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Meenakshi Aggarwal
ResearcherPublications, citations & collaboration network
Meenakshi Aggarwal is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 15 works, 398 citations, an h-index of 9 and an i10-index of 9.
15
Works
398
Citations
9
h-index
9
i10-index
IDs:OpenAlex
How has Meenakshi Aggarwal'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
5
3
1
2022202320242025
What are the most-cited papers on Meenakshi Aggarwal?
ScholarIQmost cited works
Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification
Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal, Aman Singh, Amr Tolba, Ernesto Bautista Thompson, Sushil Kumar
Agriculture. 202383 CitationsOPEN ACCESS
Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images
Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal, Abdullah Alammari, Marwan Ali Albahar, Aman Singh
Sustainability. 202367 CitationsOPEN ACCESS
Resource-efficient federated learning over IoAT for rice leaf disease classification
Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal, Thomas André Prola
Computers and Electronics in Agriculture. 202455 Citations
Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets
Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal, Rama Gautam, Fahad Alblehai, Magdy Elghatwary, Aman Singh
Agronomy. 202353 CitationsOPEN ACCESS
Contemporary and Futuristic Intelligent Technologies for Rice Leaf Disease Detection
Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal
2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). 202233 Citations
Related on ScholarIQ
Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification
Paper
Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images
Paper
Resource-efficient federated learning over IoAT for rice leaf disease classification
Paper
Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets
Paper
Contemporary and Futuristic Intelligent Technologies for Rice Leaf Disease Detection
Paper
Exploring Classification of Rice Leaf Diseases using Machine Learning and Deep Learning
Paper