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D Hemalatha
ResearcherPublications, citations & collaboration network
D Hemalatha is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 43 works, 262 citations, an h-index of 10 and an i10-index of 11.
43
Works
262
Citations
10
h-index
11
i10-index
IDs:OpenAlex
How has D Hemalatha's publication output changed over time?
ScholarIQpublication output · 2022–2025
Output grew0% over the shown period — from 1 works in 2022 to 1 in 2025.
1
2
6
1
2022202320242025
What are the most-cited papers on D Hemalatha?
ScholarIQmost cited works
A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis
Almas Begum, V. Dhilip Kumar, Junaid Asghar, D Hemalatha, G. Arulkumaran
S207319443. 202264 CitationsOPEN ACCESS
Deep Learning Techniques Advancements in Apple Leaf Disease Detection
E. Kannan, Carmel Mary Belinda M J, Alex David S, Ruth Naveena N, Almas Begum, D Hemalatha
Procedia Computer Science. 202422 CitationsOPEN ACCESS
Deep Learning Advances in Brain Tumor Classification: Leveraging VGG16 and MobileNetV2 for Accurate MRI Diagnostics
Alex David S, Almas Begum, S. Ismail Kalilulah, Ruth Naveena N, K. Rajathi, D Hemalatha
202422 Citations
Deep Learning-Based Lung Cancer Classification: Recent Developments and Future Prospects
Almas Begum, Alex David S, D Hemalatha, Lavanya Sita Sai Kollipara
202320 Citations
Predictive Management of EV Charging Stations Using Machine Learning
Alex David S, Almas Begum, D Hemalatha, K Senthil, R Monikaa, Ruth Naveena N
202418 Citations
Related on ScholarIQ
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
Institution
A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis
Paper
Deep Learning Techniques Advancements in Apple Leaf Disease Detection
Paper
Deep Learning Advances in Brain Tumor Classification: Leveraging VGG16 and MobileNetV2 for Accurate MRI Diagnostics
Paper
Deep Learning-Based Lung Cancer Classification: Recent Developments and Future Prospects
Paper
Predictive Management of EV Charging Stations Using Machine Learning
Paper