# Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review

arXiv: https://arxiv.org/abs/2202.02456 · HTML: https://mlsanity.com/paper/2202.02456/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 39 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 1 of 1)

- [Machine learning for Internet of Things data analysis: A survey](https://mlsanity.com/paper/1802.06305/) (2018)

## Built on by (most cited first, 1 of 1)

- [Mitigating Presentation Attack using DCGAN and Deep CNN](https://mlsanity.com/paper/2207.00161/) (2022)

## Abstract (arXiv)

The Internet of things (IoT) is a rapidly advancing area of technology that has quickly become more widespread in recent years. With greater numbers of everyday objects being connected to the Internet, many different innovations have been presented to make our everyday lives more straightforward. Pattern recognition is extremely prevalent in IoT devices because of the many applications and benefits that can come from it. A multitude of studies has been conducted with the intention of improving speed and accuracy, decreasing complexity, and reducing the overall required processing power of pattern recognition algorithms in IoT devices. After reviewing the applications of different machine learning algorithms, results vary from case to case, but a general conclusion can be drawn that the optimal machine learning-based pattern recognition algorithms to be used with IoT devices are support vector machine, k-nearest neighbor, and random forest.