Mohammed Al-Naeem | Computer Science | Best Researcher Award

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Mohammed Al-Naeem | Computer Science | Best Researcher Award

King Faisal University | Saudi Arabia

Dr. Mohammed Abdulaziz Al-Naeem is a dedicated scholar and researcher in computer science whose work spans wireless networks, information security, and advanced sensing technologies. He earned both his Master’s and Ph.D. degrees from Monash University, where his doctoral research focused on developing pattern transformation-invariant schemes for wireless sensor networks using an edge-detection, gradient-based mechanism—an innovative contribution that strengthened the robustness and adaptability of sensor-based systems. His academic journey began with a Bachelor of Science from King Faisal University, an institution to which he has remained professionally committed throughout his career. After joining the university as a Teaching Assistant, he steadily progressed through key academic roles, later serving as a Lecturer and ultimately as an Assistant Professor, a position he has held since 2016. Across these roles, he has contributed significantly to teaching, mentoring, and research development within the Department of Computer Science. Dr. Al-Naeem’s academic expertise centers on wireless networks, network security, and the design of resilient sensing and communication frameworks. His research interests integrate theoretical foundations with practical applications, with a focus on secure, efficient, and scalable systems capable of supporting modern intelligent environments. His work reflects an enduring commitment to advancing computational methodologies and enhancing the reliability of networked systems across diverse contexts. He is also proficient in both Arabic and English, enabling him to engage with a wide scholarly community and collaborate on international research initiatives. Through his academic leadership, research contributions, and dedication to advancing knowledge in wireless communication and cybersecurity, Dr. Al-Naeem continues to play an active and impactful role in shaping the next generation of technological innovation.

Profile: Orcid | Scopus | Google scholar

Featured Publications

Alsmadi, I., Aljaafari, N., Nazzal, M., Alhamed, S., Sawalmeh, A. H., Vizcarra, C. P., … [add remaining authors if needed]. (2022).
Adversarial machine learning in text processing: A literature survey. IEEE Access, 10, 17043–17077.

Almusallam, N., Tari, Z., Chan, J., Fahad, A., Alabdulatif, A., & Al-Naeem, M. (2021).
Towards an unsupervised feature selection method for effective dynamic features. IEEE Access, 9, 77149–77163.

Al-Naeem, M. A. (2021).
Prediction of re-occurrences of spoofed ACK packets sent to deflate a target wireless sensor network node by DDOS. IEEE Access, 9, 87070–87078.

Rana, M. U., Shah, M. A., Al-Naeem, M. A., & Maple, C. (2024).
Ransomware attacks in cyber-physical systems: Countermeasure of attack vectors through automated web defenses. IEEE Access, 12, 149722–149739.

Usman Ashraf, U. M., Ahmed, A., & Al-Naeem, M. (2021).
Reliable and QoS aware routing metrics for wireless neighborhood area networking in smart grids. Computer Networks, Article 14.
(If volume/issue/page numbers exist, please provide to complete the citation.)

Al-Naeem, M., Rahman, M. M. H., Banerjee, A., & Sufian, A. (2023/2024?).
Support vector machine-based energy efficient management of UAV locations for aerial monitoring of crops over large agriculture lands. Sustainability, 15(8), 6421.

Muhammad Suleman Memon | Computer Science | Most Cited Article Award

Published on by Citation Awards

Muhammad Suleman Memon | Computer Science | Most Cited Article Award

University of Sindh,Jamshoro | Pakistan

Dr. Muhammad Suleman Memon is an accomplished academic and researcher in the fields of Artificial Intelligence, Computer Vision, and Deep Learning, currently serving as an Assistant Professor and Incharge of the Department of Information Technology at the University of Sindh, Dadu Campus. With over twelve years of academic and research experience, he has demonstrated a strong commitment to advancing digital innovation and academic excellence. He earned his Ph.D. in Computer Systems Engineering from Quaid-e-Awam University of Science and Technology, where his research focused on cutting-edge developments in AI-driven systems. His earlier academic background includes a Master’s in Information Technology and a Bachelor’s in Computer Systems Engineering from Mehran University of Engineering and Technology, Jamshoro. Dr. Memon’s research primarily explores Artificial Intelligence applications in healthcare and agriculture, deep learning-based image classification and segmentation, explainable AI (XAI), and the Internet of Things (IoT) for smart system development. He has contributed to the design and teaching of diverse courses, including Object-Oriented Programming, Artificial Intelligence, Web Engineering, and Data Science, fostering computational thinking and innovation among students. Beyond teaching and research, he has played key administrative and leadership roles such as Focal Person for national digital initiatives, Quality Enhancement Coordinator, and Web Administrator for the Dadu Campus. His leadership has been pivotal in enhancing academic quality, managing IT infrastructure, and supporting institutional modernization. Dr. Memon’s scholarly output includes publications in reputed journals, and his ongoing work reflects a deep interest in developing sustainable and explainable AI solutions to address real-world problems. His career exemplifies the integration of academic rigor, research innovation, and leadership in shaping the next generation of computing professionals.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

Lakhan, A., Mastoi, Q. U. A., Elhoseny, M., Memon, M. S., & Mohammed, M. A. (2022). Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT-assisted mobile fog cloud. Enterprise Information Systems, 16(7), 1883122.

Memon, M. S., Kumar, P., & Iqbal, R. (2022). Meta deep learn leaf disease identification model for cotton crop. Computers, 11(7), 102.

Lakhan, A., Memon, M. S., Mastoi, Q. U. A., Elhoseny, M., Mohammed, M. A., Qabulio, M., & Abdel-Basset, M. (2022). Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network. Cluster Computing, 1–23.

Mastoi, Q. U. A., Memon, M. S., Lakhan, A., Mohammed, M. A., Qabulio, M., Al-Turjman, F., & Abdulkareem, K. H. (2021). Machine learning–data mining integrated approach for premature ventricular contraction prediction. Neural Computing and Applications, 33, 11703–11719.

Mirani, A. A., Memon, M. S., Rahu, M. A., Bhatti, M. N., & Shaikh, U. R. (2019). A review of agro-industry in IoT: Applications and challenges. Quaid-E-Awam University Research Journal of Engineering, Science & Technology, Nawabshah, 17(1), 28–33.

Mirani, A., Memon, M. S., Chohan, R., Wagan, A. A., & Qabulio, M. (2021). Machine learning in agriculture: A review. LUME, 10, 5.

Memon, W. A., Mirani, A. A., Memon, M. S., & Sodhar, I. N. (2019). Comparative study of online learning management systems: A survey in Pakistan. Information Sciences Letters, 8(3), 111–120.