Zulqarnain Muhammad, Shah Habib, Ghazali Rozaida, Alqahtani Omar, Sheikh Rubab, Asadullah Muhammad
Faculty of Computing, The Islamia University of Bahawalpur, Punjab, Pakistan.
Department and College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia.
Brain Sci. 2023 Jun 25;13(7):994. doi: 10.3390/brainsci13070994.
In today's world, stress is a major factor for various diseases in modern societies which affects the day-to-day activities of human beings. The measurement of stress is a contributing factor for governments and societies that impacts the quality of daily lives. The strategy of stress monitoring systems requires an accurate stress classification technique which is identified via the reactions of the body to regulate itself to changes within the environment through mental and emotional responses. Therefore, this research proposed a novel deep learning approach for the stress classification system. In this paper, we presented an Enhanced Long Short-Term Memory(E-LSTM) based on the feature attention mechanism that focuses on determining and categorizing the stress polarity using sequential modeling and word-feature seizing. The proposed approach integrates pre-feature attention in E-LSTM to identify the complicated relationship and extract the keywords through an attention layer for stress classification. This research has been evaluated using a selected dataset accessed from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze health-related stress data. Statistical performance of the developed approach was analyzed based on the nine features of stress detection, and we compared the effectiveness of the developed approach with other different stress classification approaches. The experimental results shown that the developed approach obtained accuracy, precision, recall and a F1-score of 75.54%, 74.26%, 72.99% and 74.58%, respectively. The feature attention mechanism-based E-LSTM approach demonstrated superior performance in stress detection classification when compared to other classification methods including naïve Bayesian, SVM, deep belief network, and standard LSTM. The results of this study demonstrated the efficiency of the proposed approach in accurately classifying stress detection, particularly in stress monitoring systems where it is expected to be effective for stress prediction.
在当今世界，压力是现代社会中导致各种疾病的主要因素，它影响着人类的日常活动。压力测量是影响政府和社会日常生活质量的一个因素。压力监测系统的策略需要一种准确的压力分类技术，该技术通过身体的反应来识别，以通过心理和情绪反应调节自身以适应环境变化。因此，本研究提出了一种用于压力分类系统的新型深度学习方法。在本文中，我们提出了一种基于特征注意力机制的增强型长短期记忆网络（E-LSTM），该机制通过序列建模和词特征捕捉来确定压力极性并进行分类。所提出的方法在E-LSTM中集成了预特征注意力，以识别复杂关系并通过注意力层提取关键词进行压力分类。本研究使用从2013年至2015年进行的第六次韩国国家健康与营养检查调查（KNHANES VI）中获取的选定数据集进行评估，以分析与健康相关的压力数据。基于压力检测的九个特征分析了所开发方法的统计性能，并将所开发方法的有效性与其他不同的压力分类方法进行了比较。实验结果表明，所开发的方法分别获得了75.54%、74.26%、72.99%和74.58%的准确率、精确率、召回率和F1分数。与朴素贝叶斯、支持向量机、深度信念网络和标准LSTM等其他分类方法相比，基于特征注意力机制的E-LSTM方法在压力检测分类中表现出卓越的性能。本研究结果证明了所提出方法在准确分类压力检测方面的效率，特别是在压力监测系统中，预计该方法对压力预测有效。
