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

Ammar Khaleel l Reinforcement Learning | Research Excellence Award

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Mr. Ammar Khaleel l Reinforcement Learning | Research Excellence Award

Széchenyi István Egyetem | Hungary

Mr. Ammar Khaleel PhD-level researcher in computer science with ongoing doctoral training, focusing on reinforcement learning–based decision making for autonomous vehicles. Experience includes designing, training, and evaluating deep reinforcement learning and control algorithms for autonomous driving, particularly lane-changing, within large-scale traffic simulations using SUMO and the TraCI Python API. Research interests span reinforcement learning, deep learning, model predictive control, intelligent transportation systems, and traffic modeling. Technical expertise covers Python, C/C++, simulation frameworks, and reproducible research workflows. Academic contributions emphasize simulation-driven experimentation and algorithmic innovation; no formal awards are listed. Overall, the work aims to advance safe, efficient, and intelligent mobility systems.

Citation Metrics (Scopus)

40
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Citations
12

Documents
5

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2

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Featured Publications


A Multi-Levels RNG Permutation

– Indonesian Journal of Electrical Engineering and Computer Science, 2019

A New Permutation Method for Sequence of Order 28

– Journal of Theoretical and Applied Information Technology, 2019

N/A and Signature Analysis for Malwares Detection and Removal

– Indian Journal of Science and Technology, 2019

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

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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)

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

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

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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.

Naima Rahiel | Public Health Analytics | Women Researcher Award

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Mrs. Naima Rahiel | Public Health Analytics | Women Researcher Award

QARTZ, Université Paris 8 | France

Naima Rahiel is a doctoral researcher in Industrial Engineering and Productics at the University of Paris 8, specializing in the modeling and optimization of complex systems, with a particular focus on hospital logistics and supply chain resilience. She holds a Master’s and a Bachelor’s degree in Industrial Engineering from the University of Oran 2, Algeria, where she built strong foundations in probabilistic analysis, production systems, and decision-making under uncertainty. Her professional experience includes academic teaching at IUT de Montreuil and practical research in industrial and healthcare environments, such as Tosyali Algeria and the Canastel Pediatric Hospital in Oran. Her research explores the resilience of healthcare supply chains through analytical and simulation-based approaches, leading to several international conference presentations and peer-reviewed publications, including contributions to Springer’s book series and the journal Environmental Systems and Decision. Passionate about innovation, data analysis, and system reliability, she aims to bridge theoretical modeling with real-world decision support tools for sustainable and adaptive supply chain management. Her academic achievements and active participation in scientific events demonstrate her commitment to advancing research on healthcare logistics and resilience engineering, contributing valuable insights to the industrial and operational research community.

Profile : Google Scholar

Featured Publication

Rahiel, N., El Mhamedi, A., & Hachemi, K. (2024). Healthcare Supply Chain: Resilience Qualitative Evaluation. In Hospital Supply Chain: Challenges and Opportunities for Improving Healthcare.

Rahiel, N., El Mhamedi, A., Hachemi, K., Aouffen, N., & Rahiel, I. (2025). Resilience of the hospital supply chain: a case study-based approach on safety stock. Environment Systems and Decisions, 45 (4), 56.

Rahiel, N., Addouche, S.A., El Mhamedi, A., & Hachemi, K. (2025). Function-Based Modeling for Reactive Optimization of Healthcare Resource Reallocation. In Proceedings of the 16th International Conference on Logistics and Supply Chain Management (LOGISTIQUA 2025).

Qifei Du | Optimization Techniques | Best Researcher Award

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Mr. Qifei Du | Optimization Techniques | Best Researcher Award

College of Safety and Ocean Engineering China University of Petroleum | China

Mr. Du Qifei is an Assistant Engineer and graduate student in Mechanical Engineering at the College of Safety and Ocean Engineering, China University of Petroleum, Beijing. His academic and professional journey centers on marine equipment structural design, with active contributions to research and innovation in marine engineering. He has participated in key R&D projects such as the study on the influence mechanism of freezing and thawing effect of Arctic permafrost on oil and gas pipelines, alongside consultancy and industry collaborations involving underwater pressurization systems and offshore wind power platform integration. Du Qifei has filed an impressive number of patents spanning underwater connectors, subsea protection devices, dredging systems, pipeline technologies, and advanced agricultural equipment. His research interests include structural simulation and optimization of marine systems, sealing performance of metal seals, underwater pressurization, and the effects of environmental stressors on marine infrastructure. Recognized for his contributions to national-level technological programs and innovation in offshore engineering, he continues to bridge theoretical research with industrial applications. With a focus on sustainable and resilient marine structures, his work contributes to advancing safe and efficient solutions for global offshore engineering challenges.

Profile: Orcid

Featured Publications

“Design and key technology of underwater oil and gas pressurization system”
“A lifting three-dimensional garage”
“A logistics distribution system”
“A mechanical and electrical equipment testing platform”
“A mining system”
“A non-destructive apple picking device”

Gabriel Segarra – Human Factors – Best Researcher Award 

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Mr. Gabriel Segarra - Human Factors - Best Researcher Award 

Medical University of South Carolina - United States

Author Profile

Early Academic Pursuits

Mr. Gabriel C. Segarra's academic journey reflects a deep-seated passion for scientific exploration and interdisciplinary collaboration. Beginning with his Bachelor of Science in Biology from the College of Charleston, Segarra demonstrated an early commitment to understanding complex biological systems. His involvement in various research projects, such as his work on complement proteins and immune responses during his SURP Summer Researcher position at the Medical University of South Carolina (MUSC), highlights his dedication to advancing knowledge in the field.

As a Teaching Assistant in Molecular Biology Laboratory, Segarra not only enhanced his own understanding of molecular processes but also contributed to the educational development of his peers. His leadership as Principal Investigator for a NASA Space Mission Design project showcases his ability to lead multidisciplinary teams toward ambitious scientific goals, culminating in winning a design contest sponsored by NASA.

Professional Endeavors

Mr. Segarra's professional trajectory underscores his commitment to applying scientific principles to real-world challenges in healthcare. His tenure as a Research Program Assistant and subsequently as a Program Coordinator at MUSC's Embedded Human Factors and Clinical Safety Science Unit evidences his transition into a pivotal role in healthcare research. Under the mentorship of Dr. Ken Catchpole, Segarra spearheaded initiatives aimed at improving patient safety and quality of care in surgical settings.

His contributions to developing system analysis tools for sterile processing and integrating team training activities for robotic-assisted surgery underscore his hands-on approach to addressing critical issues in healthcare delivery. Segarra's ability to coordinate diverse research teams and his track record of presentations at prestigious conferences reflect his growing expertise and influence in the field.

Contributions and Research Focus

Mr. Segarra's research focuses on applying systems engineering principles to healthcare, particularly in the areas of surgical care, sterile processing, and transplant coordination. His work delves into understanding the intricate interdependencies within healthcare systems, with a keen emphasis on enhancing patient safety and quality outcomes.

His publications and presentations demonstrate a nuanced understanding of the challenges inherent in healthcare delivery and his innovative approach to addressing these challenges through rigorous research and interdisciplinary collaboration. By investigating patient safety incident reporting, reprocessing protocols, and simulation models, Segarra's research provides valuable insights that can inform policy and practice in healthcare settings.

Accolades and Recognition

Mr. Segarra's contributions have garnered recognition both within academia and the broader scientific community. His presentations at international symposiums and contributions to peer-reviewed journals reflect the esteem in which his work is held. Winning the design contest for the NASA space mission design further highlights his ability to excel in competitive environments and push the boundaries of scientific inquiry.

Impact and Influence

Mr. Segarra's work has the potential to catalyze significant advancements in healthcare delivery by bridging the gap between theoretical research and practical application. By elucidating complex interdependencies and developing innovative solutions, Segarra is poised to make a lasting impact on patient care and safety.

Legacy and Future Contributions

As Segarra continues to advance in his career, his legacy will be defined by his unwavering dedication to improving healthcare systems and outcomes. By mentoring future generations of researchers and continuing to push the boundaries of knowledge, Segarra's contributions will resonate far beyond his current endeavors, shaping the future landscape of healthcare delivery and patient safety.

Citations

A total of 342 citations for his publications, demonstrating the impact and recognition of her research within the academic community.

  • Citations    342

Notable Publication