Sharma M K, Dhiman Nitesh, Mishra Vishnu Narayan
Department of Mathematics, C.C.S. University, Meerut 250004, India.
Department of Computer Application, SCRIET, C.C.S. University, Meerut 250004, India.
Appl Soft Comput. 2021 Jul;105:107285. doi: 10.1016/j.asoc.2021.107285. Epub 2021 Mar 9.
This paper presents a model based on mediative fuzzy logic in this COVID-19 pandemic. COVID-19 (novel coronavirus respiratory disease) has become a pandemic now and the whole world has been affected by this disease. Different methodologies and many prediction techniques based on various models have been developed so far. In the present article, we have developed a mediative fuzzy correlation technique based on the parameters for COVID-19 patients from different parts of India. The proposed mediative fuzzy correlation technique provides the relation between the increments of COVID-19 positive patients in terms of the passage of increment with respect to time. The peaks of infected cases in connection with the other condition are estimated from the available data. The mediative fuzzy logic mathematical model can be utilized to find a good fit or a contradictory model for any pandemic model. The proposed approach to the prediction in COVID-19 based on mediative fuzzy logic has produced promising results for the continuous contradictory prediction in India.
本文提出了一种基于中介模糊逻辑的COVID-19疫情模型。COVID-19（新型冠状病毒呼吸道疾病）现已成为大流行病，全世界都受到了这种疾病的影响。到目前为止，已经开发了不同的方法和许多基于各种模型的预测技术。在本文中，我们基于印度不同地区COVID-19患者的参数开发了一种中介模糊关联技术。所提出的中介模糊关联技术提供了COVID-19阳性患者增量与增量随时间推移之间的关系。根据现有数据估计与其他情况相关的感染病例峰值。中介模糊逻辑数学模型可用于为任何大流行模型找到合适的或矛盾的模型。所提出的基于中介模糊逻辑的COVID-19预测方法在印度的连续矛盾预测中取得了有希望的结果。
