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Sathwik Narkedimilli

12 papers hereh-index 437 citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author5

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CR3
  • cs.LG3
  • astro-ph.IM2
  • astro-ph.GA1
  • astro-ph.SR1
  • cs.CY1
same name
  • Sathwik Narkedimilli — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedPhotometric Analysis for Predicting Star Formation Rates in Large Galaxies Using Machine Learning and Deep Learning Techniques

1 citations · 1 across the 12 of their papers we have counts for

collaborators
Showing 2024Show all

3 papers · 1 filter

cs.CR2024

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…

astro-ph.GA2024★ 1 cited

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…

astro-ph.SR2024

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.