摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新数据在一份新的报告中呈现。根据NewsRx编辑在巴基斯坦旁遮普的新闻报道，研究表明，“昏昏欲睡时开车会带来很大的风险，包括认知功能下降和可能发生事故，这可能导致严重后果，如创伤、经济损失、伤害或死亡。”我们的新闻记者从Khwaja Fareed工程与信息技术大学的研究中获得了一句话：“人工智能的使用可以有效地检测司机的困倦，”这项研究旨在解决实时和准确的困倦检测以减轻疲劳相关事故的影响的关键需求。利用超过5分钟收集的超宽带雷达数据，将数据集分割成1分钟的块并转换成灰度图像。使用二维卷积神经网络从图像中提取空间特征。随后，将随机森林、XGBoost和支持向量机结合使用硬投票准则的RF-XGB-SVM集成分类器,取得了很好的分类效果,准确率为96.6%。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Punjab, Pakist an, by NewsRx editors, research stated, "Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, whi ch can lead to severe consequences such as trauma, economic losses, injuries, or death." Our news journalists obtained a quote from the research from Khwaja Fareed Unive rsity of Engineering and Information Technology: "The use of artificial intellig ence can enable effective detection of driver drowsiness, helping to prevent acc idents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over f ive minutes, the dataset was segmented into one-minute chunks and transformed in to grayscale images. Spatial features are retrieved from the images using a two- dimensional Convolutional Neural Network. Following that, these features were us ed to train and test multiple machine learning classifiers. The ensemble classif ier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machin e using a hard voting criterion, performed admirably with an accuracy of 96.6% ."
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