Yanxia Liang | Computer Science | Research Excellence Award

西安邮电大学 | China

Dr. Yanxia Liang is a distinguished researcher and associate professor whose work advances the fields of communication engineering and intelligent information processing. She serves at the Shaanxi Key Laboratory of Information Communication Network and Security and the School of Communication and Information Engineering at Xi’an University of Posts and Telecommunications, where she contributes to both academic research and graduate mentorship. Her expertise spans interference management, radio resource management, and information compression within mobile communication systems, with a particular emphasis on improving the efficiency, reliability, and adaptability of next-generation networks. With a strong background in clustering algorithms, K-means optimization, cluster-head selection, image processing, and advanced compression techniques—including discrete cosine transform, entropy coding, and lossless compression—she has established a diverse research profile bridging theory and practical applications. Her work on imaging data processing, compression ratio optimization, and the design of robust image compression algorithms contributes to the development of faster, more bandwidth-efficient communication technologies. Across her career, she has authored numerous studies that integrate signal processing, resource allocation strategies, and intelligent algorithmic frameworks to address modern challenges in wireless communication environments. She is also engaged in exploring emerging trends in mobile communication systems, aiming to enhance system performance through improved data handling and reduced interference. Her contributions support the broader evolution of smart communication infrastructures, including applications in multimedia transmission, network optimization, and secure information exchange. Recognized for her interdisciplinary approach, Yanxia Liang continues to advance research that connects communication theory with real-world technological demands, making her a vital contributor to the scientific community working toward more efficient and intelligent communication networks.

Profile: Scopus

Featured Publications

Liang, Y., Sun, C., Jiang, J., Liu, X., He, H., & Xie, Y. (2020). An efficiency-improved clustering algorithm based on KNN under ultra-dense network. IEEE Access, 8. IEEE.

Liang, Y., Zhao, S., Liu, X., He, H., Zhao, X., & Wang, H. (2024). A balanced energy-efficient clustering strategy for WSNs. IEEE Sensors Journal, 24(22). IEEE.

He, H., Liang, Y., & Li, S. (2021). Clustering algorithm based on azimuth in mmWave massive MIMO–NOMA system. In 2021 IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE.

Liang, Y., Liu, X., Jiang, J., Du, J., Sun, C., & Xie, Y. (2020). A practical dynamic clustering scheme using spectral clustering in ultra-dense network. In 2020 IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE.

Liang, Y., Jia, T., Li, N., Liu, X., Jiang, J., Lu, G., & Zhao, M. (2024). Review of static image compression algorithms. In 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE.

Liu, X., & Liang, Y. (2021). A novel Moore–Penrose-inverse-matrix-based data compression method. In 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE.

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.

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