基于区间值t球模糊关系和信息测度的COVID-19患者检测方法

Yinyu Wang, K. Ullah, T. Mahmood, Harish Garg, L. Zedam, Shouzhen Zeng, Xingsen Li
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引用次数: 3

摘要

在处理医学诊断问题时,关系和信息测度的概念具有重要意义。本文的目的是在区间值t球模糊(IVTSF)环境下,利用关系和信息度量来研究COVID-19全球大流行情景。IVTSF集(IVTSFS)允许描述人类意见的四个方面,即会员,禁欲,非会员和拒绝等级,以重要的方式处理信息并减少信息损失。我们提出了IVTSF环境下的相似性度量和相似性关系,并研究了它们的性质。考虑到COVID-19全球大流行,将信息措施和关系应用于医疗诊断问题。如何确定诊断基于症状的病人使用相似的措施和关系进行了讨论。最后,通过实例说明了使用IVTSF框架处理此类问题的优点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Methods for Detecting COVID-19 Patients Using Interval-Valued T-Spherical Fuzzy Relations and Information Measures
The concepts of relations and information measures have importance whenever we deal with medical diagnosis problems. The aim of this paper is to investigate the global pandemic COVID-19 scenario using relations and information measures in an interval-valued T-spherical fuzzy (IVTSF) environment. An IVTSF set (IVTSFS) allows describing four aspects of human opinions i.e., membership, abstinence, non-membership, and refusal grade that process information in a significant way and reduce information loss. We propose similarity measures and relations in the IVTSF environment and investigate their properties. Both information measures and relations are applied in a medical diagnosis problem keeping in view the global pandemic COVID-19. How to determine the diagnosis based on symptoms of a patient using similarity measures and relations is discussed. Finally, the advantages of dealing with such problems using the IVTSF framework are demonstrated with examples.
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