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Neha Vinayak

Assistant Professor (Off Campus), Department of Computer Science & Information Systems, BITS Pilani

Deep Learning, Machine Learning, Medical Image Analysis, Prompt Engineering, Quantum Machine Learning, Sparse Parameter Artificial Neural Networks
neha_vinayak

Publications in Conferences and Journals

Conference Publications

Zaman Md., Zaman A., Vinayak N. (2026). Predicting Tabular Scaling Exponents via Dataset Geometry and Statistical Descriptors. In: IEEE International Conference on Sustainability, Innovation and Technology (ICSIT 2026) [Accepted]

Vinayak N., Vangimalla Reddy R., Narang A. (2026). Benchmarking Vulnerabilities of Large Language Models in the Medical Domain using Prompt Injection. Big Data Innovation for Sustainable Cognitive Computing (BDCC 2025) [Accepted: Best Paper Awarded]

Vinayak, N., Ahmad, S. (2026). LC-SS: A Learning Curve based - Sample Size Estimation framework. In: Congress on Intelligent Machines and Algorithms (CIMA 2025) [Accepted]

Vinayak, N., Ahmad, S. (2026). Performance and Robustness of Distribution Based Neural Networks (DBNN): A Comparative Study. In: Bansal, J.C., Jamwal, P., Hussain, S. (eds) Sustainable Computing and Intelligent Systems. SCIS 2025. Lecture Notes in Networks and Systems, vol 1926. Springer, Cham.

Vinayak, N., Ahmad, S. (2023). Sample Size Estimation for Effective Modelling of Classification Problems in Machine Learning. In: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (eds) Advanced Network Technologies and Intelligent Computing. ANTIC 2022. Communications in Computer and Information Science, vol 1798. Springer, Cham.

Vinayak, N., Ahmad, S. (2023). A Reduced-Memory Multi-layer Perceptron with Systematic Network Weights Generated and Trained Through Distribution Hyper-parameters. In: Sharma, H., Shrivastava, V., Bharti, K.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2022. Lecture Notes in Networks and Systems, vol 689. Springer, Singapore.

 

Journal Publications

Vinayak, N., Ahmad, S. QXRNet: a hybrid CNN–QNN model with resolution-conditioned feature extraction and variational quantum circuit. Quantum Mach. Intell. 8, 48 (2026).

Vinayak, N., Pandey, D., & Ahmad, S. (2024). Low dimension medical images and generative deep learning models can help to reduce x-ray radiation exposure of patients. International Journal of Computer (IJC)52(1), 44-58.