Cheng Ju | 3D object detection | Research Excellence Award

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Dr. Cheng Ju | 3D object detection | Research Excellence Award

Xi'an Innovation College of Yan'an University | China 

The research profile centers on advancing computer vision through robust, trustworthy, and high-efficiency intelligent systems, addressing both task-specific challenges and foundational methodological limitations. Core contributions focus on object detection, anomaly segmentation, feature representation learning, and adaptive model design, with strong emphasis on real-world deployment reliability. In oriented small object detection, the proposed FDA-DETR framework overcomes weak feature representation and excessive computational cost in transformer-based detectors by integrating multi-scale frequency domain enhancement, density-aware dynamic query generation, and multi-granularity attention fusion, achieving significant gains in accuracy, robustness, and efficiency for dense and complex visual scenes. In zero-shot anomaly segmentation, the explainable recursive ERSF-AS framework resolves prompt dependency, limited anomaly feature learning, and interpretability challenges through a collaborative CLIP-SAM architecture combined with semantic, spatial, and frequency priors. Its recursive reasoning paradigm strengthens cross-domain generalization and reliability in both industrial and medical environments. Collectively, these innovations contribute not only to practical solutions for complex vision tasks but also to broader theoretical advancements in feature learning, attention modeling, adaptive mechanisms, and explainable artificial intelligence. This integrated research direction establishes a scalable foundation for future exploration across diverse computer vision and intelligent perception domains, promoting trustworthy, interpretable, and deployable AI systems for real-world applications.

Profile: Orcid 

Featured Publications 

Ju, C., Xie, Y., Wang, Z., Zhao, Y., Yan, W., Chai, R., Duan, J., Cao, Y., & Chang, Y. (2026). STGFormer: A pyramidal spatio-temporal graph transformer with cross-disciplinary feature fusion for semantic-rich trajectory prediction in heterogeneous autonomy traffic. Expert Systems with Applications.

Ju, C., Xie, Y., Duan, J., Chang, Y., & Wu, M. (2026). KeyGeoFusion: A multi-modal keypoint and geometry-aware framework for small and distant 3D object detection in sparse point clouds. Neurocomputing.

Kuruba Chandrakala | Machine Learning and Statistics | Best Researcher Award

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Dr. Kuruba Chandrakala | Machine Learning and Statistics | Best Researcher Award

Siddhartha Academy of Higher Education | India

Dr. Kuruba Chandrakala is an emerging researcher in the domains of computer vision, deep learning, and medical image processing, currently serving as Assistant Professor (Selection Grade) in the CSE department at Siddhartha Academy of Higher Education, Vijayawada. She earned her Ph.D. from NIT Tiruchirappalli, preceded by M.Tech in Computer Science and Engineering with distinction from JNTU Kakinada and B.Tech in the same discipline from JNTU Anantapur. She has qualified both NET and APSET examinations. Her professional trajectory includes roles as Head of Department (CSE-AIML) at Vignan’s Nirula Institute of Technology & Science for Women and previous teaching appointments at VNITSW and SITAM, along with industry experience as a System Engineer with Tata Consultancy Services. Her publication record comprises five Scopus indexed papers, four of which are in SCIE journals, two IEEE conference papers, and one book chapter; she also holds one patent. Her Scopus metrics include an h-index of 4, 10 documents, and 150 citations. Her research has addressed areas such as diabetic retinopathy segmentation, robust blood vessel detection, and image enhancement through deep learning architectures. She teaches courses including Deep Learning, Machine Learning, Big Data Analytics, Cloud Computing, and programming in C, C++, Java, and Python. She has earned numerous certifications from NPTEL, Coursera, Microsoft, IBM, and Wipro and received awards such as the NPTEL Discipline Star and Wipro Project Excellence Award. Her leadership and mentoring roles include serving as a mentor for Wipro TalentNext, nodal officer for Microsoft Upskilling and APSCHE virtual internship programs, and coordinator for various hackathons. She is a life member of professional bodies such as CSI, ISTE, IAENG, and IET, and has delivered several invited and guest lectures, contributing significantly to academic excellence and research advancement.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Chandrakala, K., & Gopalan, N. P. (2025). 3DECNN: A novel method for segmentation of diabetic retinopathy in retinal fundus images using 3D-edge CNN. Neural Computing and Applications.

Kuruba, C., Sharmila, S. K., Mounika, V., Aswini, D., & Poojitha, G. (2023). Three layered security model to prevent credit card fraud using LBPH and CNN-ResNet architecture. International Conference on Hybrid Intelligent Systems, 422–428.

Dharmaiah, K., Mebarek-Oudina, F., Sreenivasa Kumar, M., & Chandra Kala. (2023). Nuclear reactor application on Jeffrey fluid flow with Falkner-Skan factor, Brownian and thermophoresis, non-linear thermal radiation impacts past a wedge. Journal of the Indian Chemical Society, 100(2), 117.

Kuruba, C., & Gopalan, N. P. (2023). Robust blood vessel detection with image enhancement using relative intensity order transformation and deep learning. Biomedical Signal Processing and Control, 86, 105195.

Kuruba, C., Pushpalatha, N., Ramu, G., Suneetha, I., Kumar, M. R., & Harish, P. (2023). Data mining and deep learning-based hybrid health care application. Applied Nanoscience, 13(3), 2431–2437.