Saad Ezzini |Artificial Intelligence | Research Excellence Award

Assist. Prof. Dr. Saad Ezzini |Artificial Intelligence | Research Excellence Award

King Fahd University of Petroleum and Minerals | Saudi Arabia

Assist. Prof. Dr. Saad Ezzini is a Computer Science researcher specializing in software engineering, artificial intelligence, large language models, and natural language processing, with a strong focus on AI-driven automation and intelligent software systems. His research addresses key challenges in requirements engineering, ambiguity handling, and data-centric software analysis, with applications across multiple interdisciplinary domains. He has published extensively in leading international journals and top-tier conferences, demonstrating high research quality and relevance. His scholarly work has received 655 citations overall and 616 citations, reflecting strong and sustained research visibility. He holds an h-index of 12, indicating consistent academic impact. He also maintains an i10-index of 16, highlighting the breadth of his influential publications. Overall, his research profile demonstrates significance, impact, and leadership in contemporary AI and software engineering research.

Citation Metrics (Google Scholar)

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Citations
655

Documents
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h-index
12

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Featured Publications

Rounak Raman | Information Technology | Young Scientist Award

Mr. Rounak Raman | Information Technology | Young Scientist Award

Netaji Subhas University of Technology | India

Mr. Rounak Raman research journey reflects a diverse and impactful engagement across multidisciplinary domains including Artificial Intelligence, Wireless Sensor Networks, IoT Security, and Neuroinformatics. At the forefront of innovation, multiple projects demonstrate a strong alignment with real-world challenges and technical excellence. The development of the EEG Data Analysis System and multimodal neurofeedback loop underlines expertise in biomedical signal processing and applied AI for cognitive enhancement, contributing to measurable improvements in attentiveness and engagement. In IoT and network optimization, the implementation of the Energy Aware Hybrid Clustering Protocol (EAHCP) showcased advancements in energy-efficient communication, achieving notable performance gains in scalability and mobility management. Research on secure IoT frameworks, such as the Hierarchical Key Rotation and Isolation Protocol (HKRISRP), reinforced resilience in wireless networks through lightweight cryptography and dynamic key rotation, enhancing both security and energy performance. Additional contributions in opportunistic networks introduced context-aware, trust-based, and collision-avoidant models that optimized data aggregation and reliability through intelligent decision mechanisms and trajectory optimization. The pursuit of Generative AI culminated in the development of SyntheX, a document analysis system integrating semantic search and transcription models to streamline enterprise knowledge management. Collectively, these works exemplify a strong commitment to advancing applied research in AI, cybersecurity, and intelligent network systems

Featured Publication

Raman, R., Yadav, A., Kukreja, D., & Sharma, D. K. (2025). CONTEXT-NET: A context-aware nexus-based aggregation protocol for opportunistic networks. Internet of Things, 25, 101809. https://doi.org/10.1016/j.iot.2025.101809