Publications
You can also find my articles on my Google Scholar profile
Presented at Conference on July 6, 2025
GitHub
Presented on July 6, 2025. This paper presents important contributions toward state-of-the-art drone classification systems that are deployable in challenging environments. Building on the groundwork laid by existing drone categorization methods, we provide two major contributions: (i) a new processing pipeline that converts RF signals into optimized spectrogram images for computer vision applications, and (ii) an extensive analysis of nine state-of-the-art CNN models.
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Recommended citation: Kaushik A R, Annaamalai U, and Padmavathi S, "Enhanced Drone Classification using Transfer Learning and Optimized RF-Spectrogram," Presented at Conference, July 6, 2025.
2023 International Conference on Energy, Materials and Communication Engineering (ICEMCE), December 14-15, 2023
Google Scholar
Published on February 21, 2024. This study utilizes an Activity of Daily Living (ADL) dataset collected from 30 participants who performed six distinct activities while wearing smartphones equipped with sensors. Explainable AI (XAI) techniques like LIME and SHAP are used to understand the attributes significantly influencing model predictions.
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Recommended citation: A. R. Kaushik, K. S. Gurucharan and S. Padmavathi, "Enhancing Human Activity Recognition: An Exploration of Machine Learning Models and Explainable AI Approaches for Feature Contribution Analysis," 2023 International Conference on Energy, Materials and Communication Engineering (ICEMCE), Madurai, India, 2023, pp. 1-6, doi: 10.1109/ICEMCE57940.2023.10434184.
IEEE Conference, March 18-20, 2023
Google Scholar
Published on June 1, 2023. This work aims to produce reliable short-term forecasting using machine learning methods such as Linear Regression, Random Forest Regression, and Decision Tree Regression. Results show 99% accuracy for the proposed models.
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Recommended citation: A.R. Kaushik, S. Padmavathi, K.S. Gurucharan, and S.Charles Raja, "Performance Analysis of Regression Models in Solar PV Forecasting," IEEE Conference, March 18-20, 2023.
