5 papers
Explainable Galaxy Interaction Prediction with Hybrid Attention Mechanisms
Sathwik Narkedimilli, Satvik Raghav, Om Mishra +5
Galaxy interaction classification remains challenging due to complex morphological patterns and the limited interpretability of deep learning models. We propose an attentive neural…
Comparative Analysis of Black Hole Mass Estimation in Type-2 AGNs: Classical vs. Quantum Machine Learning and Deep Learning Approaches
Sathwik Narkedimilli, Venkata Sriram Amballa, N V Saran Kumar +5
In the case of Type-2 AGNs, estimating the mass of the black hole is challenging. Understanding how galaxies form and evolve requires considerable insight into the mass of black ho…
FL-DABE-BC: A Privacy-Enhanced, Decentralized Authentication, and Secure Communication for Federated Learning Framework with Decentralized Attribute-Based Encryption and Blockchain for IoT Scenarios
Sathwik Narkedimilli, Amballa Venkata Sriram, Satvik Raghav
This study proposes an advanced Federated Learning (FL) framework designed to enhance data privacy and security in IoT environments by integrating Decentralized Attribute-Based Enc…
Photometric Analysis for Predicting Star Formation Rates in Large Galaxies Using Machine Learning and Deep Learning Techniques
Satvik Raghav, Prasanth Ayitapu, Sathwik Narkedimilli +2
Star formation rates (SFRs) are a crucial observational tracer of galaxy formation and evolution. Spectroscopy, which is expensive, is traditionally used to estimate SFRs. This stu…
Predicting Stellar Metallicity: A Comparative Analysis of Regression Models for Solar Twin Stars
Sathwik Narkedimilli, Satvik Raghav, Sujith Makam +2
The research focuses on determining the metallicity ([Fe/H]) predicted in the solar twin stars by using various regression modeling techniques which are, Random Forest, Linear Regr…