Junaid Akram | Computer Science | Research Excellence Award

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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.

Citation Metrics (Scopus)

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Citations
615

Documents
37

h-index
15


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Fabrice Vidal | Computer Science | Research Excellence Award

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Fabrice Vidal | Computer Science | Research Excellence Award

Hospital center|France

Dr. Fabrice Vidal is a hospital pharmacist with extensive experience in hospital pharmacy management, medication safety, and healthcare quality systems. Since 2014, he has served as a Hospital Practitioner and Head of the Pharmacy for Internal Use at the Centre Hospitalier de Dax, where he plays a central role in ensuring the safe, efficient, and compliant use of medicines and medical devices. His responsibilities include the management of medical devices, oversight of pharmaceutical logistics, and active participation in the quality management of medication use and the overall medication care pathway. He has contributed significantly to the modernization of hospital pharmacy operations through involvement in the implementation of a Warehouse Management System and the development and deployment of computerized prescribing decision-support software. His expertise also extends to the validation of chemotherapy prescriptions, ensuring adherence to clinical protocols and patient safety standards. In addition, he has participated in pharmaceutical on-call duties related to medication safety and emergency preparedness. Prior to his permanent appointment, he worked as a full-time contractual Hospital Practitioner at the same institution, gaining strong operational experience within the Pharmacy for Internal Use.


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Yanxia Liang | Computer Science | Research Excellence Award

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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.

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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.

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.

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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

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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.

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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.

Chengyuan Zhang | Computer Science | Best Research Article Award

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Chengyuan Zhang | Computer Science | Best Research Article Award

Chengyuan Zhang, Hunan University, China

Dr. Chengyuan Zhang, born in October 1985 in Suining County, Hunan, is an Associate Professor and Ph.D. Supervisor at the College of Computer Science and Electronic Engineering, Hunan University. He also serves as a Guest Professor at the College of Information and Intelligence, Hunan Agricultural University. Dr. Zhang earned his Ph.D. in Computer Science from the University of New South Wales, where he also completed his master’s studies under the guidance of Professors Xuemin Lin and Wenjie Zhang. His professional experience includes academic roles in both China and Australia, notably as a postdoctoral researcher. With nearly 40 SCI-indexed publications and over 1,300 citations, he is widely recognized in the domains of multimedia computing, spatio-temporal data analysis, and machine learning. Dr. Zhang is a frequent reviewer for top-tier journals and has contributed to numerous national and provincial-level research projects. He holds editorial and committee roles in several internationally recognized journals and conferences.

Publication Profile

scopus

Education

Dr. Chengyuan Zhang began his academic journey in Software Engineering at Sun Yat-sen University, earning his bachelor’s degree in 2008. He then advanced to the University of New South Wales (UNSW), one of Australia’s leading research institutions, where he obtained a master’s degree in Computer Science in 2011. His academic excellence led him to pursue a Ph.D. in Computer Science at UNSW from July 2011 to December 2015. During his doctoral studies, he worked under the mentorship of esteemed professors Xuemin Lin and Wenjie Zhang. His research during this period laid the groundwork for his future academic contributions in data mining, multimedia computing, and spatio-temporal data processing. The international education experience at UNSW not only honed his technical expertise but also equipped him with a global research perspective and collaborative mindset, helping him transition smoothly into high-impact academic roles upon returning to China.

Experience

Dr. Chengyuan Zhang currently holds the position of Associate Professor and Deputy Director in the Department of Computer Science at Hunan University. He joined the university in December 2019 and assumed his deputy directorship in November 2023. Previously, he served as a Lecturer at the College of Information Science and Engineering at Central South University from 2016 to 2019. His international experience includes a one-year postdoctoral research position at the University of New South Wales from 2015 to 2016, where he continued his research on large-scale data processing. Over the years, Dr. Zhang has participated in and led various nationally funded projects, especially in multimedia, graph data analysis, and AI-driven spatio-temporal applications. He also contributes significantly to the academic community as an editor and reviewer for top journals such as IEEE TKDE, ACM TOIS, and IEEE TNNLS. His career reflects a balanced combination of research innovation, teaching, and academic leadership.

Awards and Honors

Dr. Chengyuan Zhang has received several recognitions for his scholarly contributions, including the Outstanding Reviewer Award from Pattern Recognition Letters, which highlights his dedication to academic quality and peer review. He serves as Academic Editor for Advances in Multimedia and as a Guest Editor for prestigious journals such as Multimedia Tools and Applications and Mathematics. Dr. Zhang has also contributed as a Reviewer for top-tier academic journals including IEEE TPAMI, IEEE TNNLS, IEEE TKDE, ACM TOIS, and ACM TOMM. In addition, he has served as a Program Committee Member for leading international conferences such as ACM Multimedia and IJCAI-PRICAI. These roles are a testament to his academic credibility and recognition within the global research community. His grant-winning research and participation in national foundations reflect his active role in shaping future advancements in multimedia, data mining, and artificial intelligence.

Research Focus

Dr. Chengyuan Zhang’s research centers on multimedia computing, spatio-temporal multi-modal data analysis, image processing, graph data analysis, and machine learning. His work aims to address complex challenges related to information retrieval, representation learning, and knowledge discovery from large-scale, heterogeneous datasets. Specifically, he focuses on designing efficient algorithms for spatio-temporal queries, cross-modal hashing retrieval, and dynamic image enhancement techniques. His research often integrates deep learning, graph theory, and semantic correlation mining, contributing to both theoretical advancements and real-world applications—especially in areas like intelligent agriculture, social recommendation systems, and wireless sensor networks. With support from multiple National Natural Science Foundation of China (NSFC) grants and Hunan provincial research programs, his work is recognized as both innovative and impactful. He has published nearly 40 papers in top journals and conferences such as IEEE TKDE, ACM TOIS, and ACM TOMM, earning over 1,300 citations and an H-index of 18.

Publication Top Notes

  1. 📖 MvHAAN: Multi-view hierarchical attention adversarial network for person re-identification – World Wide Web, 2024

  2. 📖 Bi-Direction Label-Guided Semantic Enhancement for Cross-Modal Hashing – IEEE TCSVT, 2024

  3. 🖼 Using CNN with Multi-Level Information Fusion for Image Denoising – Electronics, 2023

  4. 🖼 Adaptive Dynamic Shuffle Convolutional Parallel Network for Image Super-Resolution – Electronics, 2024

  5. 📷 Efficient Feature Redundancy Reduction for Image Denoising – World Wide Web, 2024

  6. 🔍 Efficient Maximal Biclique Enumeration on Large Uncertain Bipartite Graphs – IEEE TKDE, 2023

  7. 🔍 Efficient Maximum Edge-Weighted Biclique Search on Large Bipartite Graphs – IEEE TKDE, 2022

  8. 🤖 Robust Sparse Weighted Classification for Crowdsourcing – IEEE TKDE, 2022

  9. 🌐 Multi-Graph Heterogeneous Interaction Fusion for Social Recommendation – ACM TOIS, 2022

  10. 🌐 Scaling High-Quality Pairwise Link-Based Similarity Retrieval on Billion-Edge Graphs – ACM TOIS, 2022