Mohammed Alenazi | Computer Engineering | Best Researcher Award

Mr. Mohammed Alenazi | Computer Engineering | Best Researcher Award

Assistant Professor | University of Tabuk | Saudi Arabia

Mr. Mohammed M. Alenazi is an accomplished academic and researcher with expertise in electrical and electronics engineering, computer engineering, and artificial intelligence applications in energy-efficient networks. He earned his Ph.D. in Electrical and Electronics Engineering from the University of Leeds, UK (2018–2022), focusing on energy efficiency in AI-powered communication systems. Prior to this, he completed his M.Eng. in Computer Engineering at Florida Institute of Technology, USA (2016–2017), and a B.Eng. in Computer Engineering from University Sultan Bin Fahad (2007–2011), along with an Associate’s degree in Electrical/Electronics Equipment Installation and Repair from Tabuk College of Technology (2002–2004). Professionally, Mr. Alenazi began his career as a Senior Engineer at Saudi Telecom Company (2006–2011), where he gained practical experience in optical fiber networks, before transitioning to academia as a Teaching Assistant at Northern Border University (2012–2013) and later at the University of Tabuk, where he continues to serve since 2013, eventually advancing into an assistant professorship. His research interests include machine learning, IoT networks, energy optimization, and intelligent systems, with key contributions in developing models for energy-efficient ML-based service placement, neural network embedding in IoT, and intelligent sterilization systems, reflected in several IEEE and Scopus-indexed publications. In addition to publications, he has contributed innovative patents, such as systems for vehicle communication during accidents. His research skills encompass advanced AI modeling, simulation of communication networks, and interdisciplinary problem-solving in sustainable technologies. Mr. Alenazi is an active member of IEEE, AAAI (USA), AISB (UK), PMI, and the Saudi Council of Engineers, and he holds prestigious certifications including CCNA, CompTIA Security+ CE, and PMP. He has consistently demonstrated leadership in academia and professional communities, bridging industry and research while mentoring students. With a growing academic profile of 28 citations, 7 documents, and an h-index of 3, he is well-positioned for continued impact and recognition in his field.

Profiles: Google Scholar | Scopus | ORCID  | ResearchGate

Featured Publications

  1. Alenazi, M. M., Yosuf, B. A., El-Gorashi, T., & Elmirghani, J. M. H. (2020). Energy efficient neural network embedding in IoT over passive optical networks. 2020 22nd International Conference on Transparent Optical Networks (ICTON), 1–6. Cited by: 13

  2. Yosuf, B. A., Mohamed, S. H., Alenazi, M. M., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient AI over a virtualized cloud fog network. Proceedings of the Twelfth ACM International Conference on Future Energy Systems. Cited by: 11

  3. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient distributed machine learning in cloud fog networks. 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), 935–941. Cited by: 9

  4. Banga, A. S., Alenazi, M. M., Innab, N., Alohali, M., Alhomayani, F. M., Algarni, M. H., & others. (2024). Remote cardiac system monitoring using 6G-IoT communication and deep learning. Wireless Personal Communications, 136(1), 123–142. Cited by: 4

  5. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2022). Energy efficient placement of ML-based services in IoT networks. 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). Cited by: 4

Shayesteh Tabatabaei | Computer Science | Women Researcher Award

Assoc. Prof. Dr. Shayesteh Tabatabaei | Computer Science | Women Researcher Award

Doctored at University of Saravan, Iran

Assoc. Prof. Dr. Shayesteh Tabatabaei is a distinguished computer engineering researcher, ranked among the top 2% of scientists worldwide in 2024. She holds a Ph.D. in Computer Engineering and specializes in Wireless Sensor Networks, Mobile Ad-Hoc Networks, IoT, and Optimization Algorithms. With numerous high-impact journal publications, she has significantly contributed to intelligent routing protocols and energy-efficient networking solutions. As an Associate Professor, she teaches advanced courses in Artificial Intelligence, Fuzzy Logic, and Distributed Systems while mentoring students and researchers. Recognized as a top researcher multiple times, she has also led workshops on ISI article writing, IoT, and wireless networks. Her expertise in computational methodologies and commitment to knowledge dissemination make her a key figure in her field. Dr. Tabatabaei’s research excellence, leadership, and dedication to innovation make her a strong candidate for prestigious academic awards, with potential for further global collaborations and industry-driven research initiatives.

Professional Profile 

Education

Assoc. Prof. Dr. Shayesteh Tabatabaei holds a Ph.D. in Computer Engineering from Tehran Science and Research University, Iran, earned in 2015 with an outstanding GPA of 18.63/20. Her doctoral research focused on developing intelligent routing protocols for mobile ad-hoc networks under the supervision of Dr. M. Teshnehlab. She completed her M.Sc. in Computer Engineering at Islamic Azad University of Shabestar in 2009, where she improved the AODV routing protocol using reinforcement learning, achieving a GPA of 18.69/20. Her academic journey began with a B.Sc. in Computer Engineering from the same university, graduating in 2006 with a GPA of 17.12/20. Throughout her education, Dr. Tabatabaei demonstrated excellence in research and innovation, particularly in wireless networks and intelligent algorithms. Her strong academic background has shaped her expertise in computer engineering, making her a leading researcher and educator in the field of network optimization, IoT, and artificial intelligence.

Professional Experience

Assoc. Prof. Dr. Shayesteh Tabatabaei is a highly accomplished academic and researcher in computer engineering, currently serving as an Associate Professor in the Department of Computer Engineering at the Higher Education Complex of Saravan, Iran. With extensive teaching experience, she has instructed both undergraduate and postgraduate courses in Artificial Intelligence, Fuzzy Logic, Distributed Systems, Advanced Database Systems, and Programming Languages such as C, C++, Python, and SQL. Her research focuses on Wireless Sensor Networks, Mobile Ad-Hoc Networks, IoT, and Optimization Algorithms, with numerous high-impact journal publications and conference presentations. She has been recognized multiple times as a top researcher and has actively contributed to academic development by organizing workshops on ISI article writing, IoT, and wireless networks. Dr. Tabatabaei’s expertise extends to computational simulations and algorithm development, making her a leading figure in her field. Her dedication to education, research, and innovation continues to influence the next generation of computer engineers.

Research Interest

Assoc. Prof. Dr. Shayesteh Tabatabaei’s research interests lie at the intersection of intelligent computing and network optimization, focusing on Wireless Sensor Networks (WSNs), Mobile Ad-Hoc Networks (MANETs), Internet of Things (IoT), and Intelligent Algorithms. Her work aims to enhance the efficiency, reliability, and security of communication networks through advanced routing protocols, optimization algorithms, and artificial intelligence techniques. She has contributed significantly to energy-aware clustering, fault tolerance mechanisms, and adaptive routing in WSNs, utilizing machine learning, fuzzy logic, and evolutionary computing. Additionally, her research explores optimization algorithms such as Genetic Algorithms, Bee Colony Optimization, and Social Spider Optimization to improve network performance. Through her extensive publications in high-impact journals and conferences, Dr. Tabatabaei continues to advance the field of computational intelligence and networked systems. Her passion for innovation drives her to develop cutting-edge solutions for real-world challenges in modern communication technologies.

Award and Honor

Assoc. Prof. Dr. Shayesteh Tabatabaei has received multiple awards and honors in recognition of her outstanding contributions to research and academia. She has been ranked among the top 2% of scientists worldwide in 2024, highlighting her global impact in computer engineering. She has been recognized as the Top Researcher at various institutions multiple times, including Islamic Azad University of Malekan Branch in 2011, 2016, and 2017, and the Higher Education Complex of Saravan in 2019, 2021, and 2022. Her achievements reflect her dedication to advancing knowledge in wireless sensor networks, optimization algorithms, and artificial intelligence. In addition to her research excellence, she has led training workshops and mentored young scholars, further solidifying her reputation as a leader in her field. Her numerous accolades demonstrate her commitment to innovation, making her a strong candidate for prestigious academic and scientific awards on both national and international levels.

Research Skill

Assoc. Prof. Dr. Shayesteh Tabatabaei possesses strong research skills in computer engineering, wireless communication, and intelligent systems. Her expertise spans algorithm design, network optimization, artificial intelligence, and data analysis, with a particular focus on Wireless Sensor Networks (WSNs), Mobile Ad-Hoc Networks (MANETs), IoT, and optimization techniques. She is proficient in developing energy-efficient routing protocols, fault-tolerant clustering methods, and machine learning-based optimization algorithms. Dr. Tabatabaei has extensive experience with simulation tools such as MATLAB, R, Opnet, and GloMoSim, which she utilizes to validate her research findings. Additionally, she is skilled in multiple programming languages, including C, C++, Python, JavaScript, SQL, and Oracle, enabling her to implement and test computational models effectively. Her ability to integrate fuzzy logic, evolutionary algorithms, and artificial intelligence into network solutions showcases her innovative approach to problem-solving, making her a highly capable and influential researcher in the field.

Conclusion

Dr. Shayesteh Tabatabaei is highly qualified for the Women Researcher Award, given her global recognition, extensive research contributions, leadership in academia, and dedication to advancing knowledge in computer engineering. Strengthening international collaborations and industry partnerships could further elevate her impact.

Publications Top Noted

  • A novel fault tolerance energy-aware clustering method via social spider optimization (SSO) and fuzzy logic and mobile sink in wireless sensor networks (WSNs).

    • Cited by: 65
    • Year: 2020
  • A novel energy-aware clustering method via Lion Pride Optimizer Algorithm (LPO) and fuzzy logic in wireless sensor networks (WSNs).

    • Cited by: 50
    • Year: 2019
  • Proposing an energy-aware routing protocol by using fish swarm optimization algorithm in WSN (wireless sensor networks).

    • Cited by: 47
    • Year: 2021
  • A new method to find a high reliable route in IoT by using reinforcement learning and fuzzy logic.

    • Cited by: 36
    • Year: 2020
  • Reliable routing algorithm based on clustering and mobile sink in wireless sensor networks.

    • Cited by: 30
    • Year: 2019
  • A novel method for clustering in WSNs via TOPSIS multi-criteria decision-making algorithm.

    • Cited by: 23
    • Year: 2020
  • Improved routing vehicular ad-hoc networks (VANETs) based on mobility and bandwidth available criteria using fuzzy logic.

    • Cited by: 20
    • Year: 2020
  • A new routing protocol to increase throughput in mobile ad hoc networks.

    • Cited by: 20
    • Year: 2015
  • Provide energy-aware routing protocol in wireless sensor networks using bacterial foraging optimization algorithm and mobile sink.

    • Cited by: 19
    • Year: 2022