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.

Ms. Paluck Arora | Deep learning | Best Researcher Award

Research Scholar, Thapar university, India

Paluck Arora is a dedicated research scholar at the Thapar Institute of Engineering & Technology, specializing in Computer Science Engineering. With a passion for innovation and academic excellence, she has contributed to various research projects in medical image registration and optimization techniques. Her academic journey is marked by a Ph.D. with a 9.33 CGPA, several published papers, and collaborations with leading researchers in the field.

Profile

Scopus

Strength of the Award

Paluck Arora demonstrates exceptional research capabilities, particularly in the field of medical image registration. Her work integrates advanced techniques like deep learning, meta-heuristic approaches, and projective transformation, which are at the forefront of medical imaging innovations. With several SCIE-indexed journal publications and a consistently high impact factor in her research, Paluck showcases a strong academic foundation, particularly in the rapidly evolving field of medical imaging and computer science. Her contributions to image-guided interventions are instrumental in enhancing diagnostic accuracy and treatment planning, making her a strong contender for the Best Research Award.

Area of Improvement

While Paluck’s research output is impressive, expanding her research portfolio into real-world applications and interdisciplinary collaborations with the healthcare industry could further enhance the practical impact of her work. More patents or tangible innovations derived from her research could strengthen her case for the award by demonstrating the practical and transformative benefits of her findings in clinical settings.

🎓 Education

Ph.D. in Computer Science Engineering (2020-2024) – Thapar Institute of Engineering & Technology, Patiala, Punjab (CGPA: 9.33)
Master of Engineering in Computer Science Engineering (2016-2018) – Thapar Institute of Engineering & Technology, Patiala, Punjab (CGPA: 8.38)
Bachelor of Engineering in Computer Science Engineering (2011-2015) – Kurukshetra University, Haryana (GPA: 8.1)

💼 Experience

Paluck Arora has held positions as an Assistant Professor at MMDU University, Mullana, and Thapar Institute of Engineering & Technology, Patiala. With over five years of teaching experience, she has contributed to shaping future engineers while actively engaging in research, resulting in published work in SCIE-indexed journals.

🔍 Research Interest

Her primary research areas include medical image registration, image processing, and deep learning. She focuses on advancing algorithmic approaches for accurate and efficient medical image alignment, leveraging deep learning techniques to enhance diagnostic accuracy and treatment planning in healthcare.

🏆 Awards

Paluck Arora has achieved recognition for her contributions in the fields of image registration and computer science. Her continuous dedication to research innovation has led to multiple recognitions and accolades within the academic community.

📚 Publications Top Notes

P. Arora, R. Mehta, and R. Ahuja (2023). “An adaptive medical image registration using hybridization of teaching-learning-based optimization with affine and speeded-up robust features with projective transformation.” Cluster Computing, Springer Nature, pp: 1-21. SCIE Indexed, Impact Factor: 3.6
Cited by: 15

P. Arora, R. Mehta, and R. Ahuja (2024). “An integration of meta-heuristic approach utilizing kernel principal component analysis for multimodal medical image registration.” Cluster Computing, Springer Nature, pp: 1-21. SCIE Indexed, Impact Factor: 3.6
Cited by: 10

P. Arora, R. Mehta, and R. Ahuja (2024). “Deep-UEO: Empowering Medical Image Registration with Hybrid Strategy based on Deep Learning and United Equilibrium Optimizer.” Computers and Electrical Engineering, Elsevier SCIE Indexed, Impact Factor: 4.0
Cited by: 8

P. Arora, R. Mehta, and R. Ahuja (2024). “A Teaching-Learning based Optimization driven Approach for Robust Deformable Medical Image Registration leveraging Unsupervised Learning.” Concurrency and Computation: Practice and Experience, Wiley [SCIE Indexed, Impact Factor: 2.0] (Under Review)
Cited by: Pending

P. Arora, R. Mehta, and R. Ahuja (2024). “Deep VGG19-SURF Feature Extraction with Projective Transformation for Anatomical and Functional Medical Image Registration.” Soft Computing, Springer [SCIE Indexed, Impact Factor: 3.1] (Under Review)
Cited by: Pending

Conclusion

Paluck Arora is a strong candidate for the Best Research Award due to her significant contributions to the field of medical image registration and her application of advanced algorithms and deep learning techniques. With room for further development in industry collaborations and real-world application, she demonstrates great potential to continue making meaningful strides in her field

Paluck Arora | Deep learning | Best Researcher Award