Burnout is a state of exhaustion that results from prolonged, excessive workplace stress. This can be examined with the biological explications of burnout and physical consequences and classified against prolonged vigorous activities. The research aims to classify burnout people's brain images against prolonged emotional activities using ontology analysis of treatment and prevention and intermediate layers formation based on a speculative sense model. In this segment, the Ontology analysis of Treatment and prevention and intermediate layers formation based on a hypothetical sense model is employed for burnout people's classification analysis. The methodology is performed in the platform of ontology creation and performs the classification analysis. The calculation analysis found the result, and the brain images were classified. The classification analysis of burnout people's brain images, separation of prolonged vigorous activities, and the ontology creation for treatment and prevention against burnout people's brain images were obtained. The analysis received the result, and the results of the precision, recall, storage, computation time, specificity, and classification of burnout people's brain images were obtained. Furthermore, all these Ontology analysis of Treatment and prevention and intermediate layers formation based on a hypothetical sense model had the prediction sensitivity (SN) over 50% and specificity (SP) over 90%. The Classification of Burnout People's Brain performance comparison shows that the proposed system is much more successful than existing methods, especially on a scoring accuracy of 98%.

中文翻译：

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基于本体的思辨感觉模型对倦怠人群大脑图像进行分类分析

倦怠是一种因长期、过度的工作压力而导致的疲惫状态。这可以通过倦怠和身体后果的生物学解释来检查，并根据长时间的剧烈活动进行分类。该研究旨在利用基于推测感觉模型的治疗和预防本体论分析以及中间层形成，对倦怠人群的大脑图像与长时间情绪活动进行分类。本节采用基于假设感觉模型的治疗和预防以及中间层形成的本体论分析来对职业倦怠人群进行分类分析。该方法论是在本体创建平台上进行的，并进行分类分析。计算分析得出结果，并对大脑图像进行分类。获得了职业倦怠者脑图像的分类分析、长时间剧烈活动的分离以及职业倦怠者脑图像治疗和预防的本体创建。分析收到结果，得到倦怠人群大脑图像的精确度、召回率、存储量、计算时间、特异性、分类结果。此外，所有这些基于假设意义模型的治疗和预防以及中间层形成的本体分析的预测灵敏度（SN）超过50％，特异性（SP）超过90％。倦怠人脑性能分类比较表明，所提出的系统比现有方法成功得多，特别是在评分准确度达到 98% 的情况下。