Hongtao Li | Cyberspace Security | Best Researcher Award

Published on by Database Scientist

Prof. Dr. Hongtao Li | Cyberspace Security | Best Researcher Award

Linyi University | China

Prof. Dr. Hongtao Li is a distinguished researcher specializing in cryptography and its applications, IoT security, large model security, and AI security. He serves as a council member of the Shanxi Computer Society and has been recognized under the “Sanjin Talent” Support Program in Shanxi Province. His research contributions encompass privacy-preserving mechanisms for big data, federated learning, blockchain-based auditing, and secure data collection for smart cities and IoT systems. He has led multiple high-profile projects, including a National Natural Science Foundation of China Youth Project on big data security and privacy protection, and several provincial-level initiatives focused on IoT security, medical big data protection, and cyberspace education reform. Prof. Li holds two authorized patents for computer network security and information security devices, reflecting his strong applied research impact. He has published over 50 papers in high-impact journals, addressing differential privacy, location privacy, blockchain protocols, and privacy-preserving schemes for digital communities and healthcare systems. His work demonstrates a consistent focus on safeguarding data and enhancing security frameworks in complex networked environments. With an h-index of 11, 328 citations by 314 documents, and 30 scholarly publications, Prof. Li’s research has significantly influenced both theoretical and applied aspects of cybersecurity, particularly in IoT and large-scale data environments, positioning him as a leading figure in advancing secure and privacy-preserving technologies in China and internationally.

Profile: Scopus

Featured publications

  • Wang, J., Zhang, Z. J., Tian, J., & Li, H. T. (2024). Local differential privacy federated learning based on heterogeneous data multi-privacy mechanism. Computer Networks, 254, 110822.

  • Li, H. T., Ma, J. F., & Fu, S. (2015). A privacy-preserving data collection model for digital community. Science China Information Sciences, 58(3), 1–16.

  • Li, H. T., Guo, F., & Wang, L., et al. (2021). A blockchain-based public auditing protocol with self-certified public keys for cloud data. Security and Communication Networks, 2021(3), 1–10.

  • Li, H. T., Wang, Y., & Guo, F., et al. (2021). Differential privacy location protection method based on the Markov model. Wireless Communications and Mobile Computing, 2021, 1–10.

  • Li, H. T., Xue, X., Li, Z., et al. (2021). Location privacy protection scheme for LBS in IoT. Wireless Communications and Mobile Computing, 2021, 1–18.

Sukumar Letchmunan | Computer Science | Best Researcher Award

Published on by Database Scientist

Dr. Sukumar Letchmunan | Computer Science | Best Researcher Award

University Sains Malaysia | Malaysia

Dr. Sukumar Letchmunan is an accomplished researcher and academic at Universiti Sains Malaysia, specializing in Software Engineering, Machine Learning, Software Metrics, and Service-Oriented Software Engineering. His research primarily focuses on developing intelligent computational models and data-driven frameworks that address complex real-world problems in domains such as crime prediction, healthcare analytics, uncertainty modeling, and sustainable digital systems. Over the years, Dr. Sukumar Letchmunan has made significant contributions to artificial intelligence applications through the integration of fuzzy logic, evidential theory, and deep learning, as reflected in his high-impact publications in leading journals including IEEE Access, ACM Transactions on Knowledge Discovery from Data, Knowledge-Based Systems, and Applied Soft Computing. His collaborations have produced novel methods for multi-view evidential clustering, belief function-based uncertainty representation, and medical decision-making systems, demonstrating his expertise in handling imprecision in data-centric environments. Notably, his works on AI-driven crime forecasting, machine learning-based diabetes prediction, and deep neural network analysis for human activity recognition have been widely cited, influencing interdisciplinary research across computer science and healthcare informatics. With a strong record of international collaboration, particularly with researchers from China, Turkey, and Saudi Arabia, Dr. Sukumar Letchmunan continues to advance the frontier of trustworthy and interpretable AI systems. His recent works in fuzzy similarity measures, transformer-based spatiotemporal modeling, and green decision-making frameworks exemplify his commitment to enhancing computational intelligence for sustainable and socially relevant applications. With 1,298 citations, an h-index of 19, and an i10-index of 22, Dr. Sukumar Letchmunan research impact underscores his influential role in advancing next-generation AI methodologies and their meaningful application to global scientific and societal challenges.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

1. Butt, U. M., Letchmunan, S., Ali, M., Hassan, F. H., Baqir, A., & Sherazi, H. H. R. (2021). Machine learning based diabetes classification and prediction for healthcare applications. Journal of Healthcare Engineering, 2021(1), 9930985.

2. Khaw, T. Y., Teoh, A. P., Abdul Khalid, S. N., & Letchmunan, S. (2022). The impact of digital leadership on sustainable performance: A systematic literature review. Journal of Management Development, 41(9–10), 514–534.

3. Butt, U. M., Letchmunan, S., Hassan, F. H., Ali, M., Baqir, A., & Sherazi, H. H. R. (2020). Spatio-temporal crime hotspot detection and prediction: A systematic literature review. IEEE Access, 8, 166553–166574.

4. Liu, Z., & Letchmunan, S. (2024). Enhanced fuzzy clustering for incomplete instance with evidence combination. ACM Transactions on Knowledge Discovery from Data, 18(3), 1–20.

5. Liu, Z., Huang, H., Letchmunan, S., & Deveci, M. (2024). Adaptive weighted multi-view evidential clustering with feature preference. Knowledge-Based Systems, 294, 111770.