Jamal Zraqou | Machine Learning | Research Excellence Award

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Assoc. Prof. Dr. Jamal Zraqou | Machine Learning | Research Excellence Award

Associate Professor | University of Petra | Jordan

Assoc. Prof. Dr. Jamal S. Zraqou is an active researcher with demonstrated contributions across data-driven engineering, machine learning, cybersecurity, and digital transformation. He has authored 45 scholarly documents indexed in Scopus, accumulating 202 citations with an h-index of 9, reflecting consistent academic impact. His recent work addresses optimization techniques for engineering design, advanced machine learning methods for phishing detection, cybersecurity vulnerability analysis, and the strategic role of business intelligence in digital transformation. Dr. Zraqou has collaborated with a broad international network of over 60 co-authors, highlighting interdisciplinary and cross-sector engagement. His research supports practical problem-solving in engineering systems, information security, and decision intelligence, contributing to improved technological resilience, safer digital environments, and enhanced organizational competitiveness at societal and industrial levels.

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Francesco Inchingolo | Artificial Intelligence | Research Excellence Award

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Prof. Dr. Francesco Inchingolo | Artificial Intelligence | Research Excellence Award

Professor in Odontostomatological Diseases (Scientific Sector MED/28) | University of Bari “Aldo Moro” – University Hospital “Policlinico di Bari” | Italy

Prof. Dr. Francesco Inchingolo is a leading Italian clinician-scientist in oral and maxillofacial sciences, renowned for his multidisciplinary contributions spanning dentistry, oral surgery, orthodontics, implantology, regenerative medicine, and public health. A Full Professor of Odontostomatological Diseases and long-standing director of major specialization programs, he has significantly advanced clinical training and translational research at the University of Bari “Aldo Moro.” His global scholarly impact is exemplified by 12,137 citations from 6,038 documents, 489 publications, and an h-index of 66, demonstrating sustained excellence and international recognition. A Principal Investigator in multiple funded projects, he has driven innovation in stem-cell applications, platelet-derived biomaterials, piezosurgery, bone regeneration, orthodontic biomechanics, geriatric dentistry, pediatric oral care, and complex maxillofacial pathologies. His extensive editorial and reviewer roles, together with collaborations across Europe, Asia, and the United States, emphasize his position as a central figure in global dental research networks. Prof. Inchingolo has delivered numerous invited lectures worldwide and serves as Visiting Professor in several international institutions, strengthening academic exchanges and capacity building. His work has been recognized through an exceptional series of national and international distinctions—including the prestigious Sant’Apollonia Award, multiple CDUO, SIDO, and SIOH honors, international research incentive awards, and cultural and scientific excellence recognitions such as the Carthage 2.0 Prize, “Tribute to Life,” and best research poster awards across diverse dental disciplines. Through his high-impact publications, clinical innovations, and leadership in advanced oral surgery and implantology programs, he has contributed substantially to improving patient outcomes, advancing therapeutic technologies, and shaping modern dental and maxillofacial practice on a global scale.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

Hossein Ghaffarian | Machine Learning | Editorial Board Member

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Dr. Hossein Ghaffarian | Machine Learning | Editorial Board Member 

Assistant Professor | Arak University | Iran

Dr. Hossein Ghaffarian is a distinguished researcher and faculty member in the Department of Computer Engineering at Arak University, Iran, recognized for his expertise in computer networks, intelligent transportation systems (ITS), data mining, and applied artificial intelligence. His academic contributions encompass both theoretical and applied dimensions of wired and wireless network architectures, network security, and quality of service optimization. Dr. Ghaffarian’s scholarly work demonstrates a strong interdisciplinary orientation, bridging computer systems architecture with real-world applications in vehicular ad hoc networks (VANETs), indoor localization, and cloud-based network solutions. He has served in multiple academic and professional capacities, including as IT and Product Manager at Sanaat Yar Afzar Iranian and consultant for Iran’s Ministry of Education and the Electrical Industry Data Committee (Tavanir). His innovative research has earned national recognition, including a Best Paper Award at the IEEE International Conference on Internet of Things and Applications. Dr. Ghaffarian has also contributed to key industrial and governmental projects, such as developing WAN solutions for electrical industries and designing cloud-based monitoring systems. His research achievements are further complemented by his active engagement in academic translation and technical education, with works such as Python Numpy for Beginners and Python Pandas for Beginners (Farsi editions). Dr. Hossein Ghaffarian’s academic impact is reflected in his international research visibility, with 82 citations by 81 documents, 21 publications, and an h-index of 4, underscoring his growing influence in computer engineering and artificial intelligence research.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Ghaffarian, H., Fathy, M., & Soryani, M. (2012). Vehicular ad hoc networks enabled traffic controller for removing traffic lights in isolated intersections based on integer linear programming. IET Intelligent Transport Systems, 6(2), 115–123. Citations: 52

  2. Farahani, B. J., Ghaffarian, H., & Fathy, M. (2009). A fuzzy based priority approach in mobile sensor network coverage. International Journal of Recent Trends in Engineering, 2(1), 138. Citations: 19

  3. Rashvand, H. F., & Chao, H. C. (2013). Dynamic ad hoc networks. Institution of Engineering and Technology. Citations: 18

  4. Parvin, H., Minaei-Bidgoli, B., & Ghaffarian, H. (2011). An innovative feature selection using fuzzy entropy. In International Symposium on Neural Networks (pp. 576–585). Citations: 16

  5. Keramatpour, A., Nikanjam, A., & Ghaffarian, H. (2017). Deployment of wireless intrusion detection systems to provide the most possible coverage in wireless sensor networks without infrastructures. Wireless Personal Communications, 96(3), 3965–3978. Citations: 15

Nur Intan Raihana Ruhaiyem | Machine Learning | Best Researcher Award

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Dr. Nur Intan Raihana Ruhaiyem | Machine Learning | Best Researcher Award

Senior Lecturer | Universiti Sains Malaysia | Malaysia

Dr. Nur Intan Raihana Ruhaiyem is a highly accomplished researcher and Senior Lecturer at the School of Computer Sciences, Universiti Sains Malaysia, with notable expertise in computational biology, image processing, data visualization, and artificial intelligence applications. Her research spans deep learning, computer vision, and biomedical informatics, focusing on developing intelligent systems that enhance healthcare diagnostics, cultural heritage preservation, and data-driven decision-making. She has authored over 50 scholarly publications in reputable international journals and conferences, including IEEE Access, Biomedical Signal Processing and Control, Intelligence-Based Medicine, Diagnostics (Basel), Image and Vision Computing, and Scientific Reports. Her works have collectively garnered more than 230 citations and an h-index of 7, underscoring her growing impact in the computational and data science research community. Recent contributions such as the development of Mamba-based UNet architectures for medical image segmentation and hybrid restoration models for historical murals reflect her capacity to integrate advanced AI models into multidisciplinary domains. Dr. Ruhaiyem’s collaborative research extends internationally, with partnerships involving scholars from Australia, China, and the broader ASEAN region. Her role as a technical committee member for several prominent conferences—such as the International Visual Informatics Conference and Soft Computing in Data Science—demonstrates her leadership in promoting innovation and research excellence in data science and visual analytics. A Certified Professional Trainer recognized by Malaysia’s Human Resources Development Fund, she has also played a key role in professional education, serving as a lead instructor for national Data Science Certification programs. Through her research, mentorship, and active academic engagement, Dr. Ruhaiyem contributes significantly to advancing digital transformation, fostering analytical literacy, and bridging computational intelligence with societal needs.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

1. Younis, H. A., Ruhaiyem, N. I. R., Ghaban, W., Gazem, N. A., & Nasser, M. (2023). A systematic literature review on the applications of robots and natural language processing in education. Electronics, 12(13), 2864. Citations: 75

2. Salisu, S., Ruhaiyem, N. I. R., Eisa, T. A. E., Nasser, M., Saeed, F., & Younis, H. A. (2023). Motion capture technologies for ergonomics: A systematic literature review. Diagnostics, 13(15), 2593. Citations: 63

3. Goni, M. R., Ruhaiyem, N. I. R., Mustapha, M., Achuthan, A., & Nassir, C. M. N. C. M. (2022). Brain vessel segmentation using deep learning—A review. IEEE Access, 10, 111322–111336. Citations: 42

4. Yang, J., & Ruhaiyem, N. I. R. (2024). Review of deep learning-based image inpainting techniques. IEEE Access, 12, 138441–138482. Citations: 17

5. Younis, H. A., Ruhaiyem, N. I. R., Badr, A. A., Abdul-Hassan, A. K., Alfadli, I. M., & others. (2023). Multimodal age and gender estimation for adaptive human-robot interaction: A systematic literature review. Processes, 11(5), 1488. Citations: 16

Saifullah Khalid | Artificial Intelligence | Innovative Research Award

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Dr. Saifullah Khalid | Artificial Intelligence | Innovative Research Award

Principal Scientist | IBMM RESEARCH | Sudan

Dr. Saifullah Khalid is a distinguished aviation and aerospace researcher renowned for his groundbreaking work in AI-driven aviation systems, air traffic management optimization, and unmanned aerial systems. With dual PhDs in engineering and a career spanning advanced aeronautical research, he serves as Principal Scientist at IBMM Research, Sudan. His academic background includes a PhD in Electronics and Communication Engineering from SN University, India (2013). Dr. Khalid’s expertise encompasses autonomous UAV systems, quantum-inspired optimization algorithms, sustainable aviation power systems, and digital tower operations. He has authored over 270 publications, including 38 Web of Science-indexed papers, and holds an impressive portfolio of 85 patents—50 as sole inventor—setting two world records for patent achievements. His professional experience extends to teaching and mentoring, supervising PhD candidates in AI-based air route optimization and guiding over 200 engineering projects. A committed academic leader, he has developed ICAO-compliant curricula and serves as Vice Chairman of the Academic Council Asia at NextGen University International. His awards include world records for patent excellence and international recognition for research innovation. His technical skills span MATLAB/Simulink, Python, UAV system design, and AI applications in aviation. 305 Citations; 67 Documents; h-index: 10

Profiles: Google scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Khalid, S., & Dwivedi, B. (2011). Power quality issues, problems, standards & their effects in industry with corrective means. International Journal of Advances in Engineering & Technology, 1(2), 1–11. Citations: 167

  2. Nishad, D. K., Tiwari, A. N., Khalid, S., Gupta, S., & Shukla, A. (2024). AI-based UPQC control technique for power quality optimization of railway transportation systems. Scientific Reports, 14(1), 17935. Citations: 31

  3. Khalid, S. (2018). Performance evaluation of Adaptive Tabu Search and Genetic Algorithm optimized shunt active power filter using neural network control for aircraft power utility of 400 Hz. Journal of Electrical Systems and Information Technology, 5(3), 723–734. Citations: 30

  4. Khalid, S., Dwivedi, B., Kumar, N., & Agrawal, N. (2007). A review of state-of-art techniques in active power filters and reactive power compensation. National Journal of Technology, 3(1), 10–18. Citations: 26

  5. Khalid, S., & Dwivedi, B. (2010). Power quality: An important aspect. International Journal of Engineering Science and Technology, 2(11), 6485–6490. Citations: 25