# Majid Kundroo

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/majid-kundroo/

## Facts

| Field | Value |
| --- | --- |
| Citations | 357 |
| Field | Privacy-Preserving Technologies in Data |
| h-index | 9 |
| i10-index | 9 |
| Last Known Institution | Chungbuk National University |
| OpenAlex ID | https://openalex.org/A5012289983 |
| ORCID iD | 0000-0003-4858-1919 |
| Works | 18 |

## Researcher papers

- [Sentiment analysis of social media response on the Covid19 outbreak](https://scholariq.org/papers/sentiment-analysis-of-social-media-response-on-the-covid19-outbreak/)
- [Deep LDA : A new way to topic model](https://scholariq.org/papers/deep-lda-a-new-way-to-topic-model/)
- [Apple diseases: detection and classification using transfer learning](https://scholariq.org/papers/apple-diseases-detection-and-classification-using-transfer-learning/)
- [Federated learning with hyper-parameter optimization](https://scholariq.org/papers/federated-learning-with-hyper-parameter-optimization/)
- [WSN-Driven Advances in Soil Moisture Estimation: A Machine Learning Approach](https://scholariq.org/papers/wsn-driven-advances-in-soil-moisture-estimation-a-machine-learning-approach/)
- [Experimental Evaluation and Analysis of Federated Learning in Edge Computing Environments](https://scholariq.org/papers/experimental-evaluation-and-analysis-of-federated-learning-in-edge-computing/)
- [FLDQN: Cooperative Multi-Agent Federated Reinforcement Learning for Solving Travel Time Minimization Problems in Dynamic Environments Using SUMO Simulation](https://scholariq.org/papers/fldqn-cooperative-multi-agent-federated-reinforcement-learning-for-solving/)
- [Node-Based Horizontal Pod Autoscaler in KubeEdge-Based Edge Computing Infrastructure](https://scholariq.org/papers/node-based-horizontal-pod-autoscaler-in-kubeedge-based-edge-computing/)
- [Demystifying Impact of Key Hyper-Parameters in Federated Learning: A Case Study on CIFAR-10 and FashionMNIST](https://scholariq.org/papers/demystifying-impact-of-key-hyper-parameters-in-federated-learning-a-case-study/)
- [FedEasy : Federated learning with ease](https://scholariq.org/papers/fedeasy-federated-learning-with-ease/)

## Researcher topics

- [Privacy-Preserving Technologies in Data](https://scholariq.org/topics/privacy-preserving-technologies-in-data/)
- [Stochastic Gradient Optimization Techniques](https://scholariq.org/topics/stochastic-gradient-optimization-techniques/)
- [Cryptography and Data Security](https://scholariq.org/topics/cryptography-and-data-security/)
- [Mobile Crowdsensing and Crowdsourcing](https://scholariq.org/topics/mobile-crowdsensing-and-crowdsourcing/)
- [Topic Modeling](https://scholariq.org/topics/topic-modeling/)

## Researcher university

- [Chungbuk National University](https://scholariq.org/institutions/chungbuk-national-university/)

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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.