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

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

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.

Citation Metrics (Scopus)

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486

Documents
49

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Featured Publications

George Vlontzos | Agricultural Data Analysis | Research Excellence Award

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Prof. George Vlontzos | Agricultural Data Analysis | Research Excellence Award

University of Thessaly | Greece

Prof. George Vlontzos is a distinguished agricultural economist and Full Professor at the University of Thessaly, Greece, where he directs the Laboratory of Agricultural Economics and Consumer Behavior. He holds a Ph.D. in Planning and Regional Development (University of Thessaly), an MBA in Agribusiness (University of Wales, Aberystwyth) and a BSc in Agriculture (Aristotle University of Thessaloniki). With a strong international academic profile, he has served as a visiting professor at the University of Florida and contributed to graduate programs at the Mediterranean Agronomic Institute of Montpellier. Prof. Vlontzos has authored 62 documents indexed on Scopus with 952 citations by 886 documents and an h‑index of 17, reflecting significant research impact in agricultural economics, efficiency and sustainability analysis, consumer behavior, optimization and AI applications in agriculture. His research employs advanced quantitative and behavioral models—such as Data Envelopment Analysis, Stochastic Frontier Analysis, the Theory of Planned Behavior and Artificial Neural Networks—to address economic and environmental efficiency, consumer decision processes, and sustainable agri‑food systems. He has published extensively in refereed journals, participated in major international conferences, and contributed to multiple competitive projects co‑funded by the EU, national and private sources. His leadership in research and education underscores his influence in shaping sustainable agricultural policy and practice.

Profile : Scopus

Featured Publications

Assessing the economic impact of insect pollination on the agricultural sector: A department-level case study in France

– Environmental and Sustainability Indicators, 2025

Emerging Technologies for Investigating Food Consumer Behavior: A Systematic Review

– Review, 2025

A hybrid remotely operated underwater vehicle for maintenance operations in aquaculture: Practical insights from Greek fish farms

– Computers and Electronics in Agriculture, 2025

Digital Transformation of Food Supply Chain Management Using Blockchain: A Systematic Literature Review Towards Food Safety and Traceability

– Business and Information Systems Engineering, 2025

Variable RTS in hierarchical network DEA: Enhancing efficiency in higher education systems

– Socio Economic Planning Sciences, 2024

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)

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

Piotr Porwik | Machine Learning and AI Applications | Best Researcher Award

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Prof. Dr. Piotr Porwik | Machine Learning and AI Applications | Best Researcher Award

University of Silesia | Poland

Prof. Dr. Piotr Porwik is a full professor in the Institute of Computer Science at the University of Silesia in Katowice, specializing in engineering and technical sciences. He earned his M.Sc. in computer science from the University of Silesia in 1978, his Ph.D. in 1985, and completed his habilitation in 2006 at AGH University of Science and Technology in Krakow. His research spans machine learning, biometrics, image processing, biomedical imaging, and spectral methods for Boolean functions. Across his career, he has authored 81 scientific documents that have attracted 1,003 citations from 783 citing documents, and he holds an h-index of 17. He has published extensively in journals, international conferences, books, and book chapters, while also supervising Master’s and Ph.D. students. Prof. Porwik has served in major academic leadership roles, including Deputy Dean and Director of the Institute, and played a key role in shaping academic publishing as Editor-in-Chief of the Journal of Medical Informatics and Technologies. His achievements have been recognized through numerous distinctions, including the Bronze Cross of Merit and multiple awards from the President of the University of Silesia for scientific accomplishments. His work continues to advance biometric security, classifier development, and biomedical informatics, underscoring his long-standing academic influence and research innovation.

Profiles : Scopus | Orcid

Featured Publications

Wrobel, K., Porwik, P., & Orczyk, T. (2025). “Evaluation of the Effectiveness of Ranking Methods in Detecting Feature Drift in Artificial and Real Data” in (Book) / Lecture Notes in?

Mensah Dadzie, B., & Porwik, P. (2025). “Feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis” in (Book)

Porwik, P., Orczyk, T., Wrobel, K., & Mensah Dadzie, B. (2025). “A Novel Method for Drift Detection in Streaming Data Based on Measurement of Changes in Feature Ranks” in Journal of Artificial Intelligence and Soft Computing Research, (DOI: 10.2478/jaiscr-2025-0008).

Porwik, P., Orczyk, T., & Japkowicz, N. (2024). “Supervised and Unsupervised Analysis of Feature Drift in a New Type of Detector” in Preprint (Research Square)

Porwik, P., Orczyk, T., & Doroz, R. (2022). “A Stable Method for Detecting Driver Maneuvers Using a Rule Classifier” in Lecture Notes in Computer Science

Muhammad Saddam Khokhar | Data Representation | Best Researcher Award

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Dr. Muhammad Saddam Khokhar | Data Representation | Best Researcher Award

Yangzhou University | China

Dr. Muhammad Saddam Khokhar is an accomplished academic and researcher specializing in generative artificial intelligence and computer vision. With over a decade of teaching, research, and leadership experience, he has made significant contributions to artificial intelligence, quantum computing, and deep learning. His expertise spans across developing innovative AI-driven solutions, managing academic programs, and mentoring research scholars at undergraduate, postgraduate, and doctoral levels. As an assistant professor and former department chairman, he has been instrumental in advancing artificial intelligence education, accreditation processes, and research-driven innovation in multiple institutions internationally.

Professional Profiles

ORCID

GOOGLE SCHOLAR

Education

Dr. Khokhar earned his Doctorate in Computer Application Technology from Jiangsu University, China, where his dissertation focused on nonlinear optimization and dimensional reduction using advanced Spearman correlation analysis integrated with deep learning models. His research was recognized with an excellent dissertation award for its innovative methodologies. He also holds a Master’s degree in Computer Science and Information Technology with a focus on machine learning, data mining, and software project management, as well as a Bachelor’s degree in Software Engineering, where he developed strong expertise in artificial intelligence, software architecture, and advanced programming techniques.

Experience

With extensive academic and professional experience, Dr. Khokhar has served in multiple teaching and research roles, including Assistant Professor at the College of Artificial Intelligence, Yangzhou University, and Postdoctoral Fellow at the College of Software Engineering, focusing on generative AI projects. He has also chaired the Department of Artificial Intelligence at Dawood University of Engineering and Technology and contributed to curriculum development, laboratory design, and accreditation processes for AI and quantum computing programs. Earlier in his career, he held lecturer positions at leading Pakistani universities and worked as a software engineer, gaining industry insights that enriched his academic pursuits.

Research Interests

His research primarily focuses on generative artificial intelligence, computer vision, deep learning, and nonlinear optimization techniques. Dr. Khokhar has developed innovative models leveraging Spearman correlation analysis for medical image processing, robotic vision, traffic surveillance, and multi-camera monitoring systems. His recent projects explore lightweight generative adversarial networks (IoTGAN), hybrid attention-driven generative frameworks, and blockchain-based secure election systems. He has also actively contributed to discussions on artificial general intelligence, metaverse technologies, and ethical implications of AI in modern societies through various publications and invited talks.

Awards

Dr. Khokhar has been recognized with several prestigious awards for his contributions to innovation, research, and academic excellence. These include the Best Innovation Award for developing a revolutionary AI platform for gene editing and medical technology, multiple academic achievement awards during his doctoral studies, and accolades in application development and research competitions. His work has been published in high-impact journals and presented at international conferences, showcasing his commitment to advancing cutting-edge AI research and practical solutions for global challenges.

Publications

Muhammad Saddam Khokhar, Misbah Ayoub, Zakria, Abdullah Lakhan. (2025). “Advanced Nonlinear Optimization of Low- and High-Resolution Medical Images Using Adaptive Deep Spearman Correlation Analysis (D-SCA) for Pattern Sequence Recognition.” Applied Soft Computing.

Muhammad Saddam Khokhar. (2025). “Energy Harvesting in IoT, Fog, and Blockchain–Thermoelectric Cooling Innovations.”

Muhammad Saddam Khokhar. (2025). “Health Coin: Revolutionizing Global Healthcare with Green Blockchain Technology.”

Zakria Zakria, Jianhua Deng, Rajesh Kumar, Muhammad Saddam Khokhar, Jingye Cai, Jay Kumar. (2022). “Multiscale and Direction Target Detecting in Remote Sensing Images via Modified YOLO-v4.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Abdullah Lakhan, Qurat-ul-ain Mastoi, Mazhar Ali Dootio, Fehaid Alqahtani, Ibrahim R. Alzahrani, Fatmah Baothman, Syed Yaseen Shah, Syed Aziz Shah, Nadeem Anjum, Qammer Hussain Abbasi, et al. (2021). “Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network.” Electronics.

Abdullah Lakhan, Mazhar Ali Dootio, Tor Morten Groenli, Ali Hassan Sodhro, Muhammad Saddam Khokhar. (2021). “Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks.” Electronics.

Conclusion

Dr. Muhammad Saddam Khokhar embodies a dynamic blend of academic rigor, innovative research, and leadership in the field of artificial intelligence. His career reflects a commitment to advancing AI education, developing impactful technologies, and contributing to global scientific knowledge. Through his research, publications, and mentorship, he continues to inspire the next generation of AI professionals while fostering interdisciplinary collaborations that bridge technology with societal advancement.