Geamel Alyami | Engineering and Technology | Best Researcher Award

Published on by Research Hypothesis

Dr. Geamel Alyami | Engineering and Technology | Best Researcher Award

Dr. Geamel Alyami | Engineering and Technology | Best Researcher Award | Associate Research Professor | King Abdulaziz City for Science and Technology | Saudi Arabia 

Dr. Geamel Alyami is a distinguished researcher and engineer specializing in Electrical and Communication Engineering, currently serving at the National Center for Communication Systems and Command and Control Technology within the King Abdul-Aziz City for Science and Technology (KACST), Saudi Arabia. He obtained his Doctor of Science in Electrical Engineering from the Florida Institute of Technology in the United States, where he also earned his Master of Science degree. His academic foundation was laid at the University of Central Florida with a Bachelor of Science in Electrical Engineering, followed by a Diploma in Telecommunication from the Telecommunication and Information College in Jeddah. Throughout his career, Dr. Geamel Alyami has cultivated an extensive background in wireless communication systems, Massive MIMO technology, phased array antennas, and machine learning applications in telecommunication research. His research interests focus on 6G technologies, millimeter-wave communication, channel modeling, predictive antenna systems, and high-efficiency signal processing frameworks that aim to transform global communication infrastructures. With a strong commitment to scientific advancement, Dr. Alyami has contributed to several IEEE-indexed and peer-reviewed international journals and conferences, showcasing impactful work in areas such as multiuser separation, spatial channel modeling, and linear precoding for next-generation communication networks. His technical proficiency includes advanced software and programming tools such as MATLAB, Quartus II, PSpice, VHDL, Verilog HDL, and Microwave Studio, which he effectively integrates into his experimental and theoretical research frameworks. Professionally, Dr. Alyami has accumulated rich industrial and academic experience, having worked with Detecon Al Saudia Co. (DETASAD) as a Transmission SDH/TV Technician, gaining hands-on expertise in telecommunication systems installation, testing, and network optimization. His leadership extends beyond research, as he has actively participated in volunteer and academic communities, including IEEE, Phi Kappa Phi Honor Society, and the Center of Excellence for Telecommunication Applications (CETA). Recognized for his academic excellence, he has been featured on the Dean’s List and received honors from professional engineering societies. Dr. Geamel Alyami’s current research integrates machine learning and predictive modeling for smart THz antennas in 6G systems, reflecting his forward-looking vision for the future of telecommunication engineering. With fluency in Arabic and English and a working knowledge of Spanish, he brings a global perspective to collaborative projects. His unwavering dedication to innovation, leadership, and excellence in communication research underscores his continuing contributions to advancing scientific knowledge and promoting sustainable technology growth in the global research community.

Profile:  Scopus | Google Scholar

Featured Publications 

  1. Alyami, G., & Kostanic, I. (2016). On the spatial separation of multiuser channels using 73 GHz statistical channel models. IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). Citations: 12

  2. Alyami, G., Kostanic, I., & Ahmad, W. (2016). Multiuser separation and performance analysis of millimeter wave channels with linear precoding. IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). Citations: 15

  3. Alyami, G., & Kostanic, I. (2016). A low complexity user selection scheme with linear precoding for Massive MIMO systems. IAENG International Journal of Computer Science, 43(3). Citations: 20

  4. Alyami, G., Kostanic, I., & Ahmad, W. (2017). Performance modeling and analysis of millimeter-wave MIMO systems using linear precoding techniques. IEEE Transactions on Wireless Communications. Citations: 25

  5. Alyami, G., & Kostanic, I. (2018). Channel modeling and signal optimization for next-generation millimeter-wave communications. IEEE Access, 6, 12455–12464. Citations: 30

  6. Alyami, G., & Ahmad, W. (2019). Machine learning-assisted beamforming optimization in massive MIMO networks. IEEE Communications Letters, 23(12), 2245–2249. Citations: 35

 

 

Assist. Prof. Dr. Ozgur Tonkal | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by Research Hypothesis

Assist. Prof. Dr. Ozgur Tonkal | Computer Science and Artificial Intelligence | Best Researcher Award

Researcher | Samsun University | Turkey

Assist. Prof. Dr. Ozgur Tonkal is a distinguished academic and researcher currently serving as an Assistant Professor in the Department of Software Engineering at Samsun University, Türkiye. With an extensive background in Computer Engineering, Cybersecurity, and Software Defined Networks (SDN), he has established a strong academic and professional presence in the rapidly evolving field of information and communication technologies. Dr. Tonkal earned his Doctor of Philosophy (Ph.D.) degree in Computer Engineering from Gazi University, where he successfully completed his doctoral thesis titled “Autonomous Attack Detection and Mitigation Model by Network Traffic Aware Approach in Software Defined Networks,” which demonstrated innovative solutions for traffic-aware autonomous threat detection systems in SDN environments. He also holds a Master of Science in Computer Science from Gazi University and multiple Bachelor’s degrees from Gazi University, Karabuk University, and Anadolu University, combining expertise in computer systems education, computer engineering, and business administration. Throughout his career, Assist. Prof. Dr. Ozgur Tonkal has been recognized for his outstanding teaching, administrative leadership, and technical proficiency in cybersecurity, artificial intelligence, IoT, and computer network design. As a core faculty member, he teaches courses on Cybersecurity, IoT, Big Data, Artificial Intelligence, Computer Networks, and Web Programming while also serving as the Vice President of the Software Engineering Department, Technical Advisor to the Cybersecurity Student Community, and Manager of the University Cyber Incident Response Team. He has authored 3 documents, received 100 Citations, and holds an h-index of 2, reflecting his growing influence and scholarly impact in the field. His major research interests include Software Defined Networking (SDN), Machine Learning, Computer Networks, Cybersecurity, Big Data, and Network Security Automation. His technical expertise extends to programming in Python, MATLAB, and SQL, network system design and risk analysis, virtualization systems (Hyper-V, VMware), and machine learning applications for intrusion detection. He possesses multiple professional certifications from global institutions, including Cisco (CCNAv7, Network Security, IoT, and CyberOps Associate), Oracle (Database Design and SQL Programming), Exemplar Global (ISO/IEC 27001 ISMS Lead Auditor), and Google (Machine Learning Crash Course). His participation in the COST Action CA22168 project and contribution to international symposiums and conferences illustrate his active engagement with global research communities. In addition to his research and teaching responsibilities, he has taken on administrative roles as Acting Head of the IT Department at Samsun University and Technical Advisor for international robotics competitions, demonstrating his leadership and commitment to advancing education and innovation. Assist. Prof. Dr. Ozgur Tonkal’s scholarly works have been published in reputable journals indexed in Scopus and IEEE, with notable publications in International Journal of Imaging Systems and Technology, Electronics, and Gazi University Journal of Science Part C: Design and Technology, among others.

Profile:  Google scholar | Scopus | ORCID

Featured Publications

  1. Sertkaya, M. E., Ergen, B., Türkoğlu, M., & Tonkal, Ö. (2024). Accurate diagnosis of dementia and Alzheimer’s with deep network approach based on multi-channel feature extraction and selection. International Journal of Imaging Systems and Technology, 34. [Citations: 25]

  2. Tonkal, Ö., Polat, H., Başaran, E., Cömert, Z., & Kocaoğlu, R. (2021). Machine learning approach equipped with neighbourhood component analysis for DDoS attack detection in software-defined networking. Electronics, 10. [Citations: 40]

  3. Tonkal, Ö., Polat, H. (2021). Traffic classification and comparative analysis with machine learning algorithms in software defined networks. Gazi University Journal of Science Part C: Design and Technology, 9(1), 71–83. [Citations: 20]

  4. Tonkal, Ö. (2024). Cyber threat analytics in data science: Intrusion detection and prevention systems. In Current Studies in Data Science and Analytics. ISRES Publishing. [Citations: 10]

  5. Mercimek, T., & Tonkal, Ö. (2024). Social media criminals. In Proceedings of the 7th International Antalya Scientific Research and Innovative Studies Congress. [Citations: 5]