New Control Chart Based On Neutrosophic Maxwell Distribution with Decision Making Applications

The neutrosophic approach is a potential area to provide a novel framework for dealing with uncertain data. This study aims to introduce the neutrosophic Maxwell distribution (M̃D) for dealing with imprecise data. The proposed notions are presented in such a manner that the proposed model may be used in a variety of circumstances involving indeterminate, ambiguous, and fuzzy data. The suggested distribution is particularly useful in statistical process control (SPC) for processing uncertain values in data collection. The existing formation of VSQ-chart is incapable of addressing uncertainty on the quality variables being investigated. The notion of neutrosophic VSQchart (Ṽ SQ) is developed based on suggested neutrosophic distribution. The parameters of the suggested Ṽ SQ-chart and other performance indicators, such as neutrosophic power curve (P̃C), neutrosophic characteristic curve (C̃C) and neutrosophic run length (R̃L) are established. The performance of the Ṽ SQ-chart under uncertain environment is also compared to the performance of the conventional model. The comparative findings depict that the proposed Ṽ SQ-chart outperforms in consideration of neutrosophic indicators. Finally, the implementation procedure for real data on the COVID-19 incubation period is explored to support the theoretical part of the proposed model © 2023,Neutrosophic Sets and Systems. All Rights Reserved.

Assessing countries' performances against COVID-19 via WSIDEA and machine learning algorithms.

The COVID-19 pandemic, which first spread to the People of Republic of China and then to other countries in a short time, affected the whole world by infecting millions of people and have been increasing its impact day by day. Hundreds of researchers in many countries are in search of a solution to end up this pandemic. This study aims to contribute to the literature by performing detailed analyses via a new three-staged framework constructed based on data envelopment analysis and machine learning algorithms to assess the performances of 142 countries against the COVID-19 outbreak. Particularly, clustering analyses were made using k-means and hierarchic clustering methods. Subsequently, efficiency analysis of countries were performed by a novel model, the weighted stochastic imprecise data envelopment analysis. Finally, parameters were analyzed with decision tree and random forest algorithms. Results have been analyzed in detail, and the classification of countries are determined by providing the most influential parameters. The analysis showed that the optimum number of clusters for 142 countries is three. In addition, while 20 countries out of 142 countries were fully effective, 36% of them were found to be effective at a rate of 90%. Finally, it has been observed that the data such as GDP, smoking rates, and the rate of diabetes patients do not affect the effectiveness level of the countries.