Prof. Dr. Chengli Sun | Artificial Intelligence | Best Researcher Award

Published on by Superior Awards

Prof. Dr. Chengli Sun | Artificial Intelligence | Best Researcher Award

Prof. Dr. Chengli Sun | Guangzhou Maritime University | China

Prof. Dr. Chengli Sun is a distinguished scholar in the field of signal and information processing, widely recognized for his significant contributions to intelligent acoustic technology and next-generation speech systems.To date, his work has accumulated 336 citations across 303 documents, demonstrating the broad recognition and adoption of his research outcomes by both domestic and international peers. With 46 published documents spanning high-impact journals His research encompasses speech recognition, speech enhancement, acoustic scene analysis, and computer vision, with a strong focus on advancing human–machine voice interaction under complex and noisy environments. He has led a series of high-impact scientific projects, including major National Natural Science Foundation of China grants and key provincial and municipal initiatives, driving breakthroughs in generative adversarial network models, dual-diffusion speech enhancement, sustainable learning-oriented vehicle voice interaction, and speaker-specific keyword spotting. These research outcomes have enabled practical advancements in intelligent transportation, robotics, smart devices, and public safety. Alongside his research achievements, Prof. Dr. Chengli Sun plays an integral role in academic development and scientific service by contributing to expert review committees and supporting the progress of information processing and acoustics disciplines. He remains committed to high-level talent cultivation through leadership in first-class undergraduate teaching programs and the promotion of interdisciplinary innovation in artificial intelligence and signal processing. Collectively, his sustained research efforts, academic influence, and dedication to education position him as a leading figure shaping the future of intelligent voice technologies.

Profiles: Scopus | Orcid

Featured Publications

  • Sun, M., Sun, C., Zou, C., Zhang, J., & Xiang, D. (2025). Modeling of multi-electrode epicardial electrograms for conductivity estimation in atrial fibrillation. IEEE Access.

  • Li, J., Xiang, D., Li, C., Mao, S., Chen, Y., Sun, M., He, W., Deng, Y., & Sun, C. (2025, December 3). Learning student knowledge states from multi-view question–skill networks. Symmetry.

  • Leng, Y., Zhang, E., Zhuang, J., Shen, C., Sun, C., Yuan, Q., & Pan, J. (2025, October). A topic-specific representation learning framework for acoustic scene classification. Applied Soft Computing.

  • Rao, Z., Sun, C., Sun, J., Chen, F., Leng, Y., Sun, M., & Guo, Q. (2025, October 16). A new speech enhancement model based on residual denoising diffusion. Circuits, Systems, and Signal Processing.

  • Wan, M., Zhu, J., Sun, C., Yang, Z., Yin, J., & Yang, G. (2024). Tensor low-rank graph embedding and learning for one-step incomplete multi-view clustering. IEEE Transactions on Multimedia.

 

 

Prof. Ebenezer Esenogho | Artificial Intelligence | Best Researcher Award

Published on by Superior Awards

Prof. Ebenezer Esenogho | Artificial Intelligence | Best Researcher Award

Prof. Ebenezer Esenogho | University of South Africa | South Africa

Prof. Ebenezer Esenogho (NRF C2-rated) is a distinguished academic and research leader recognized for his extensive contributions to Artificial Intelligence, telecommunications engineering, and digital innovation. With a strong background in computer, electronics, and communication engineering, he has built an influential career marked by advanced research, academic leadership, and international collaboration. He has produced impactful work across areas such as AI-driven wireless systems, 5G networks, cognitive radio, cybersecurity, IoT, smart grid technologies, software-defined systems, big data, and cloud computing. His scholarly influence is reflected in 920 citations from 762 documents, 40 published documents, and an h-index of 12. Prof. Esenogho has secured numerous competitive grants, awards, and research fellowships in recognition of his excellence, and he has contributed to global research advancement through participation in strategic committees, collaborative initiatives, and high-level innovation programs. He has supervised a wide portfolio of postgraduate researchers, contributed to editorial boards, reviewed international grant proposals, and chaired sessions at reputable conferences. Beyond research, he is committed to academic mentorship, capacity building, and advancing technology-driven development through community-focused initiatives. Prof. Ebenezer continues to play a leading role in shaping the future of intelligent systems, next-generation networks, and multidisciplinary innovation across regional and global research landscapes.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

  • Esenogho, E., Mienye, I. D., Swart, T. G., Aruleba, K., & Obaido, G. (2022). A neural network ensemble with feature engineering for improved credit card fraud detection. IEEE Access, 10, 16400–16407.

  • Esenogho, E., Djouani, K., & Kurien, A. M. (2022). Integrating artificial intelligence, Internet of Things, and 5G for next-generation smart grid: A survey of trends, challenges, and prospects. IEEE Access, 10, 4794–4831.

  • Sarah, I. D. M., Ebiaredoh-Mienye, A., Esenogho, E., & Swart, T. G. (2022). A machine learning method with filter-based feature selection for improved prediction of chronic kidney disease. Bioengineering, 9(8).

  • Nguyen, N., Duong, T., Chau, T., Nguyen, V. H., Trinh, T., Tran, D., Ho, T., … Esenogho, E. (2022). A proposed model for card fraud detection based on CatBoost and deep neural network. IEEE Access, 10, 96852–96861.

  • Arnaz, A., Lipman, J., Abolhasan, M., Hiltunen, M., … Esenogho, E. (2022). Toward integrating intelligence and programmability in open radio access networks: A comprehensive survey. IEEE Access, 10, 67747–67770.

  • Obaido, G., Ogbuokiri, B., Swart, T. G., Ayawei, N., Kasongo, S. M., Aruleba, K., … Esenogho, E. (2022). An interpretable machine learning approach for hepatitis B diagnosis. Applied Sciences, 12(21), 11127.