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Academic Experts
Dr. RUCHIKA BALA

Biography

Dr. Ruchika Bala earned her B.E. degree in Computer Science from Rajiv Gandhi Proudyogiki Vishwavidyalaya and completed her M.E. from Delhi Technological University (formerly Delhi College of Engineering). With over 6.5 years of teaching experience and 14 years of expertise in patent research, she has cultivated a strong academic and professional foundation. She obtained her Ph.D. in Information Technology from Indira Gandhi Delhi Technical University for Women (IGDTUW) in 2024, focusing on her research interests. Her areas of specialization include Medical Image Processing, Machine Learning, Deep Learning, Computer Vision, Data Structures, Algorithms, and Automata Theory. Currently, she is working as an Asst Prof. (Sr Grade) in the Department of CSE/IT at JIIT, Noida

Research Highlights

During my PHD research work, I pioneered novel deep learning architectures for diabetic retinopathy classification, including CTNet using CNN and ViT. Published over 10 peer-reviewed research articles in high-impact journals such as IET Image Processing, NCAA, Springer, and ARCO, Springer, IEEE conferences, etc. Developed a dual-branch CNN framework for effective copy-move forgery detection, contributing to advancements in digital image forensics with 50+ citations. Actively involved in interdisciplinary research, integrating machine learning with medical imaging, focusing on early detection of retinal diseases and forgery detection, etc. Contributed to international conferences, including AIST (Artificial Intelligence and Speech Technology), presenting state-of-the-art approaches in vision-based healthcare applications, reviewing research papers, coordinated sessions, etc. Also, I actively participate as a reviewer for SCI journals in the domains of AI, image processing, and medical diagnostics.

Areas of Interest
  • Deep Learning/Machine Learning
  • Healthcare, Computer Vision
  • Data structures, Algorithms
Publications

1) N. Goel, S. Kaur, and R. Bala, "Dual branch convolutional neural network for copy move
forgery detection," IET Image Processing, vol. 15, no. 3, pp. 656–665, 2021,
https://doi.org/10.1049/ipr2.12051.
2) R. Bala, A. Sharma, and N. Goel, “Comparative analysis of diabetic retinopathy
classification approaches using machine learning and deep learning techniques,” Arch
Computat Methods Eng, 2023 https://doi.org/10.1007/s11831-023-10002-5
3) R. Bala, A. Sharma, and N. Goel, “CTNet: convolutional transformer network for
diabetic retinopathy classification,” Neural Comput & Applic, 2023
https://doi.org/10.1007/s00521-023-09304-3 
4) Bala R., Sharma A., Goel N. (2022) A Lightweight Deep Learning Approach for Diabetic
Retinopathy Classification. In: Dev A., Agrawal S.S., Sharma A. (eds) Artificial
Intelligence and Speech Technology. AIST 2021. Communications in Computer and
Information Science, vol 1546. Springer, Cham. https://doi.org/10.1007/978-3-030-
95711-7_25
5) Bala R., Sharma A., and Goel N., “A novel convolutional neural network architecture for
diabetic retinopathy screening,” in 2022 4th International Conference on Artificial
Intelligence and Speech Technology (AIST), 2022, pp. 1–6.