摘要
Robotics&Machine Learning Daily News的一位新闻记者兼工作人员新闻编辑每日新闻-机器学习的新数据在一份新的报告中呈现。根据NewsRx编辑在印度加齐阿巴德的新闻报道，研究称：“人类活动识别(HAR)是一个活跃的研究课题，因为它在许多现实世界的应用中都有应用，比如健康监测和生物识别用户识别。构成医疗物联网(IoMT)和网络物理系统的重要组成部分的智能可穿戴设备可以每天提供有关人类活动的信息。可使用S软生物识别技术进行用户识别。这项研究的财政支持来自沙特国王大学。我们的新闻记者从SRM科技研究所的研究中获得了一句话："在过去的几年里，HAR的流行问题解决方法之一是人工智能方法S。由于安全性与稳健性有关，我们的主要目标是以更好的模型能力来解决这个问题。我们考虑了机器学习算法如随机森林(RF)、决策树(DT)、k-近邻(K-NN)(和深度学习算法如卷积神经网络(CNN)、短期记忆(LSTM)和门控递归单元(GRU))以达到HAR的目的.为了提高模型的性能,我们引入了优化技术以及CNN、LSTM和GRU .我们依靠随机梯度下降(S GD).并对Adam和RMSProp进行了优化，通过精度和F-1评分来评估模型的强度。此外，本研究还在三个包含多种人类活动的DA TASET上进行了研究。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting out of Ghaziabad, India, by NewsRx editors, research stated, “Human Activity Recognition (HAR) is an active research topic as it finds use in many real-world applications such as health mo nitoring and biometric user identification. Smart wearables which form an integr al part of the Internet of Medical Things (IoMT) and Cyber-Physical Systems can provide information about human activities on a daily basis, which may be used a s soft biometrics for user identification.” Financial support for this research came from King Saud University. Our news journalists obtained a quote from the research from the SRM Institute o f Science and Technology, “Over the last few years, one of the popular problem-s olving approaches for HAR has been in the form of artificial intelligence method s. Since security is related to robustness, our primary aim is to solve the prob lem with better model capabilities. In this study, we consider machine learning algorithms like Random Forest (RF), Decision Trees (DT), K-Nearest Neighbors (k- NN)(and deep learning algorithms such as Convolutional Neural Networks (CNN), Lo ng Short Term Memory (LSTM), and Gated Recurrent Units (GRU)) for the purpose of HAR. In order to improvise the model performance, we introduce optimization tec hniques along with CNN, LSTM, and GRU. We rely on Stochastic Gradient Descent (S GD), and optimizers Adam and RMSProp, and evaluate the strength of the models us ing Accuracy and F-1 score. Moreover, the study has been carried out on three da tasets that incorporate several human activities.”
NETL
NSTL