Junaid Akram | Computer Science | Research Excellence Award

Junaid Akram | Computer Science | Research Excellence Award

Unsw College | Australia

Dr. Junaid Akram is a researcher in decentralized systems and trustworthy AI, with a PhD from the University of Sydney. His work focuses on building secure, privacy-preserving, and reliable trust mechanisms for safety-critical, crowdsourced platforms, particularly in drone-based services for environmental monitoring and emergency response. His research integrates blockchain and decentralized public key infrastructures, verifiable credentials, adversarial machine learning, and data analytics to improve security, fairness, and resilience in multi-sided ecosystems. Through frameworks such as decentralized identity management, tamper-resistant reputation systems, anomaly detection, and robust graph learning, his contributions aim to reduce operational risk while enhancing participation and service quality in emerging digital platforms.

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Citations
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Documents
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Muhammad Suleman Memon | Computer Science | Most Cited Article Award

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.

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.