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

Published on by Research Data Analysis

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)

800
600
400
200
0

Citations
328

Documents
76

h-index
9

Citations

Documents

h-index


View Scopus Profile

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

Published on by Research Data Analysis

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)

40
30
20
10
0

Citations
13

Documents
5

h-index
2

Citations

Documents

h-index


View Scopus Profile

Featured Publications

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

Bushra Abro | Artificial Intelligence | Research Excellence Award

Published on by Research Data Analysis

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.

View Orcid Profile

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

Shaymaa Sorour | Artificial Intelligence | Best Researcher Award

Published on by Research Data Analysis

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.

Citation Metrics (Scopus)

800
600
400
200
0

Citations
486

Documents
49

h-index
11

Citations

Documents

h-index



View Scopus Profile
View Orcid Profile
View Google Scholar Profile

Featured Publications

Razi Al-Azawi | Image Processing | Research Excellence Award

Published on by Research Data Analysis

Prof. Dr. Razi Al-Azawi | Image Processing | Research Excellence Award

University of Technology | Iraq

Prof. Dr. Razi Al-Azawi is a distinguished academic and researcher specializing in informatics, artificial intelligence, and advanced computational systems. He holds dual B.Sc. degrees in Laser and Optoelectronics Engineering from the University of Technology and Mathematical Sciences from Mustansiriah University, an M.Sc. in Modeling and Simulation from the University of Technology, and a PhD in Informatics from Kharkov National University of Radio Electronics. Over a career spanning more than two decades, he has taught a wide range of postgraduate and undergraduate courses, including image processing, deep learning, AI, modeling and simulation, information theory, optimization, probability, web design, and advanced programming. His research interests encompass machine learning, data mining, medical image analysis, laser engineering, cybersecurity, and advanced computational modeling. He has supervised over 50 undergraduate theses and numerous postgraduate dissertations at the Higher Diploma, M.Sc., and PhD levels. His scholarly output includes 16 documents, 264 citations by 257 documents, and an h-index of 6. He has served as a reviewer for several international journals and conferences and holds multiple patents under processing. Through his academic leadership and scientific contributions, he continues to advance innovation in computer science, AI, and engineering domains.

Profiles : Scopus |

Featured Publications

Helmi Ayari | Data processing | Research Excellence Award

Published on by Research Data Analysis

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)

Konstantinos Kaselouris | Smart Cities and Urban Analytics | Research Excellence Award

Published on by Research Data Analysis

Mr. Konstantinos Kaselouris | Smart Cities and Urban Analytics | Research Excellence Award

National Technical University of Athens | Greece

Konstantinos-Eirinaios Kaselouris is a civil engineer and research associate specializing in transportation planning and engineering, with a strong focus on traffic engineering, road safety, driver behavior, and statistical and machine-learning modeling. He holds a Diploma in Civil Engineering from the National Technical University of Athens, where he completed a thesis examining the influence of mobile phone use on driving behavior through advanced machine-learning techniques applied to imbalanced naturalistic driving data. His professional experience includes work as a junior civil engineer and site inspector at Attiko Metro S.A., followed by research activities within NTUA’s Department of Transportation Planning and Engineering. He has contributed to major European research projects such as ESRA3 and ERSOnext, and has authored or co-authored conference papers presented at significant international transportation forums including the Transport Research Arena and the International Congress on Transportation Research. His research interests center on improving road safety through data-driven methodologies, crash hotspot identification, and behavioral analytics. He has earned distinctions throughout his academic career, including the 2024 Thomaideio Award for innovative work on crash hotspot identification. His ongoing goal is to advance safe, sustainable mobility through rigorous scientific research and practical engineering solutions.

Profile : Scopus

Featured Publications

Kaselouris K., Folla K., Yannis G. “Monitoring national road safety strategies in the EU” in Proceedings of the 10th Transport Research Arena (TRA), 15–18 April 2024, Dublin, Ireland.

Kaselouris K., Michelaraki E., Katrakazas C., Kallidoni M., Yannis G. “Investigating the influence of mobile phone use on driving behaviour with machine learning analysis” in Proceedings of the 10th Transport Research Arena (TRA), 15–18 April 2024, Dublin, Ireland.

Kaselouris K., Deliali K., Michelaraki E., Dragomanovits A., Yannis G. “A novel methodology for crash hotspot identification and network-wide safety ranking” in Proceedings of the 11th International Congress on Transportation Research, 20–22 September 2023, Heraklion, Greece.

Kaselouris K., Nikolaou D., Laiou A., Yannis G. “E-survey of road users’ attitudes – ESRA3” in 7th UN Global Road Safety Week, Athens, 19 May 2023.

Fan Xiao | Data Processing | Research Excellence Award

Published on by Research Data Analysis

Assoc. Prof. Dr. Fan Xiao | Data Processing | Research Excellence Award

Sun Yat-sen University | China

Xiao Fan is an Associate Professor and PhD advisor at the School of Earth Sciences and Engineering, Sun Yat-sen University, recognized for his contributions to mineral exploration, mathematical geoscience, and data-driven Earth system analysis. He received his bachelor’s and doctoral degrees in mineral resource exploration from China University of Geosciences (Wuhan), with joint doctoral training at the University of Ottawa. His research focuses on mineral system modeling, multi-physics numerical simulation, fractal and singularity analysis, machine-learning-based mineral prospectivity, and knowledge-driven geoscientific data integration. As a core member of major national innovation teams, he has led or participated in several national and provincial research projects and contributed to the creation of an advanced UAV-based geophysical–geochemical acquisition and processing platform. He has published 54 documents with 986 citations from 706 sources and holds an h-index of 19, along with multiple patents, software copyrights, and highly cited works in leading SCI journals. He serves on editorial boards and scientific committees, including roles as youth editor and guest editor, and has organized sessions for the International Geological Congress. His work advances quantitative mineral prediction and computational geoscience, contributing impactful models and methods that enhance understanding of mineralization processes and exploration efficiency.

Profiles : Scopus | Orcid

Featured Publications

(2025). “Three-Dimensional Prospectivity Modeling of Jinshan Ag-Au Deposit, Southern China by Weights-of-Evidence.” Journal of Earth Science.

(2025). “Lithologic mapping of intermediate-acid intrusive rocks in the eastern Tianshan Gobi Desert using machine learning and multi-source data fusion.” Earth Science Frontiers.

(2025). “A DFT study on mechanisms of indium adsorption on sphalerite (100), (110), and (111) surfaces: Implications for critical metal mineralization.” Ore Geology Reviews.

(2025). “Data-driven expeditious mapping and identifying granites in covered areas via deep machine learning: A case study on the implications for geodynamics and mineralization of Eastern Tianshan.” Lithos.

(2025). “Numerical Modeling and Exploration Data Coupled-driven Mineral Prospectivity Mapping: A Case Study of Fankou Pb-Zn Deposit.” Geotectonica et Metallogenia.

Arun Kumar | Mathematical Modeling | Best Researcher Award

Published on by Research Data Analysis

Dr. Arun Kumar | Mathematical Modeling | Best Researcher Award

IIT Mandi | India

Dr. Arun Kumar is a Research Associate (Postdoctoral) at the Indian Institute of Technology Mandi, specializing in mathematical and computational modeling of complex biological, ecological, and epidemiological systems. He earned his Ph.D. in Mathematics from Banaras Hindu University in 2023, following an M.Sc. in Mathematics from the same institution and a B.Sc. from CCS University, Meerut. His research focuses on nonlinear dynamics, bifurcation theory, delay differential equations, reaction–diffusion systems, Turing patterns, and the integration of deep learning and physics-informed neural networks (PINNs) for solving partial differential equations. Dr. Kumar has published extensively in high-impact journals on topics including SIR/SIRS epidemic models, cross-diffusion models, predator–prey dynamics, and pattern formation in spatial ecological systems. His work bridges theoretical mathematics and practical applications in disease modeling and ecology, offering insights into complex population interactions, control strategies, and spatio-temporal dynamics. Currently, he is developing deep learning algorithms to solve PDEs with applications in ecological and epidemiological systems. His ongoing research explores predator-prey interactions, learning in ecological models, and the forecasting of infectious diseases such as monkeypox. Dr. Kumar’s contributions have advanced the understanding of nonlinear systems, providing both analytical and computational tools for studying complex biological and ecological phenomena.

Profile : Google Scholar

Featured Publications

Gupta, R. P., & Kumar, A. (2022). “Endemic bubble and multiple cusps generated by saturated treatment of an SIR model through Hopf and Bogdanov–Takens bifurcations.” Mathematics and Computers in Simulation, 197, 1–21.

Gupta, R. P., Kumar, A., & Yadav, D. K. (2024). “The complex dynamical study of a UAI epidemic model in non-spatial and spatial environments.” The European Physical Journal Plus, 139(2), 117.

Yadav, D. K., Gupta, R. P., & Kumar, A. (2022). “Nonlinear dynamics of a three species prey-predator system incorporating fear effect and harvesting.” Journal of Mathematical Control Science and Applications.

Kumar, A., Gupta, R. P., & Tiwari, S. (2022). “Influences of nonlinear cross-diffusion on a reduced SI epidemic model with saturated treatment.”

Kumar, A., Kumari, N., Mandal, S., & Tiwari, P. K. (2025). “Autonomous and non-autonomous dynamics of an SIRS model with convex incidence rate.” Journal of the Franklin Institute, 108236.

Daxiong Ji | Data Analysis | Research Excellence Award

Published on by Research Data Analysis

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.