Kai Jin | Computer Science | Best Researcher Award

Posted on by Phenomenological Research Awards

Dr. Kai Jin | Computer Science | Best Researcher Award

Lecturer at Sanya Research Institute of Hunan University of Science and Technology | China

Dr. Kai Jin is an accomplished researcher and academic whose work bridges the fields of computer science, artificial intelligence, and information engineering. With a strong educational foundation culminating in a Ph.D. in Computer Science and Technology from Hunan University, he has built a research career characterized by innovation, interdisciplinary collaboration, and practical impact. His professional experience spans both academia and industry, having served as a lecturer and researcher at the Sanya Research Institute of Hunan University of Science and Technology, as well as a software engineer in technology firms where he developed expertise in system architecture and Java-based solutions. Dr. Kai Jin’s scholarly contributions focus on network measurement, image recognition, and deep learning areas that are pivotal to advancing intelligent computing and data-driven technologies. He has authored six scientific papers published in high-impact journals and international conferences, including IEEE Transactions on Network Science and Engineering, Connection Science, and Scientific Reports. His work has earned 70 citations by 60 documents, with an h-index of 5, reflecting the growing influence of his research within the global academic community. In addition to publications, Dr. Kai Jin has secured four invention patents covering innovations in network traffic measurement, remote sensing image detection, brain tumor identification, and predictive maintenance for industrial IoT systems. His research projects, supported by national and provincial grants, such as the National Natural Science Foundation of China and the Hunan Provincial Key R&D Program, demonstrate a commitment to technological progress and societal benefit. Beyond his technical achievements, Dr. Kai Jin’s leadership in research collaborations and mentorship reflects his dedication to fostering the next generation of computer scientists. His current research continues to explore the integration of deep learning models with real-world systems, optimizing intelligent network management, and enhancing computational efficiency. Through his scientific rigor, creativity, and contributions to both theoretical and applied computing, Dr. Kai Jin has established himself as a leading voice in modern computer science, shaping innovations that address the complex challenges of today’s interconnected digital world.

Profile: Scopus

Featured Publications:

1. Jin, K., Xie, K., Wang, X., Tian, J., Xie, G., & Wen, J. (2022). Low-cost online network traffic measurement with subspace-based matrix completion. IEEE Transactions on Network Science and Engineering, 10(1), 53–67.

2. Jin, K., Xie, K., Tian, J., Liang, W., & Wen, J. (2023). Low-cost network traffic measurement and fast recovery via redundant row subspace-based matrix completion. Connection Science, 35(1), 2218069.

3. Jin, K., Banizaman, H., Gharehveran, S. S., & Jokar, M. R. (2025). Robust power management capabilities of integrated energy systems in the smart distribution network including linear and non-linear loads. Scientific Reports, 15(1), 6615.

4. Zhu, M., Rasheed, R. H., Albahadly, E. J. K., Zhang, J., Alqahtani, F., Tolba, A., & Jin, K.* (2025). Application of fixed and mobile battery energy storage flexibilities in robust operation of two-way active distribution network. Electric Power Systems Research, 244, 111556.

5. Wen, J., Chen, Y., & Jin, K.* (2023, June). Revolutionizing network performance: The active and passive service path performance monitoring analysis method. In 2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud) / 2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom) (pp. 1–6). IEEE.

6. Huo, Y., Jin, K., Cai, J., Xiong, H., & Pang, J. (2023). Vision Transformer (ViT)-based applications in image classification. In Proceedings of the 9th IEEE International Conference on High Performance and Smart Computing (HPSC 2023) (pp. 135–140). IEEE.

7. Jin, K., Xie, K., Tian, J., Liang, W., & Wen, J. (2024). A acylthiourea based ion-imprinted membrane for selective removal of Ag⁺ from aqueous solution. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 2024, Article 9 citations.

laiba Sultan Dar | Time series analysis | Best Researcher Award

Posted on by Phenomenological Research Awards

Ms. laiba Sultan Dar | Time series analysis | Best Researcher Award 

Phd Student at Abdul wali khan university | Pakistan

Ms. Laiba Sultan Dar is a dedicated researcher and Ph.D. scholar at Abdul Wali Khan University, specializing in time series analysis and decomposition techniques. Her academic journey is marked by a strong foundation in applied mathematics and data analytics, enabling her to contribute effectively to statistical modeling, forecasting, and computational research. She has developed expertise in identifying patterns and trends within large datasets, applying modern analytical frameworks to address complex problems in areas such as economics, climate studies, and system optimization. Her research integrates both theoretical and empirical approaches, focusing on improving the precision and interpretability of time-dependent models. Through her doctoral studies, she has actively engaged in institutional research projects and contributed to scholarly publications in recognized international journals, including those indexed by Scopus and ScienceDirect. Her work demonstrates a balance between mathematical rigor and practical application, particularly in designing adaptive models for real-world data forecasting. In addition to her academic pursuits, Ms. Laiba Sultan Dar has shown growing involvement in research collaborations and interdisciplinary studies that bridge statistical science and technological innovation. Her ability to combine methodological sophistication with computational efficiency has made her research relevant to emerging domains such as artificial intelligence-driven data analysis and predictive modeling. She continuously enhances her research capabilities by incorporating advanced tools and techniques, fostering a deeper understanding of time series dynamics. With a citation index reflecting the recognition of her early scholarly efforts, she remains committed to producing impactful research that advances statistical methodologies and contributes to data-driven decision-making. Her intellectual curiosity and analytical acumen position her as a promising researcher in quantitative sciences. Ms. Laiba Sultan Dar’s long-term goal is to strengthen the integration of mathematical modeling with applied sciences, promoting innovation and scientific development in her field. Her commitment to excellence, collaborative mindset, and ongoing pursuit of research excellence make her a deserving candidate for recognition among emerging scholars in data analytics and statistical research.

Profile: Google Scholar

Featured Publications:

  • Dar, L., Akmal, A., Naseem, M. A., & Khan, K. U. D. (2011). Impact of stress on employees’ job performance in the business sector of Pakistan. Global Journal of Management and Business Research, 11(6), 1–4.

  • Dar, L. A., Naseem, M. A., Rehman, R. U., & Niazi, G. S. (2011). Corporate governance and firm performance: A case study of Pakistan oil and gas companies listed in Karachi Stock Exchange. Global Journal of Management and Business Research, 11(8), 1–10.

  • Malik, M., Wan, D., Dar, L., Akbar, A., & Naseem, M. A. (2014). The role of work-life balance in job satisfaction and job benefit. Journal of Applied Business Research (JABR), 30(5), 1627–1638.

  • Dar, L. S., Aamir, M., Khan, Z., Bilal, M., Boonsatit, N., & Jirawattanapanit, A. (2022). Forecasting crude oil price volatility by reconstructing EEMD components using ARIMA and FFNN models. Frontiers in Energy Research, 10, 991602.

  • Dar, L. S., Aamir, M., Bibi, S., & Bilal, M. (2025). A novel robust adaptive decomposition approach for solar energy potential using atmospheric transparency and UV radiation indicators. Journal of Radiation Research and Applied Sciences, 18(4), 101946.

  • Dar, L. S., Aamir, M., Hamraz, M., Faiz, N., Emam, W., & Tashkandy, Y. (2025). A robust adaptive signal decomposition method for enhanced mode extraction in financial time series. IEEE Access.

  • Saleem, Q., Dar, L., Shahid, M., & Rana, S. (2012). A quantitative analysis of the role of human resource development in economic growth in Pakistan. International Journal of Management Sciences and Business Research.

  • Bilal, M., Aamir, M., Abdullah, S., Mahmood, N., Khalil, U., Khalid, N., Ahmed, M., & Dar, L. (2022). Assessment of the COVID-19 pandemic’s impact on gasoline prices in Pakistan. VFAST Transactions on Mathematics, 10(2), 52–67.