# Meenakshi Aggarwal

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/meenakshi-aggarwal/

## Facts

| Field | Value |
| --- | --- |
| Citations | 398 |
| Field | Smart Agriculture and AI |
| h-index | 9 |
| i10-index | 9 |
| OpenAlex ID | https://openalex.org/A5000303741 |
| Works | 15 |

## Researcher papers

- [Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification](https://scholariq.org/papers/pre-trained-deep-neural-network-based-features-selection-supported-machine/)
- [Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images](https://scholariq.org/papers/lightweight-federated-learning-for-rice-leaf-disease-classification-using-non/)
- [Resource-efficient federated learning over IoAT for rice leaf disease classification](https://scholariq.org/papers/resource-efficient-federated-learning-over-ioat-for-rice-leaf-disease/)
- [Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets](https://scholariq.org/papers/federated-transfer-learning-for-rice-leaf-disease-classification-across/)
- [Contemporary and Futuristic Intelligent Technologies for Rice Leaf Disease Detection](https://scholariq.org/papers/contemporary-and-futuristic-intelligent-technologies-for-rice-leaf-disease/)
- [Exploring Classification of Rice Leaf Diseases using Machine Learning and Deep Learning](https://scholariq.org/papers/exploring-classification-of-rice-leaf-diseases-using-machine-learning-and-deep/)
- [Federated Learning on Internet of Things: Extensive and Systematic Review](https://scholariq.org/papers/federated-learning-on-internet-of-things-extensive-and-systematic-review/)
- [A comprehensive review of federated learning: Methods, applications, and challenges in privacy-preserving collaborative model training](https://scholariq.org/papers/a-comprehensive-review-of-federated-learning-methods-applications-and-challenges/)
- [Privacy preserved collaborative transfer learning model with heterogeneous distributed data for brain tumor classification](https://scholariq.org/papers/privacy-preserved-collaborative-transfer-learning-model-with-heterogeneous/)
- [FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification](https://scholariq.org/papers/fl-qnns-memory-efficient-and-privacy-preserving-framework-for-peripheral-blood/)

## Researcher topics

- [Smart Agriculture and AI](https://scholariq.org/topics/smart-agriculture-and-ai/)
- [Privacy-Preserving Technologies in Data](https://scholariq.org/topics/privacy-preserving-technologies-in-data/)
- [Spectroscopy and Chemometric Analyses](https://scholariq.org/topics/spectroscopy-and-chemometric-analyses/)
- [Artificial Intelligence in Healthcare and Education](https://scholariq.org/topics/artificial-intelligence-in-healthcare-and-education/)
- [Leaf Properties and Growth Measurement](https://scholariq.org/topics/leaf-properties-and-growth-measurement/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.