Almagrabi Alaa O, Abdullah Saleem, Shams Maria, Al-Otaibi Yasser D, Ashraf Shahzaib
Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589 Saudi Arabia.
Department of Mathematics, Abdul Wali Khan University Mardan, Mardan, Khyber Pakhtunkhwa Pakistan.
J Ambient Intell Humaniz Comput. 2022;13(4):1687-1713. doi: 10.1007/s12652-021-03130-y. Epub 2021 Apr 5.
The emergency situation of COVID-19 is a very important problem for emergency decision support systems. Control of the spread of COVID-19 in emergency situations across the world is a challenge and therefore the aim of this study is to propose a q-linear Diophantine fuzzy decision-making model for the control and diagnose COVID19. Basically, the paper includes three main parts for the achievement of appropriate and accurate measures to address the situation of emergency decision-making. First, we propose a novel generalization of Pythagorean fuzzy set, q-rung orthopair fuzzy set and linear Diophantine fuzzy set, called q-linear Diophantine fuzzy set (q-LDFS) and also discussed their important properties. In addition, aggregation operators play an effective role in aggregating uncertainty in decision-making problems. Therefore, algebraic norms based on certain operating laws for q-LDFSs are established. In the second part of the paper, we propose series of averaging and geometric aggregation operators based on defined operating laws under q-LDFS. The final part of the paper consists of two ranking algorithms based on proposed aggregation operators to address the emergency situation of COVID-19 under q-linear Diophantine fuzzy information. In addition, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19.
新冠疫情的紧急情况对于应急决策支持系统而言是一个非常重要的问题。在全球范围内控制新冠疫情在紧急情况下的传播是一项挑战，因此本研究的目的是提出一种用于控制和诊断新冠疫情的q线性丢番图模糊决策模型。基本上，本文包括三个主要部分，以实现适当且准确的措施来应对应急决策情况。首先，我们提出了毕达哥拉斯模糊集、q阶正交对模糊集和线性丢番图模糊集的一种新颖推广，称为q线性丢番图模糊集（q-LDFS），并讨论了它们的重要性质。此外，聚合算子在聚合决策问题中的不确定性方面发挥着有效作用。因此，基于q-LDFS的某些运算定律建立了代数范数。在本文的第二部分，我们基于q-LDFS下定义的运算定律提出了一系列平均和几何聚合算子。本文的最后一部分由基于所提出的聚合算子的两种排序算法组成，以解决q线性丢番图模糊信息下的新冠疫情紧急情况。此外，提供了新型食肉动物（新冠疫情）情况的数值案例研究，作为基于所提出算法的应急决策应用。结果探索了我们所提出方法的有效性，并提供了准确的应急措施来应对新冠疫情的全球不确定性。
