基于Shannon熵的理论人工智能识别新冠肺炎病毒株

H. Nieto-Chaupis
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引用次数: 2

摘要

基于当前大流行是由一种失序引起的这一事实,本文采用香农熵的概念对Covid-19感染数据进行建模。它的使用代表了一种人工智能的提议,这种人工智能可能被用于高级软件中,以对新感染进行即时测量。所提出的理论适用于英国数据的情况,产生了一个有趣的匹配。因此,可以看出,流行病的浪潮可以用应变和熵的幽灵来解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Theoretical Artificial Intelligence Based on Shannon Entropy to Identify Strains in Covid-19 Pandemic
Based in the fact that ongoing pandemic is caused by a kind of disorder, this paper employs the concept of Shannon entropy to model data of infections by Covid-19. The usage of this represents a proposal as a type of artificial intelligence that might be used in advanced softwares to perform instantaneous measurements of new infections. The presented theory is applied to the case of UK data, yielding an interesting matching. Therefore, it is seen that waves of pandemics can be explained in terms of apparition of strains and entropy.
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