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.

Modeling epidemics based on quantum decision-making model by the qutrit states and employing neutrosophic form of percolation analysis

This paper aims to model epidemics based on the quantum decision-making model by the qutrit states. We generate COVID-19 data for Turkey based on real-world values and an estimate of R (reproduction number). Then, we develop a neutrosophic representation of both actual and hypothetical probabilistic data. As a third stage, we use the Mann–Whitney U test to determine whether or not our values represent a genuine danger or a real risk. In addition, we develop a percolation model for both the actual and hypothetical probabilities functions. We next create a probabilistic vector allowing us to employ a more detailed definition of uncertainty in the COVID-19 risk analysis. The scalar data are used for the majority of descriptive statistics and data analysis. However, descriptive analysis for circumstances that are more closely tied to cognitive and semantic features may, on occasion, provide more in-depth conclusions than descriptive analysis based on scalar data in certain cases. As a result, we demonstrate that the analysis of COVID-19 spreads may be carried out using neutrosophic and vectorial forms of probability functions. Furthermore, we demonstrate that percolation theory may be utilized to simulate the spread of COVID-19 in a neutrosophic form. When researching societal understandings of risk and safety, it is standard practice to do so without first gaining a clear understanding of the conceptual foundations of the "risk” phenomena under investigation. It is possible for researchers who are looking at how a particular phenomenon is linked with risk by society actors to find themselves in a dilemma as to what the real basis of the connection is. By offering a mathematical semantic model for the descriptive study of COVID-19 spread, the analysis contributes to the elucidation of the concerns in the literature. © 2023 Elsevier Inc. All rights reserved.