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.

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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.

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Featured Publications

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

Mmasechaba Lebogang Moropane l Environmental Sciences | Research Excellence Award

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Ms. Mmasechaba Lebogang Moropane l Environmental Sciences | Research Excellence Award

 University of the Western Cape | South Africa

Ms. Mmasechaba Lebogang Moropane is a Doctoral Researcher in Environmental and Water Sciences at the University of the Western Cape with an MSc (Summa Cum Laude) and BSc Honours in the same field. Her education integrates remote sensing, hydrology, geospatial analysis, machine learning, and big data analytics. Professionally, she serves as an Earth Science Departmental Research Assistant, supporting teaching and contributing to peer-reviewed journals, policy-oriented reports, and international conferences. Her research interests focus on groundwater-dependent ecosystems, non-perennial rivers, land degradation, and food–water security in semi-arid regions, employing advanced satellite analytics, AI, and XAI. She has received recognition through high-impact publications, peer-review roles, and national and international research collaborations, positioning her as a promising early-career researcher with strong interdisciplinary impact.

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Featured Publications

International Journal of Applied Earth Observation and Geoinformation, 2026
Mmasechaba L. Moropane; Cletah Shoko; Timothy Dube; Dominic Mazvimavi

Imran Rehan l Data Classification | Best Researcher Award

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Assist. Prof. Dr. Imran Rehan l Data Classification | Best Researcher Award

Gomal University | Pakistan

Assist. Prof. Dr. Imran Rehan is a physicist specializing in biophotonics with a Ph.D. in Physics and a postdoctoral focus on AI in healthcare. With over a decade of teaching and research experience, Dr. Imran Rehan integrates Raman spectroscopy, LIBS, fluorescence spectroscopy, and phase-lock-in polarimetry with machine learning to develop noninvasive, label-free diagnostic tools for diseases such as diabetes, cancer, and kidney disorders. His research emphasizes clinical spectroscopy, biomarker discovery, and optical instrumentation development. Dr. Imran Rehan has published extensively, contributed to book chapters, designed advanced spectroscopic systems, and received honors including the International Outstanding Scientist Award and Best Researcher of the Year, demonstrating impactful interdisciplinary innovation.

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Featured Publications

Machine learning assisted laser spectroscopy for simultaneous multiclass diabetes detection from blood plasma
– Microchemical Journal, 2026
Cardiovascular risk prediction in diabetes: a hybrid machine learning approach
– Biomedical Physics Engineering Express, 2025
A CatBoost and ExtraTrees-based softvoting ensemble approach for non-invasive diabetes detection using hair LIBS spectral data
– Microchemical Journal, 2025
Synthesis and characterization of CNTs doped polymeric composites: comparative studies on exploring impact of CNT concentration on morphological, structural, thermokinetic and mechanical attributes
– Zeitschrift Fur Physikalische Chemie, 2025
Application of Laser Spectroscopy and Machine Learning for Diagnostics of Uncontrolled Type 2 Diabetes
– Applied Spectroscopy, 2025

Sooyoun Lee l Data Analysis Software | Women Researcher Award

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Dr. Sooyoun Lee l Data Analysis Software | Women Researcher Award

National Institute of Health | South Korea

Dr. Sooyoun Lee is a Research Assistant Professor at Ajou University’s Bio-AI Center, specializing in bioinformatics, biomedical science, and computational biology. She holds a PhD in Bioinformatics from Seoul National University and an M.S. in Biomedical Science from Ajou University. Her research focuses on multi-omics data analysis, cancer genomics, precision medicine, and AI-driven drug repositioning. Dr. Lee has extensive experience in bioinformatics tool development, transcriptome analysis, and proteogenomics, with numerous high-impact publications and patents. Her work bridges computational methods and medical applications, advancing personalized healthcare solutions and integrative approaches in biomedical research.

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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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Deme Hirko l Water Engineer | Research Excellence Award

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Dr. Deme Hirko l Water Engineer | Research Excellence Award

 Jimma University | Ethiopia

Dr. Deme Hirko is a motivated, results-oriented civil engineer with a PhD (defended November 2025, Stellenbosch University) and over 10 years of academic and research experience in water resources engineering, hydrology, and climate change modelling. His education spans a PhD in Civil Engineering, an MSc in Water Resources and Irrigation Engineering, and a BSc in Hydraulic and Water Resources Engineering. Professionally, Dr. Deme Hirko has served as lecturer, teaching assistant, hydrology analyst, site engineer, and departmental coordinator. His research interests include climate-resilient water allocation, hydrological modelling, and AI applications. He possesses strong skills in machine learning, WEAP, Python, GIS, and HPC, has supervised numerous theses, received institutional recognition for leadership, and is committed to advancing sustainable, data-driven water management through postdoctoral research.

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Wei Zhang | Statistical Methods | Women Researcher Award

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Dr. Wei Zhang | Statistical Methods | Women Researcher Award

University of Tasmania | Australia

Dr. Wei Zhang is a Senior Lecturer and Course Coordinator at the Centre for Maritime and Logistics Management, Australian Maritime College, University of Tasmania. He holds a Ph.D. in Maritime Studies from Nanyang Technological University, Singapore, an M.E. in Transportation Planning & Management, and a B.E. in International Shipping Management from Dalian Maritime University, China. Dr. Zhang’s research focuses on maritime clusters, port development, logistics management, sustainable maritime transport, Industry 4.0 applications, smart port technologies, and hydrogen supply chains. He has led and contributed to multiple national and international research projects, including studies on offshore wind energy integration, maritime cyber security, and port resilience. He has supervised several doctoral and honours research projects, resulting in publications in high-impact journals such as International Journal of Hydrogen Energy, Maritime Economics and Logistics, and Transportation Research Part A. Dr. Zhang has received recognition for his Ph.D. thesis and service excellence at AMC, and he actively contributes to professional committees, including the International Association of Maritime Economists and Women in Logistics and Transport. With 17 documents, 483 citations, and an h-index of 11, his work significantly advances evidence-based maritime logistics management and policy development. Dr. Zhang continues to integrate research, teaching, and industry collaboration to promote sustainable and resilient maritime and logistics systems globally.

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Featured Publications


The relationship between household debt and the Big Five personality traits


– Journal of Behavioral and Experimental Economics, 2025 | Contributors: Taehyun Lee; Almas Heshmati

Quantifying the air-rail intermodal fares based on dynamic connection times


– Transport Policy, 2025 | Contributors: Xu Zhang; Ketao Sun; Vera Zhang

Analyzing Regional Disparities in China’s Green Manufacturing Transition


– Sustainability, 2025 | Contributors: Xuejuan Wang; Qi Deng; Riccardo Natoli; Li Wang; Wei Zhang; Catherine Xiaocui Lou

Container Liner Shipping System Design Considering Methanol-Powered Vessels


– Journal of Marine Science and Engineering, 2025 | Contributors: Zhaokun Li; Xinke Yu; Jianning Shang; Kang Chen; Xu Xin; Wei Zhang; Shaoqiang Yu

Opportunities and Challenges of Hydrogen Ports: An Empirical Study in Australia and Japan


– Hydrogen, 2024 | Contributors: Peggy Shu-Ling Chen; Hongjun Fan; Hossein Enshaei; Wei Zhang; Wenming Shi; Nagi Abdussamie; Takashi Miwa; Zhuohua Qu; Zaili Yang

Shaymaa Sorour | Artificial Intelligence | Best Researcher Award

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Dr. Shaymaa Sorour | Artificial Intelligence | Best Researcher Award

King Faisal University | Saudi Arabia

Dr. Shaymaa E. Sorour is an Assistant Professor of Computer Science specializing in Artificial Intelligence, Machine Learning, Deep Learning, and Optimization, with a strong focus on educational technologies and intelligent learning systems. She earned her Ph.D. in Computer Science from Kyushu University, Japan (2016), following an M.Sc. in Computer Education and a B.Sc. in Computer Teacher Preparation with honors. Dr. Sorour has extensive academic experience across teaching, research, quality assurance, and academic advising, serving in faculty roles at King Faisal University, Saudi Arabia, and Kafrelsheikh University, Egypt. Her research integrates data mining, learning analytics, student performance prediction, adaptive and intelligent educational systems, and technology-enhanced learning, with publications in leading international journals and conferences. She has actively contributed to global scholarly communities through sustained participation in IEEE, LNCS, and international education and AI venues. Her scholarly impact includes 486 citations, 49 documents, and an h-index of 11, reflecting consistent contributions to AI in education and learning analytics (486 citations by 441 documents). Among her recognitions, she received a Best Paper Award at an international conference. Dr. Sorour’s work continues to bridge advanced computational intelligence with practical, scalable educational innovation, supporting data-driven decision-making and improved learning outcomes worldwide.

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