis, plasmonic biosensing, microgrid feasibility assessment, and low-power VLSI system design. He has published extensively in high-impact journals and international conferences, demonstrating expertise in hardware–software co-design, machine learning applications in healthcare, and emerging sensor technologies. His work has resulted in several innovative solutions, including cardiovascular disease classifiers, hypertension detection systems, EOG-based eye-movement processors, and microgrid sustainability assessments. He has received multiple academic scholarships and awards recognizing his exceptional academic performance and research accomplishments. Driven by a commitment to impactful scientific contribution, he aims to continue developing intelligent, efficient, and accessible engineering solutions through interdisciplinary research and innovation.

Profile : Google Scholar

Featured Publications

Chowdhury, A., Das, D., Eldaly, A.B.M., Cheung, R.C.C., & Chowdhury, M.H. (2024). “Photoplethysmogram-based heart rate and blood pressure estimation with hypertension classification.” IPEM–Translation, 100024.

Joy, J.D., Rahman, M.S., Rahad, R., Chowdhury, A., & Chowdhury, M.H. (2024). “A novel and effective oxidation-resistant approach in plasmonic MIM biosensors for real-time detection of urea and glucose in urine for monitoring diabetic and kidney disease.” Optics Communications, 573, 131012.

Chowdhury, A., Das, D., Hasan, K., Cheung, R.C.C., & Chowdhury, M.H. (2023). “An FPGA implementation of multiclass disease detection from PPG.”
IEEE Sensors Letters, 7(11), 1–4.

Islam, M.A., Chowdhury, A., Jahan, I., & Farrok, O. (2024). “Mitigation of environmental impacts and challenges during hydrogen production.”
Bioresource Technology, 131666.

Chowdhury, A., Das, D., Cheung, R.C.C., & Chowdhury, M.H. (2023). “Hardware/software co-design of an ECG–PPG preprocessor: A qualitative and quantitative analysis.”
Proceedings of the 2023 International Conference on Electrical, Computer and Communication Engineering, 9.

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

Daxiong Ji | Data Analysis | Research Excellence Award

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Dr. Daxiong Ji | Data Analysis | Research Excellence Award

Zhejiang University | China

Dr. Daxiong Ji is an Associate Professor at the Institute of Marine Electronics and Intelligent Systems, Ocean College, Zhejiang University, and a Senior Member of IEEE. He earned his Ph.D. in Pattern Recognition and Intelligent Systems from the University of Chinese Academy of Sciences and a B.S. in Automation from Wuhan Polytechnic University. Dr. Ji has held visiting scholar positions at the University of Melbourne, University of Plymouth, and University of Victoria, collaborating internationally on advanced marine robotics research. His expertise spans automatic control, artificial intelligence applications, autonomous marine robotics, electrical engineering, and intelligent systems. He has supervised numerous undergraduate, master’s, and doctoral students, leading courses on Marine Robot Design, Marine Intelligent Systems, and Professional Practices. Dr. Ji has received multiple honors, including the First Prize of Zhejiang Provincial Teaching Achievement Award, recognition as an Excellent Moral Education Mentor, and awards for scientific and technological achievements from the Chinese Academy of Sciences. He has led and participated in numerous national and international research projects, focusing on autonomous navigation, fault diagnosis, and data-driven control of underwater vehicles. Dr. Ji is also an accomplished inventor with multiple patents in underwater robotics and autonomous systems. His research has been widely published in top journals, reflecting significant contributions to marine intelligent systems.

Profiles : Orcid | Google Scholar

Featured Publications

Ji, D.; Ogbonnaya, S.G.; Hussain, S.; Hussain, A.F.; Ye, Z.; Tang, Y.; Li, S. Three-Dimensional Dynamic Positioning Using a Novel Lyapunov-Based Model Predictive Control for Small Autonomous Surface/Underwater Vehicles. Electronics, 2025, 14(3), 489.

Xu, L.; Ji, D. Online Fault Diagnosis Using Bioinspired Spike Neural Network. IEEE Transactions on Industrial Informatics, 2024.

Zhou, J.; Ye, Z.; Zhao, J.; Ji, D.; Peng, Z.; Lu, G.; Tadda, M.A.; Shitu, A.; Zhu, S. Multi-detector and Motion Prediction-Based High-Speed Non-Intrusive Fingerling Counting Method. Biosystems Engineering, 2024, 218, 1–15.

Ji, D.; Wang, R. Path Following of QAUV Using Attitude-Velocity Coupling Model. Ocean Engineering, 2024, 293, 116885.

Ji, D.; Cheng, H.; Zhou, S.; Li, S. Dynamic Model-Based Integrated Navigation for a Small and Low-Cost Autonomous Surface/Underwater Vehicle. Ocean Engineering, 2023, 273, 114091.

Xiaoxia Yu | Data Analysis Innovation |  Best Scholar Award  

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Mr. Xiaoxia Yu | Data Analysis Innovation |  Best Scholar Award  

Chongqing University of Technology | China

Dr. Xiaoxia Yu is a scholar in mechanical engineering whose work advances intelligent diagnostics and predictive maintenance for large-scale rotating machinery, particularly wind turbines. With a Ph.D. in Mechanical Engineering and earlier degrees in Vehicle Engineering and Armored Vehicle Engineering, she has built a strong interdisciplinary foundation that integrates mechanical systems knowledge with advanced computational modeling. Her research spans fault diagnosis, health assessment, digital twin systems, graph neural networks, reinforcement learning, and signal processing, supported by a growing publication record that includes 29 documents, 477 citations by 448 documents, and an h-index of 7. As a Lecturer, she leads research projects funded by regional scientific agencies and has contributed to national-level R&D initiatives related to machinery health management. Her work appears in high-impact journals, and she has secured patents focused on structural health monitoring, image recognition, and intelligent fault detection. Recognized with competitive grants and academic honors, she continues to influence the fields of renewable energy reliability and smart manufacturing. Through her commitment to innovation, research leadership, and engineering application, she is emerging as a key contributor to the development of intelligent, data-driven mechanical health monitoring systems.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Yu, X., Tang, B., & Zhang, K. (2021). Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks. IEEE Transactions on Instrumentation and Measurement, 70, 1–14.

Yu, X., Tang, B., & Deng, L. (2023). Fault Diagnosis of Rotating Machinery Based on Graph Weighted Reinforcement Networks Under Small Samples and Strong Noise. Mechanical Systems and Signal Processing, 186, 109848.

Zhang, K., Tang, B., Deng, L., & Yu, X. (2021). Fault Detection of Wind Turbines by Subspace Reconstruction‑Based Robust Kernel Principal Component Analysis. IEEE Transactions on Instrumentation and Measurement, 70, 1–11.

Li, B., Tang, B., Deng, L., & Yu, X. (2020). Multiscale Dynamic Fusion Prototypical Cluster Network for Fault Diagnosis of Planetary Gearbox Under Few Labeled Samples. Computers in Industry, 123, 103331.

Xiong, P., Tang, B., Deng, L., Zhao, M., & Yu, X. (2021). Multi‑block Domain Adaptation with Central Moment Discrepancy for Fault Diagnosis. Measurement, 169, 108516.

Umme Habiba | Machine Learning and AI Applications | Research Excellence Award

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Mrs. Umme Habiba | Machine Learning and AI Applications | Research Excellence Award

North Dakota State University(NDSU) | United States

Umme Habiba is a dedicated researcher and educator in computer science whose work centers on machine learning, deep learning, transformer-based architectures, explainable AI, and swarm intelligence, particularly within medical data analysis. She is pursuing her Ph.D. and M.Sc. in Computer Science at North Dakota State University, where she has gained extensive teaching experience as an instructor of record and graduate teaching assistant across several undergraduate courses. Her research contributions span clinical text mining, medical risk prediction, brain–computer interface modeling, IoT security, and usability analysis of mHealth applications, with publications in reputable international journals. She has also collaborated on projects involving mobile sensor–based spatial analysis, voice-controlled IoT automation systems, and hybrid ML models for network intrusion detection. Her academic journey began with a bachelor’s degree in Computer Science and Engineering, where she developed a strong foundation in data-driven system design and intelligent applications. She has received consistently high teaching evaluations and has demonstrated a commitment to interdisciplinary research and student learning. Her long-term goal is to contribute impactful solutions at the intersection of artificial intelligence and healthcare while advancing as both a researcher and an educator.

Profile : Orcid

Featured Publication

PSO-optimized TabTransformer architecture with feature engineering for enhanced cervical cancer risk prediction, 2026

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.