# Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification

arXiv: https://arxiv.org/abs/2001.09636 · HTML: https://mlsanity.com/paper/2001.09636/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things](https://mlsanity.com/paper/2311.04944/) (2023)
- [Incremental Learning Techniques for Online Human Activity Recognition](https://mlsanity.com/paper/2109.09435/) (2021)
- [Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2409.01329/) (2024)
- [Decentor-V: Lightweight ML Training on Low-Power RISC-V Edge Devices](https://mlsanity.com/paper/2509.18118/) (2025)
- [Machine Learning and Ensemble Approach Onto Predicting Heart Disease](https://mlsanity.com/paper/2111.08667/) (2021)
- [Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment](https://mlsanity.com/paper/2312.03020/) (2023)
- [An Interpretable Deep Learning Approach for Skin Cancer Categorization](https://mlsanity.com/paper/2312.10696/) (2023)
- [CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining](https://mlsanity.com/paper/2509.08586/) (2025)
- [Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks](https://mlsanity.com/paper/2606.31594/) (2026)

## Abstract (arXiv)

In recent years, the growth of Internet of Things (IoT) as an emerging technology has been unbelievable. The number of networkenabled devices in IoT domains is increasing dramatically, leading to the massive production of electronic data. These data contain valuable information which can be used in various areas, such as science, industry, business and even social life. To extract and analyze this information and make IoT systems smart, the only choice is entering artificial intelligence (AI) world and leveraging the power of machine learning and deep learning techniques. This paper evaluates the performance of 11 popular machine and deep learning algorithms for classification task using six IoT-related datasets. These algorithms are compared according to several performance evaluation metrics including precision, recall, f1-score, accuracy, execution time, ROC-AUC score and confusion matrix. A specific experiment is also conducted to assess the convergence speed of developed models. The comprehensive experiments indicated that, considering all performance metrics, Random Forests performed better than other machine learning models, while among deep learning models, ANN and CNN achieved more interesting results.