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

Published on by Citation Awards

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.

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.