摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑每日新闻-关于人工智能ce的详细数据已经呈现。根据NewsRx编辑来自哈马德-本-哈利法大学的新闻，该研究指出，"压力识别，特别是使用机器学习(ML)和心率变异性(HRV)等生理数据，有希望进行心理健康干预。"这项研究的资助者包括卡塔尔国家研究基金。新闻记者从哈马德·本·哈利法大学的研究中获得了一句话：“然而，情感计算和医疗保健领域有限的数据集可能导致关于ML模型性能的不准确结论。本文采用监督学习算法，使用HRV测量对压力和放松状态进行分类。考虑到与小数据相关的局限性，我们的研究结果表明，随机森林模型在区分压力和非压力状态方面表现出最好的性能，显著地，它在区分压力和松弛状态方面表现出比中性状态（F1分数：65.8%）更高的性能。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news originating from Hamad Bin Khalifa Uni versity by NewsRx editors, the research stated, “Stress recognition, particularl y using machine learning (ML) with physiological data such as heart rate variabi lity (HRV), holds promise for mental health interventions.” Funders for this research include Qatar National Research Fund. The news reporters obtained a quote from the research from Hamad Bin Khalifa Uni versity: “However, limited datasets in affective computing and healthcare resear ch can lead to inaccurate conclusions regarding the ML model performance. This s tudy employed supervised learning algorithms to classify stress and relaxation s tates using HRV measures. To account for limitations associated with small datas ets, robust strategies were implemented based on methodological recommendations for ML with a limited dataset, including data segmentation, feature selection, a nd model evaluation. Our findings highlight that the random forest model achieve d the best performance in distinguishing stress from non-stress states. Notably, it showed higher performance in identifying stress from relaxation (F1-score: 8 6.3%) compared to neutral states (F1-score: 65.8%).”
NETL
NSTL