论文标题
在物联网设备中基于机器学习的模式识别的应用：审查
Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review
论文作者
论文摘要
物联网（IoT）是一个迅速发展的技术领域，近年来迅速变得更加普遍。随着更多的日常物品连接到互联网，已经提出了许多不同的创新，以使我们的日常生活更加简单。在物联网设备中，模式识别非常普遍，因为它可能带来的许多应用和好处。进行了多种研究，目的是提高速度和准确性，降低复杂性并降低物联网设备中模式识别算法的总体处理能力。在审查了不同机器学习算法的应用后，结果因情况而异，但可以得出一个总体结论，即与IoT设备一起使用的最佳基于机器学习的模式识别算法是支持向量机器，K-Neareast邻居和随机森林。
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.
