Mohammed M Alenazi | Computer Science and Artificial Intelligence | Best Researcher Award

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Dr. Mohammed M Alenazi | Computer Science and Artificial Intelligence | Best Researcher Award

Assistance Professor | University of Tabuk | Saudi Arabia

Dr. Mohammed M. Alenazi is an accomplished researcher and academic specializing in Artificial Intelligence, Machine Learning, and Energy Optimization in IoT Networks. He holds a PhD in Electrical and Electronics Engineering from the University of Leeds, a Master of Engineering in Computer Engineering from the Florida Institute of Technology, a Bachelor of Engineering in Computer Engineering from Prince Fahd Bin Sultan University, and an Associate Degree in Electrical/Electronics Equipment Installation and Repair from Tabuk College of Technology. He began his professional career as a Senior Engineer at Saudi Telecom Company, gaining extensive experience in optical fiber networks and large-scale communication systems, and later transitioned into academia as a Teaching Assistant at Northern Border University and the University of Tabuk, where he currently serves as an Assistant Professor. His research interests focus on energy-efficient deployment of ML-based services in IoT networks, neural network embedding in passive optical networks, and AI-driven intelligent systems, including a patented vehicle safety communication system. Dr. Alenazi has published in reputed journals and conferences including IEEE Xplore and ResearchGate and actively participates in professional organizations such as IEEE, AAAI, AISB, the Saudi Council of Engineers, and the Project Management Institute, holding certifications including CCNA, CompTIA Security+, and PMP. He has received awards and honors for his contributions to AI and networking research, and his skills include machine learning, energy optimization, teaching, and project management. His research has received 28 citations across 8 documents, with an h-index of 3, reflecting his growing academic impact and influence in the field.

Profiles : ORCID | Scopus | Google Scholar | 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.

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.

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.

4.Banga, A. S., Alenazi, M. M., Innab, N., Alohali, M., Alhomayani, F. M., Algarni, M. H., et al. (2024). Remote cardiac system monitoring using 6G-IoT communication and deep learning.

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.

Venus Haghighi | Computer Science | Best Researcher Award

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Mrs. Venus Haghighi | Computer Science | Best Researcher Award

Research Associate | Macquarie University | Australia

Mrs. Venus Haghighi is a final-year PhD candidate in Computer Science at Macquarie University, Sydney, focusing on artificial intelligence, data science, and graph learning techniques for fraud detection in complex networks. She holds a master’s degree in Computer Engineering from Isfahan University of Technology, where she worked on mobile cloud computing, and a bachelor’s degree in Computer Engineering from Shahid Bahonar University of Kerman, where she researched AES cryptography. Her professional experience includes serving as a data science researcher at the Intelligent Computing Laboratory, where she develops advanced graph neural networks, hypergraph models, and large language model enhanced frameworks for detecting camouflaged malicious actors. She has also contributed as a sessional teaching associate in both the School of Computing and the Business School at Macquarie University, teaching subjects such as cybersecurity, data science, and information systems. Her research interests span graph neural networks, hypergraph learning, graph transformer networks, graph representation learning, and the integration of LLMs with graph-based methods for real-world applications. She has published in leading venues such as IEEE ICDM, ACM WSDM, ACM CIKM, IJCAI, and ACM Web Conference, along with journal contributions in IEEE Transactions and IEEE Access. Her achievements include the Google Conference Travel Grant, HDR Research Rising Star Award, 3MT Thesis Competition recognition, DF-CRC PhD Top-Up Scholarship, and the Pro-Vice Chancellor Research Excellence Scholarship. She is skilled in Python, PyTorch, PyTorch Geometric, Deep Graph Library, data visualization, and advanced AI model design. Her research impact is evidenced by 150 citations across 13 documents with an h-index of 4.

Profile: Google Scholar

Featured Publications

1. Soltani, B., Haghighi, V., Mahmood, A., Sheng, Q. Z., & Yao, L. (2022). A survey on participant selection for federated learning in mobile networks. Proceedings of the 17th ACM Workshop on Mobility in the Evolving Internet Architecture (MobiArch).

2. Haghighi, V., & Moayedian, N. S. (2018). An offloading strategy in mobile cloud computing considering energy and delay constraints. IEEE Access, 6, 11849–11861.

3. Shabani, N., Wu, J., Beheshti, A., Sheng, Q. Z., Foo, J., Haghighi, V., Hanif, A., & … (2024). A comprehensive survey on graph summarization with graph neural networks. IEEE Transactions on Artificial Intelligence, 5(8), 3780–3800.

4. Soltani, B., Zhou, Y., Haghighi, V., & Lui, J. (2023). A survey of federated evaluation in federated learning. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI).

5. Shabani, N., Beheshti, A., Jolfaei, A., Wu, J., Haghighi, V., Najafabadi, M. K., & Foo, J. (2024). Attention-based graph summarization for large-scale information retrieval. IEEE Transactions on Consumer Electronics, 70(3), 6224–6235.

LI Ma | Computer Science | Best Researcher Award

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Prof. LI Ma | Computer Science | Best Researcher Award

Professor at North China University of Technology Beijing, China

Prof. Li Ma  is a distinguished Professor and Dean of the School of Information Science at North China University of Technology, Beijing.  He also serves as a Doctoral Supervisor at Beijing University of Technology. With over three decades of academic and research contributions, Prof. Ma has authored and co-authored more than  journal and conference papers.  His scholarly journey began with a B.S. degree from Beijing Institute of Technology , followed by an M.S. from North University of China , and a Ph.D. from Beijing Institute of Technology . His research spans artificial intelligence, advanced computing, and physical oceanography, integrating interdisciplinary approaches to solve complex challenges.  A recognized leader, he is a Distinguished Member of the China Computer Federation (CCF), and an active member of IoT committees, IEEE-CS, and ACM. Prof. Ma continues to guide innovation while mentoring the next generation of researchers.

Professional Profile

Scopus Profile

Education 

Prof. Li Ma academic foundation is built upon rigorous training at prestigious Chinese institutions.  He earned his B.S. degree from Beijing Institute of Technology, one of China’s leading centers for science and engineering education.  He then pursued his M.S. degree at North University of China, Shaanxi, where he further specialized in computational and information sciences. With a growing passion for advancing artificial intelligence and computing technologies, he returned to Beijing Institute of Technology for doctoral studies, successfully completing his Ph.D. During his doctoral journey, he focused on exploring advanced models and algorithms, setting the stage for his prolific academic career. This educational pathway provided him with a strong balance of theoretical expertise and applied research training, enabling him to later contribute significantly to AI, computational sciences, and interdisciplinary applications in fields such as physical oceanography.

Experience 

Prof. Li Ma professional journey reflects leadership in both academia and research.  Currently, he serves as Professor and Dean of the School of Information Science at North China University of Technology, Beijing, where he oversees academic development, curriculum innovation, and interdisciplinary research.  Additionally, he holds the position of Doctoral Supervisor at Beijing University of Technology, mentoring Ph.D. candidates and guiding cutting-edge projects in artificial intelligence and advanced computing.  His contributions extend beyond teaching and supervision he has authored over research papers, shaping knowledge in AI algorithms, model optimization, and computational sciences.  As an influential figure, he also leads academic innovation teams across Beijing municipal universities, fostering collaborative networks.  Beyond his institutional roles, he actively participates in professional societies such as CCF, IEEE-CS, and ACM, strengthening global research ties. With decades of experience, Prof. Ma continues to bridge science, technology, and education for future advancements.

Research Interest 

Prof. Li Ma research interests are diverse and interdisciplinary, bridging computer science with applied fields.  His core expertise lies in artificial intelligence technology, particularly in developing robust models that enhance accuracy, allocation algorithms, attention mechanisms, and bounding box optimization.  He also explores deep learning applications, focusing on classification head architectures, loss functions, and anchor boxes within image recognition systems, including real-world datasets like COCO.  Another dimension of his research extends to complex computational dependencies and buffer space optimization, enhancing the efficiency of AI-driven systems.  Uniquely, Prof. Ma also applies computational models to physical oceanography, integrating AI with environmental and marine sciences. This interdisciplinary approach highlights his vision of combining data science, machine learning, and computational modeling to solve critical problems across science and technology. His work reflects innovation at the crossroads of advanced computing, AI research, and environmental applications.

Award and Honor

Prof. Li Ma has earned recognition as a leading scholar and academic leader.  He is a Distinguished Member of the China Computer Federation (CCF), a prestigious acknowledgment of his contributions to computer science research and development in China. He is also an active member of IEEE Computer Society and ACM, which reflects his international engagement and commitment to advancing global standards in computing and AI.  Beyond memberships, Prof. Ma leads an Academic Innovation Team supported by Beijing Municipal Colleges and Universities, showcasing his leadership in fostering research excellence and interdisciplinary collaboration.  His roles as Dean and Doctoral Supervisor further illustrate the trust placed in him to shape future researchers and contribute to academic policy.  While specific individual awards were not listed in the available record, his professional honors demonstrate recognition at both national and international levels in AI, computing, and interdisciplinary science.

Research Skill

Prof. Li Ma possesses a broad range of advanced research skills that position him at the forefront of computer science and AI.  His expertise includes algorithm design and optimization, focusing on allocation methods, classification models, and bounding box refinement for image recognition tasks. He has strong command over deep learning frameworks, applying attention mechanisms, anchor boxes, and classification head models to improve accuracy and system performance. Additionally, his skills in large-scale dataset utilization (e.g., COCO dataset) enable him to test, validate, and refine machine learning models effectively.  His computational skills extend into buffer space optimization and handling complex dependencies, key for enhancing efficiency in AI-driven environments. Beyond technical areas, he demonstrates leadership in interdisciplinary applications, especially in using AI for physical oceanography and environmental modeling. These skills, combined with over publications, reflect his ability to merge theory with impactful real-world applications.

Publication Top Notes

Title: FedECP: Enhancing global collaboration and local personalization for personalized federated learning
Journal: Knowledge Based Systems
Year: 2025

Title: A verifiable EVM-based cross-language smart contract implementation scheme for matrix calculation
Journal: Digital Communications and Networks
Year: 2025

Title: Construction of Low-latency Artificial Intelligence of Things for Marine Meteorological Forecasting
Journal: Tien Tzu Hsueh Pao Acta Electronica Sinica
Year: 2025

Title: Blockchain-Based Trust Model for Inter-Domain Routing
Journal: Computers Materials and Continua
Year: 2025

Title: Multivariate Short-Term Marine Meteorological Prediction Model
Journal: IEEE Transactions on Geoscience and Remote Sensing
Year: 2025

Title: A trusted IoT data sharing method based on secure multi-party computation
Journal: Journal of Cloud Computing
Year: 2024

Title: Obstacle Avoidance Method Using DQN to Classify Obstacles in Unmanned Driving
Journal: Jisuanji Gongcheng (Computer Engineering)
Year: 2024

Title: A quantum artificial bee colony algorithm based on quantum walk for the 0-1 knapsack problem
Journal: Physica Scripta
Year: 2024

Title: MIMA: Multi-Feature Interaction Meta-Path Aggregation Heterogeneous Graph Neural Network for Recommendations
Journal: Future Internet
Year: 2024

Conclusion

Prof. Li Ma is an accomplished scholar in computer science, artificial intelligence, and computational technologies, currently serving as Dean of the School of Information Science at North China University of Technology and Doctoral Supervisor at Beijing University of Technology. With over  publications and  citations, his research contributions span AI model optimization, federated learning, blockchain systems, IoT, marine meteorological forecasting, and quantum-inspired algorithms.