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)

Bardia Rodd | Machine Learning and AI Applications | Best Researcher Award

Published on by Research Data Analysis

Prof. Bardia Rodd | Machine Learning and AI Applications | Best Researcher Award

SUNY Upstate Medical University | United States

Prof. Bardia Rodd is an Associate Professor at SUNY Upstate Medical University and Associate Director of AI Innovation at the AI for Health Equity, Analytics, and Diagnostics (AHEAD) Center. He completed dual PhDs in Electrical & Computer Engineering from Université Laval and in Computer Science from the University of Malaya, alongside a postdoctoral fellowship at the University of Pennsylvania Perelman School of Medicine, focusing on precision medicine, artificial intelligence, and image-guided systems. His academic career spans faculty positions at SUNY Upstate, the University of Maryland, and Laval University, with extensive experience in biocomputational engineering, machine learning, medical image analysis, and data-driven healthcare solutions. He has led numerous grants, including initiatives in AI in education, biomedical research, and curriculum development. Prof. Rodd has supervised multiple graduate and postgraduate students and contributed to open educational resources, advancing accessible teaching in machine learning and bioengineering. He is an active member of professional societies including IEEE, SPIE, and the Association of Pathology Informatics, serving on multiple technical committees and leadership roles. His research interests include AI-driven biomedical imaging, predictive modeling, and health equity applications. Prof. Rodd has authored a textbook on machine learning for data analysis and continues to integrate research, teaching, and innovation to advance computational medicine and AI education.

Profile : Scopus

Featured Publications

Siezen, H., Awasthi, N., Rad, M. S., Ma, L., & Rodd, B. (2025). “Low-rank distribution embedding of dynamic thermographic data for breast cancer detection.” Journal of Thermal Biology, 104303.

Usamentiaga, R., Fidanza, A., Yousefi, B., Iacutone, G., Logroscino, G., & Sfarra, S. (2025). “Advancing knee injury prevention and anomaly detection in rugby players through automated processing of infrared thermography: A novel biothermodynamics approach.” Thermal Science and Engineering Progress, 103782.

Rad, M. S., Huang, J. V., Hosseini, M. M., Choudhary, R., Siezen, H., Akabari, R., et al. (2025). “Deep learning for digital pathology: A critical overview of methodological framework.” Journal of Pathology Informatics, 100514.

Yousefi, B., Khansari, M., Trask, R., Tallon, P., Carino, C., Afrasiyabi, A., Kundra, V., Ma, L., Ren, L., Farahani, K., & Hershman, M. (2025). “Measuring subtle HD data representation and multimodal imaging phenotype embedding for precision medicine.” IEEE Transactions on Instrumentation and Measurement, TIM-24-01821.

Cao, Y., Sutera, P., Mendes, W. S., Yousefi, B., Hrinivich, T., Deek, M., et al. (2024). “Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.” Radiotherapy and Oncology, 199, 110443.

Lan Xie | COVID-19 Analysis | Research Excellence Award

Published on by Research Data Analysis

Assoc. Prof. Dr. Lan Xie | COVID-19 Analysis | Research Excellence Award

Tsinghua University | China

Assoc. Prof. Dr. Lan Xie is a leading researcher in drug discovery and traditional Chinese medicine (TCM) integration with modern biomedical technologies. She earned her M.D. in Clinical Medicine from Peking University and a Ph.D. in Biology from Tsinghua University. Dr. Xie completed postdoctoral training at Tsinghua University and a visiting scholar program at the University of Pittsburgh. She has held faculty positions at Tsinghua University since 2014, currently serving as Associate Professor at the School of Basic Medical Sciences. Her research focuses on combining TCM with systems biology, multi-omics, and artificial intelligence to develop novel therapeutic strategies. She pioneered the Molecular Materia Medica database and an intelligent TCM formulation algorithm leveraging gene expression profiles and dysregulated disease pathways. Her work has been recognized with numerous awards, including the Huaxia Medical Science and Technology Award, International Award for Contribution to Chinese Medicine, and multiple Tsinghua University teaching excellence awards. Dr. Xie’s research achievements encompass high-throughput TCM bioactive component screening, single-cell omics-based mechanistic studies, and pathway-guided intelligent drug formulation. With 1,716 citations by 47 documents, 47 publications, and an h-index of 24, her contributions continue to advance modern medicine by bridging traditional knowledge and cutting-edge technology.

Profile : Scopus

Featured Publications

“A universal gene expression signature-based strategy for the high-throughput discovery of anti-inflammatory drugs.” Inflammation Research. 2025.

“Identification of the fruit of Brucea javanica as an anti-liver fibrosis agent working via SMAD2/SMAD3 and JAK1/STAT3 signaling pathways.” Journal of Pharmaceutical Analysis. 2025.

“Pulsatilla chinensis functions as a novel antihyperlipidemic agent by upregulating LDLR in an ERK-dependent manner.” Chinese Medicine United Kingdom. 2024.

“Mechanism study of Xiaoyao San against nonalcoholic steatohepatitis-related liver fibrosis based on a combined strategy of transcriptome analysis and network pharmacology.” Pharmaceuticals. 2024.

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.

Samaneh Saeedinia | Engineering | Best Researcher Award

Published on by Research Data Analysis

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.

Mebarka Allaoui | Machine Learning and AI Applications | Best Paper Award

Published on by Research Data Analysis

Dr. Mebarka Allaoui | Machine Learning and AI Applications | Best Paper Award

Bishop’s University | Canada

Dr. Mebarka Allaoui dedicated computer science researcher with a strong background in machine learning, manifold learning, and computer vision, this scholar holds a PhD in Computer Science focused on embedding techniques and their applications to visual data analysis. Their academic journey includes a master’s degree in industrial computer science and a bachelor’s degree in information systems, all completed with high distinction. Professionally, they have served as a Postdoctoral Fellow contributing to industry-funded research on anomaly detection, developing novel embedding, deep learning, and clustering methods to enhance the interpretability of latent representations and improve fraud detection in real-world financial datasets. Prior experience includes working as a computer engineer supporting system administration, software development, data analysis, and network configuration, alongside several teaching appointments delivering practical courses in software engineering, algorithmics, and web development. Their research contributions span dimensionality reduction, clustering, optimization, document analysis, and scientific information retrieval, with publications in reputable journals and conferences. Collaborative work further extends to studies on optimizers, object detection, and embedding initialization strategies. Recognized for high-quality academic performance and impactful research outputs, they continue to advance data-driven methodologies, aiming to bridge theoretical innovation with practical applications in intelligent systems and decision-support technologies.

Profile : Google Scholar

Featured Publications

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). “Considerably improving clustering algorithms using UMAP dimensionality reduction technique” in International Conference on Image and Signal Processing, 317–325.

Drid, K., Allaoui, M., & Kherfi, M. L. (2020). “Object detector combination for increasing accuracy and detecting more overlapping objects” in International Conference on Image and Signal Processing, 290–296.

Allaoui, M., Belhaouari, S. B., Hedjam, R., Bouanane, K., & Kherfi, M. L. (2025). “t-SNE-PSO: Optimizing t-SNE using particle swarm optimization” in Expert Systems with Applications, 269, 126398.

Allaoui, M., Kherfi, M. L., Cheriet, A., & Bouchachia, A. (2024). “Unified embedding and clustering” in Expert Systems with Applications, 238, 121923.

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). “International Conference on Image and Signal Processing” in Springer.

Modafar Ati | Machine Learning and AI Applications | Best Researcher Award

Published on by Research Data Analysis

Assoc. Prof. Dr. Modafar Ati | Machine Learning and AI Applications | Best Researcher Award

Abu Dhabi University | United Arab Emirates

Assoc. Prof. Dr. Modafar Ati is an accomplished academic and researcher in Computer Science and Information Technology with extensive experience in teaching, curriculum development, and leadership. He holds a BSc in Electrical & Communication Engineering and a PhD in Electrical & Computing Engineering from Newcastle upon Tyne, UK. Dr. Ati has over three decades of experience in both academia and industry, serving in senior roles including Associate Professor at Abu Dhabi University, Acting Dean at Sohar University, and Assistant Dean at multiple institutions in Oman. His professional expertise spans software development, systems integration, networking, ICT, project management, Service-Oriented Architecture (SOA), Artificial Intelligence, Big Data, Cybersecurity, and eHealth smart systems. He has published numerous research articles focusing on AI-driven Chronic Disease Management and smart healthcare systems and has led research groups in eGovernment and ICT. Dr. Ati has received multiple grants and awards, including the Chancellor Innovation Award, and serves on editorial boards and as a senior reviewer for international journals and conferences. His teaching portfolio covers programming, network security, project management, human-computer interaction, and mobile application development. Dr. Ati’s contributions to education, research, and innovation reflect a strong commitment to advancing technology-driven solutions in healthcare, smart cities, and digital systems.

Profile : Google Scholar

Featured Publications

Hussein, A. S., Omar, W. M., Li, X., & Ati, M. (2012). “Efficient chronic disease diagnosis prediction and recommendation system” in 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, 209–214.

Mohamed, O., Kewalramani, M., Ati, M., & Al Hawat, W. (2021). “Application of ANN for prediction of chloride penetration resistance and concrete compressive strength” in Materialia, 17, 101123.

Mohamed, O. A., Ati, M., & Najm, O. F. (2017). “Predicting compressive strength of sustainable self-consolidating concrete using random forest” in Key Engineering Materials, 744, 141–145.

Hussein, A. S., Omar, W. M., Li, X., & Ati, M. (2012). “Accurate and reliable recommender system for chronic disease diagnosis” in Global Health, 3(2), 113–118.

Ati, M., Kabir, K., Abdullahi, H., & Ahmed, M. (2018). “Augmented reality enhanced computer aided learning for young children” in 2018 IEEE Symposium on Computer Applications & Industrial Electronics.

Decheng Li | Engineering | Best Researcher Award

Published on by Research Data Analysis

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.