Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
portfolio
Human-Following Bot for the Indian Army
GAN-powered autonomous robot that follows soldiers through rain and low-visibility night conditions. Selected and funded for the Indian Army (Ladakh).
Bluetooth Low Energy Security Audit Framework
A framework for auditing the security of BLE communication in IoT devices via BT-HCI packet inspection and automated analysis. Developed at C3iHub, IIT Kanpur.
FinBERT Sentiment + Financial-Stress Portfolio Optimization
A quantitative pipeline fusing transformer-based market sentiment with financial-stress indicators and volatility metrics for better risk-adjusted returns.
Agentic AI Systems on AWS
Designing and deploying agentic AI systems for autonomous task execution, with training and deployment pipelines on AWS SageMaker and EC2.
Diffusion Models & Project INDUS (LLM)
Research on diffusion-based unpaired image-to-image translation and explainable AI, plus data collection and translation for the INDUS LLM project.
publications
A Comprehensive Bluetooth Security Audit Framework for IoT Devices
Published in Computers & Security (Elsevier) — Q1, SCI [Under Review], 2025
A security-auditing framework for Bluetooth Low Energy in IoT devices, built on BT-HCI packet inspection and automated communication analysis. (Under review, Q1 SCI.)
Recommended citation: K. Leelasankar, N. Aslam, A. Vemuri, J. Gadde, U. S. Lavu, N. K. Shah, and S. Venkatesan. (2025). "A Comprehensive Bluetooth Security Audit Framework for IoT Devices." Submitted to Computers & Security (Elsevier), Q1, SCI. [Under Review].
An Efficient ADAS Framework for Cross-Domain Image Translation and Feature Preservation Utilizing CycleGAN
Published in CONFLUENCE 2025, Noida — Springer LNEE, 2025
A CycleGAN-based ADAS framework for cross-domain image translation that preserves task-relevant features for driver-assistance perception.
Recommended citation: A. Bidhan, N. K. Shah, R. Kumar, V. Gupta, and S. Prakash. (2025). "An Efficient ADAS Framework for Cross-Domain Image Translation and Feature Preservation Utilizing CycleGAN." 15th Int. Conf. on Cloud Computing, Data Science & Engineering (CONFLUENCE 2025), Noida, India. Springer Lecture Notes in Electrical Engineering.
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Integrating FinBERT Sentiment with Financial Stress for Optimized Portfolio Performance
Published in Academy of Marketing Studies Journal, 2025
A portfolio-optimization approach that fuses FinBERT-derived market sentiment with financial-stress indicators to improve risk-adjusted returns.
Recommended citation: S. Pandey, R. Singh, N. K. Shah, and A. Bidhan. (2025). "Integrating FinBERT Sentiment with Financial Stress for Optimized Portfolio Performance." Academy of Marketing Studies Journal.
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A Novel Adaptation of the UNIT Framework for Cross-Domain Climate Data Translation with Enhanced Feature Preservation
Published in 12th Int. Conf. on Signal Processing and Integrated Networks (SPIN 2025) — Springer, 2025
An adaptation of the UNIT (unsupervised image-to-image translation) framework for cross-domain climate data, with enhanced feature preservation.
Recommended citation: N. K. Shah et al. (2025). "A Novel Adaptation of the UNIT Framework for Cross-Domain Climate Data Translation with Enhanced Feature Preservation." 12th Int. Conf. on Signal Processing and Integrated Networks (SPIN 2025). Springer.
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Kathmandu Road Dataset: Ratnapark to Tripureshwor
Published in Harvard Dataverse, 2025
An open urban road-scene dataset covering the Ratnapark–Tripureshwor corridor in Kathmandu, published on Harvard Dataverse.
Recommended citation: N. K. Shah, J. Gadde, A. Bidhan, K. Gullapalli, C. P. Maurya, and N. Singh. (2025). "Kathmandu Road Dataset: Ratnapark to Tripureshwor." Harvard Dataverse. https://doi.org/10.7910/DVN/38E8AG
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RainGAN-Kathmandu: A Generative Adversarial Framework for Synthetic Rainfall Augmentation in Urban Road Scene Datasets
Published in IEEE TENCON 2025, Malaysia, 2025
A GAN framework that synthesizes realistic rainfall over urban road scenes to augment datasets for robust perception in adverse weather.
Recommended citation: N. K. Shah, J. Gadde, N. Singh, C. P. Maurya, and S. K. Singh. (2025). "RainGAN-Kathmandu: A Generative Adversarial Framework for Synthetic Rainfall Augmentation in Urban Road Scene Datasets." IEEE TENCON 2025, Malaysia.