Chao Wang | Computer Science and Artificial Intelligence | Research Excellence Award

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Mr. Chao Wang | Computer Science and Artificial Intelligence | Research Excellence Award

North China University of Technology | China

Dr. Chao Wang, an accomplished Associate Professor at the North China University of Technology, is a distinguished researcher whose work significantly advances the fields of vehicular networks, IoT security, and edge computing. Holding a Ph.D. in Computer Science, Dr. Wang has developed a strong academic portfolio grounded in deep technical expertise and innovative thinking. His research addresses some of the most pressing challenges in intelligent transportation systems, focusing on secure data communication, privacy-preserving mechanisms, and efficient resource allocation in highly dynamic vehicular environments. With 23 publications in SCI and Scopus-indexed journals and conferences, his work demonstrates a consistent trajectory of high-quality scientific output. His research impact is further reflected in 660 citations, an H-index of 10, and an i10-index of 10, according to Google Scholar as of December 3, 2025. These metrics underscore his growing global influence and the relevance of his contributions to next-generation intelligent mobility systems. Dr. Wang has successfully completed and continues to lead multiple national and provincial research projects, focusing on enhancing the reliability, safety, and intelligence of connected vehicle ecosystems. His innovations include blockchain-based frameworks for secure traffic data management, anomaly detection systems for vehicle-to-vehicle communication, and privacy-preserving architectures for IoT-enabled transportation infrastructures. With four patents published or under process, he demonstrates strong translational capability, often transforming theoretical models into practical, real-world solutions. His collaborations with researchers from Springer Nature, IEEE, and various international universities highlight his interdisciplinary approach and commitment to advancing global research partnerships. Although he has not yet undertaken industry consultancy projects, Dr. Wang’s research outputs inherently serve industrial needs, especially in smart transportation, urban planning, and secure IoT deployment. He is also an active professional member of IEEE, contributing to the broader scientific community through peer review, academic exchanges, and participation in scholarly networks. Beyond research, Dr. Wang is dedicated to academic mentorship, guiding students who have achieved recognition in national-level competitions, illustrating his commitment to nurturing the next generation of innovators. With strong expertise, a solid publication record, impactful innovations, and a dedication to advancing secure and intelligent transportation systems, Dr. Wang exemplifies the qualities celebrated by the Research Excellence Award. His achievements reflect not only academic rigor but also societal relevance, making him a highly deserving nominee for this international honor.

Profile: Orcid

Featured Publications

Li, J., Wang, C., Seo, D., Cheng, X., He, Y., Sun, L., Xiao, K., & Huo, Y. (2021). Deep learning-based service scheduling mechanism for GreenRSUs in the IoVs. Wireless Communications and Mobile Computing, 2021, Article 7018486. https://doi.org/10.1155/2021/7018486

Wang, C. (2020). Destination prediction-based scheduling algorithms for message delivery in IoVs. IEEE Access, 8, 1–15. https://doi.org/10.1109/ACCESS.2020.2966494

Wang, C. (2018). A blockchain-based privacy-preserving incentive mechanism in crowdsensing applications. IEEE Access, 6, 1–12. https://doi.org/10.1109/ACCESS.2018.2805837

Wang, C. (2015). A reliable broadcast protocol in vehicular ad hoc networks. International Journal of Distributed Sensor Networks, 11(8), Article 286241. https://doi.org/10.1155/2015/286241

Wang, C. (2015). Ads dissemination in vehicular ad hoc networks. In 2015 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE. https://doi.org/10.1109/ICC.2015.7248890

Wang, C. (2014). Schedule algorithms for file transmission in vehicular ad hoc networks. In Wireless Algorithms, Systems, and Applications (pp. 135–147). Springer. https://doi.org/10.1007/978-3-319-07782-6_12

Wang, C. (2014). S-disjunct code-based MAC protocol for reliable broadcast in vehicular ad hoc networks. In 2014 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) (pp. 1–6). IEEE. https://doi.org/10.1109/IIKI.2014.66

Rajiv Dahiya | Business Analytics | Best Researcher Award

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Dr. Rajiv Dahiya | Business Analytics | Best Researcher Award

University of North Carolina at Pembroke | United States

Dr. Rajiv Dahiya is an Assistant Professor of Business Analytics at the Thomas College of Business and Economics, University of North Carolina at Pembroke. His research integrates the disciplines of Business Analytics, Decision Science, and Information Systems, focusing on how organizations can leverage data-driven strategies to enhance decision-making, develop dynamic capabilities, and improve firm performance. Drawing on over a decade of professional experience in enterprise systems and analytics, Dr. Dahiya combines theoretical insight with practical applications in quantitative modeling, data visualization, and predictive analytics. His primary research stream examines the strategic role of Big Data Analytics (BDA) and Analytics Human Capital (AHC) in driving competitive advantage and firm outcomes. Building on the Resource-Based and Knowledge-Based Views, his doctoral dissertation, “Firm Performance in the Era of Big Data Analytics: Effects of Analytics Human Capital on Firm Capabilities and Performance,” empirically investigates how analytics expertise within firms fosters superior performance through enhanced decision-making capabilities. This work contributes to the growing body of literature on digital transformation and has been recognized for addressing critical gaps in understanding the human element of analytics success. Dr. Dahiya has published and presented his work in reputed outlets such as the Journal of Strategy and Management, Journal of Strategy & Innovation, and the Southeast Decision Science Institute Proceedings. His studies explore emerging topics including text analytics for content analysis, social media analytics and organizational attractiveness, and data-driven risk modeling in online identity theft. Current projects under review extend his analytical framework to assess the impact of business analytics and decision-making capabilities on organizational performance and employ classification analytics to forecast the success of management practices in Eastern European firms. Methodologically, Dr. Dahiya employs empirical, data-centric approaches such as multivariate regression, structural modeling, and machine learning classification, integrating data from sources like WRDS and Thomson Reuters. His interdisciplinary expertise bridges data science, management theory, and information systems, providing a robust platform for applied business research. Through his work, Dr. Dahiya aims to advance the theoretical understanding and practical deployment of analytics-driven decision support systems in corporate environments. His ongoing contributions align with the mission of developing evidence-based managerial insights that support data-driven transformation in today’s competitive business landscape. He remains an active member of the Decision Sciences Institute (DSI) and INFORMS Analytics Society, promoting research collaboration and knowledge dissemination in analytics and management science.

Profile: Scopus

Featured Publications

Dahiya, R., Le, S., & Kroll, M. J. (2025). Big data analytics and firm performance: The effects of human capital and mediating firm capabilities. Journal of Strategy & Innovation, 36(1).

Dahiya, R., Le, S., Ring, K., & Watson, K. (2022). Big data analytics and competitive advantage: The strategic role of firm-specific knowledge. Journal of Strategy and Management (Special Issue: Digital Transformation & Knowledge Management).

Dahiya, R. (2025). Text analytics for content analysis: A case study in online privacy. Proceedings of the Southeast Decision Science Institute.

Dahiya, R. (2025). Social media analytics and organization attractiveness. Proceedings of the Southeast Decision Science Institute.

Dahiya, R., & Van Slyke, C. (2024). Examining the impacts of analytics human capital on firm performance. Proceedings of the Southeast Decision Science Institute.

Dahiya, R., Kroll, M., Le, S., & Duong, B. (2020). Impact of managerial and employee analytics human capital on firm performance: An empirical study. Proceedings of the Southern Management Association Annual Meeting, St. Pete Beach, FL, October 20–23, 2020.

Dahiya, R., & Van Slyke, C. (2019). Big data analytics and competitive advantage: A systematic literature review. Proceedings of the Decision Science Institute Annual Meeting, New Orleans, LA, November 21–24, 2019.

Lamia Fourati | Computer Science | Best Researcher Award

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Prof Dr. Lamia fourati | Computer Science | Best Researcher Award

Computer science and multimedia higher institute of Sfax | Tunisia

Prof. Lamia Chaari Fourati is a distinguished Professor of Telecommunications at the Higher Institute of Computer Science and Multimedia (ISIMS), Sfax University, Tunisia, and a senior researcher at the Digital Research Center of Sfax (CRNS) within the SM@RTS Laboratory. With over two decades of academic and research excellence, she has established herself as one of North Africa’s leading figures in wireless communications, intelligent network systems, and AI-driven communication technologies. Her research is centered on the design, optimization, and security of wireless networks—including Wireless Body Area Networks (WBANs), Internet of Things (IoT), UAV-based networks (FANETs), and Internet of Vehicles (IoV). She has significantly contributed to developing MAC and routing protocols, trust management frameworks, and privacy-preserving communication systems for next-generation wireless infrastructures. Her innovative work also spans AI, machine learning, reinforcement learning, blockchain, and federated learning, applied to 6G, edge, and cloud computing ecosystems, with a focus on security, reliability, and energy efficiency. Prof. Fourati’s international collaborations are extensive. She serves as a Working Group Member in European COST Actions such as INTERACT (CA20120) and CA22168 on Physical Layer Security for Trustworthy and Resilient 6G Systems. She has led and participated in multiple sponsored projects on AI-based secure frameworks for UAVs, trust management in IoV, and smart water systems, in partnership with institutions like University of Troyes (France) and University of Aveiro (Portugal). Her research has also been pivotal in developing autonomous and energy-efficient communication systems for smart cities, vehicular networks, and healthcare applications. A dedicated educator and mentor, Prof. Fourati has supervised numerous Master’s and Ph.D. theses and has served on national and international doctoral juries. Her global academic engagements include visiting professorships in India, delivering advanced courses on LLMs, generative AI, network security, IoT, and mobile networks, as well as invited tutorials and talks at major IEEE and international events such as NoF, CRiSIS, IEEE ICBC, and ICHI. Her distinguished contributions have earned her prestigious recognitions, including the African Union Kwame Nkrumah Regional Scientific Award (2016), Miss.Africa Seed Fund Award (2018), Burj Kallel Award for Best Researcher (2019), and Outstanding Woman in Tech – North Africa (2021). In 2024, she was named among the Top Tunisian Women in Tech. An IEEE Senior Member and active participant in organizations such as OWSD, N2Women, ISOC, and ITU, Prof. Fourati continues to inspire the next generation of researchers and women in STEM. Her work integrates sustainable development, green computing, and ethical AI, aiming to build a future where intelligent, secure, and inclusive communication systems empower societies globally.

Profiles: Scopus | Orcid

Featured Publications

Fourati, L. C. (2024). Investigation of security threat datasets for intra- and inter-vehicular environments. Sensors, 24(11), 3431. https://doi.org/10.3390/s24113431

Fourati, L. C. (2022). Analysis of LoRaWAN 1.0 and 1.1 protocols security mechanisms. Sensors, 22(10), 3717. https://doi.org/10.3390/s22103717

Fourati, L. C. (2022). A genetic algorithm-based intelligent solution for water pipeline monitoring system in a transient state. Concurrency and Computation: Practice and Experience, 34(21), e5959. https://doi.org/10.1002/cpe.5959

Fourati, L. C. (2022). Cyber-physical systems for structural health monitoring: Sensing technologies and intelligent computing. Journal of Supercomputing, 78, 15126–15153. https://doi.org/10.1007/s11227-021-03875-5

Fourati, L. C. (2022). Investigation on vulnerabilities, threats and attacks prohibiting UAVs charging and depleting UAVs batteries: Assessments and countermeasures. Ad Hoc Networks, 131, 102805. https://doi.org/10.1016/j.adhoc.2022.102805

Fourati, L. C. (2021). 5G network slicing: Fundamental concepts, architectures, algorithmics, projects practices, and open issues. Concurrency and Computation: Practice and Experience, 33(24), e6352. https://doi.org/10.1002/cpe.6352

Fourati, L. C. (2021). A convoy of ground mobile vehicles protection using cooperative UAVs-based system. In 2021 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1–6). IEEE. https://doi.org/10.1109/ISNCC52172.2021.9615724

Fourati, L. C. (2021). A survey of 5G network systems: Challenges and machine learning approaches. International Journal of Machine Learning and Cybernetics, 12, 1–27. https://doi.org/10.1007/s13042-020-01178-4

Fourati, L. C. (2021). Blockchain-based trust management approach for IoV. In Lecture Notes in Networks and Systems (Vol. 206, pp. 455–467). Springer. https://doi.org/10.1007/978-3-030-75100-5_42

Ramkumar Kalyanaraman | Computer Science | Outstanding Scientist Award

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Prof. Dr. Ramkumar Kalyanaraman | Computer Science | Outstanding Scientist Award

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology | India

Dr. K. Ramkumar is a distinguished academician, researcher, and innovator with over twenty-three years of rich teaching and research experience in the field of Engineering and Computer Science. He is presently serving as a Professor in the Department of Computer Science and Engineering at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India. His illustrious academic journey is marked by consistent dedication to research, innovation, and academic excellence. He obtained his Doctor of Philosophy (Ph.D.) in Computer Science and Engineering from Manonmaniam Sundaranar University, Tirunelveli, in 2018, specializing in Security and Privacy in Cloud Computing, a domain of critical importance in the digital era. To further enhance his expertise and broaden his research perspectives, he pursued a Post-Doctoral Fellowship (PDF) at the Federal University of Ceará, Fortaleza, Brazil, in 2023, focusing on Artificial Intelligence and Biomedical Data Analytics. Throughout his career, Dr. Ramkumar has held several prestigious leadership positions in academia, contributing extensively to institutional growth and quality enhancement. He has served as Professor and Head of the Department (CSE) at Rajalakshmi Institute of Technology, Chennai, and earlier as Professor and Associate Dean (Engineering & Technology) at SRM University, Delhi-NCR, Sonepat, Haryana, where he also chaired the Board of Studies. His earlier academic affiliations include SRM Institute of Science and Technology (Vadapalani Campus, Chennai), Kings Engineering College, and Indira Institute of Engineering and Technology, where he also functioned as Director of Placements. His industry exposure includes working with global technology companies such as Satyam Computer Services Ltd. and LogicaCMG Pvt. Ltd. (now CGI), where he held roles as Associate Consultant, IT Consultant, and Project Leader, leading large technical teams across onshore and offshore environments. Dr. Ramkumar’s research interests encompass a broad spectrum of emerging technologies including Cloud Computing Security, Artificial Intelligence, Machine Learning, IoT Frameworks, Blockchain Systems, and Biomedical Data Analytics. His prolific research output includes numerous publications in SCI, Scopus, and Web of Science-indexed journals, with several articles published in reputed platforms such as Elsevier, Springer, Taylor & Francis, and IEEE. His recent works focus on AI-based diabetic risk prediction, intelligent human activity recognition for assistive technologies, quantum image encryption, and deep learning applications for medical imaging. According to his Google Scholar profile, Dr. Ramkumar has achieved over 1,327 citations, with an h-index of 18 and an i10-index of 24, reflecting the global impact and scholarly recognition of his research contributions. His academic influence extends beyond publications, as he has co-supervised several Post-Doctoral Fellows at the Singapore Institute of Technology, demonstrating his commitment to mentoring and nurturing emerging researchers. Dr. Ramkumar has also published and been granted multiple patents across domains such as wireless sensor networks, mobile ad hoc networks, IoT-based monitoring systems, AI-driven diagnostic tools, and environmental pollution control mechanisms, reflecting his strong inclination toward innovation-driven applied research. Dr. K. Ramkumar stands as a dynamic academic leader whose contributions bridge academia, research, and industry, exemplifying excellence in technological innovation, knowledge dissemination, and professional leadership. His remarkable blend of teaching expertise, research achievements, and administrative acumen continues to inspire students, scholars, and peers across the global academic and scientific community.

Profiles: Scopus | Google Scholar

Featured Publications

Ramkumar, K. (2022). A comparative analysis of methods of endmember selection for use in subpixel classification: A convex hull approach. Computational Intelligence and Neuroscience, 2022, Article ID 3770871

Ramkumar, K., Ananthi, N., Brabin, D. R. D., Goswami, P., Baskar, M., & Bhatia, K. K. (2021). Efficient routing mechanism for neighbour selection using fuzzy logic in wireless sensor network. Computers & Electrical Engineering, 94, 107365.

Banerjee, U., Chakrabortty, J., Rahaman, S. U., & Ramkumar, K. (2024). One-loop effective action up to dimension eight: Integrating out heavy scalar(s). The European Physical Journal Plus, 139(2), 1–29.

Chakrabortty, J., Rahaman, S. U., & Ramkumar, K. (2024). One-loop effective action up to dimension eight: Integrating out heavy fermion(s). Nuclear Physics B, 1000, 116488.

Ramkumar, K., Medeiros, E. P., Dong, A., de Albuquerque, V. H. C., Hassan, M. R., & Hassan, M. M. (2024). A novel deep learning framework based Swin transformer for dermal cancer cell classification. Engineering Applications of Artificial Intelligence, 133, 108097.

Banerjee, U., Chakrabortty, J., Rahaman, S. U., & Ramkumar, K. (2024). One-loop effective action up to any mass-dimension for non-degenerate scalars and fermions including light–heavy mixing. The European Physical Journal Plus, 139(2), 169.

 

Shaowei Wang | Computer Science | Distinguished Scientist Award

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Mr. Shaowei Wang | Computer Science | Distinguished Scientist Award

Guangzhou University | China

Dr. Shaowei Wang is currently an Associate Professor at the School of Artificial Intelligence, Guangzhou University. He earned his Ph.D. from the University of Science and Technology of China (USTC) in 2019. Prior to his academic appointment, he worked as an Applied Researcher at Tencent Technology (Shenzhen), where he contributed to industry-grade privacy-preserving solutions. His research primarily focuses on privacy-preserving computing, federated learning, and AI security, with a strong emphasis on differential privacy techniques and secure data sharing protocols. Dr. Wang has an impressive scholarly record, having authored or co-authored over 40 peer-reviewed papers, with 15 publications as the first or corresponding author in top-tier venues such as USENIX Security, IEEE S&P, INFOCOM, and ICDE. As of September 2025, his work has garnered 1,106 citations, achieving an h-index of 16 (Google Scholar indexed), reflecting the impact and relevance of his contributions to the field. He has been the Principal Investigator (PI) for six research projects, including three funded by the National Natural Science Foundation of China, and a key researcher in five national-level and regional-level projects. Notable ongoing research includes work on shuffled differential privacy, privacy attacks on pre-trained models, and secure digital identity protocols. His academic excellence and innovation have earned him multiple accolades, including an Honorable Mention Award at USENIX Security, a Top 3% Paper Award at ICASSP, and the First Prize in Natural Sciences from the Guangdong Artificial Intelligence Association. Dr. Wang remains committed to advancing the frontiers of privacy-preserving AI through impactful research, interdisciplinary collaboration, and high-quality publications in the global research community.

Profile: Scopus | Google Scholar

Featured Publications

Wang, S., Huang, L., Nie, Y., Zhang, X., Wang, P., Xu, H., & Yang, W. (2019). Local differential private data aggregation for discrete distribution estimation. IEEE Transactions on Parallel and Distributed Systems, 30(9), 2046–2059.

Xin, B., Yang, W., Geng, Y., Chen, S., Wang, S., & Huang, L. (2020). Private FL-GAN: Differential privacy synthetic data generation based on federated learning. In ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. [pages not provided]). IEEE.

Shen, Y., Huang, L., Li, L., Lu, X., Wang, S., & Yang, W. (2015). Towards preserving worker location privacy in spatial crowdsourcing. In 2015 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6). IEEE.

Nie, Y., Yang, W., Huang, L., Xie, X., Zhao, Z., & Wang, S. (2018). A utility-optimized framework for personalized private histogram estimation. IEEE Transactions on Knowledge and Data Engineering, 31(4), 655–669.

Wang, S., Huang, L., Nie, Y., Wang, P., Xu, H., & Yang, W. (2018). PrivSet: Set-valued data analyses with local differential privacy. In IEEE INFOCOM 2018 – IEEE Conference on Computer Communications (pp. 1088–1096). IEEE.

Yang, G., Wang, S., & Wang, H. (2021). Federated learning with personalized local differential privacy. In 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) (pp. [pages not provided]). IEEE.

Xin, B., Geng, Y., Hu, T., Chen, S., Yang, W., Wang, S., & Huang, L. (2022). Federated synthetic data generation with differential privacy. Neurocomputing, 468, 1–10.