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Anitha Patil
ResearcherPublications, citations & collaboration network
Anitha Patil is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 28 works, 45 citations, an h-index of 4 and an i10-index of 1.
28
Works
45
Citations
4
h-index
1
i10-index
IDs:OpenAlex
How has Anitha Patil's publication output changed over time?
ScholarIQpublication output · 2018–2026
Output declined50% over the shown period — from 2 works in 2018 to 1 in 2026.
2
3
5
1
2018202320252026
What are the most-cited papers on Anitha Patil?
ScholarIQmost cited works
CXR-MultiTaskNet a unified deep learning framework for joint disease localization and classification in chest radiographs
K. Hemant Kumar Reddy, Anitha Patil
Scientific Reports. 202510 CitationsOPEN ACCESS
Enhanced Deep Learning Models for Efficient Stroke Detection Using MRI Brain Imagery
Anitha Patil, Suresh Kumar Govindaraj
International Journal on Recent and Innovation Trends in Computing and Communication. 20239 CitationsOPEN ACCESS
Efficient Human Activity Classification Via 1D Capsule Networks and Chaotic Optimization
Rajanikanth Aluvalu, K. R. Venugopala Reddy, Anitha Patil, V. Uma Maheswari, Gangula Vijay Kumar, K. S. Reddy
20256 Citations
Prototype Architecture to Control Building Energy Usage Using PLC and CPS
Geetanjali Kale, Anitha Patil
2018 International Conference on Current Trends towards Converging Technologies (ICCTCT). 20185 Citations
An AI Enabled Framework for MRI-based Data Analytics for Efficient Brain Stroke Detection
Anitha Patil, S Govindaraj
20233 Citations
Related on ScholarIQ
Koneru Lakshmaiah Education Foundation
Institution
CXR-MultiTaskNet a unified deep learning framework for joint disease localization and classification in chest radiographs
Paper
Enhanced Deep Learning Models for Efficient Stroke Detection Using MRI Brain Imagery
Paper
Efficient Human Activity Classification Via 1D Capsule Networks and Chaotic Optimization
Paper
Prototype Architecture to Control Building Energy Usage Using PLC and CPS
Paper
An AI Enabled Framework for MRI-based Data Analytics for Efficient Brain Stroke Detection
Paper