基于本体的推测感模型对倦怠人群大脑图像的分类分析

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chandrakirishnan Balakrishnan Sivaparthipan, Priyan Malarvizhi Kumar, Thota Chandu, BalaAnand Muthu, Mohammed Hasan Ali, Boris Tomaš
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引用次数: 0

摘要

倦怠是一种由长期过度的工作压力引起的疲惫状态。这可以用倦怠和身体后果的生物学解释来检验,并将其与长期剧烈活动进行分类。本研究旨在利用治疗和预防的本体分析以及基于推测感模型的中间层形成,对倦怠人群的大脑图像进行分类,以对抗长期的情绪活动。在这一部分中,基于假设感模型的治疗和预防本体论分析以及中间层的形成被用于倦怠人群的分类分析。该方法在本体创建平台上进行,并进行分类分析。计算分析发现了结果,并对大脑图像进行了分类。对倦怠人群的脑图像进行了分类分析,对长期剧烈活动进行了分离,并建立了治疗和预防倦怠人群脑图像的本体论。分析得到了结果,并得出了倦怠人群大脑图像的准确性、召回率、存储率、计算时间、特异性和分类结果。此外,所有这些基于假设意义模型的治疗和预防的本体论分析以及中间层的形成具有超过50%的预测敏感性(SN)和超过90%的特异性(SP)。对倦怠人群大脑性能的分类比较表明,所提出的系统比现有方法成功得多,尤其是在98%的评分准确率上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification analysis of burnout people's brain images using ontology-based speculative sense model

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%.

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来源期刊
Computational Intelligence
Computational Intelligence 工程技术-计算机:人工智能
CiteScore
6.90
自引率
3.60%
发文量
65
审稿时长
>12 weeks
期刊介绍: This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.
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