Search

The main aim of this study was to apply an analytical method to solve a nonlinear system of fractional differential equations (FDEs). This method is the Adomian decomposition method (ADM), and a comparison between its results was made by using a numerical method: Runge-Kutta 4 (RK4). It is proven that there is a unique solution to the system. The convergence of the series solution is given, and the error estimate is also proven. After that, the susceptible-infected-recovered (SIR) model was taken as an real phenomenon with such systems. This system is discussed with three different fractional derivatives (FDs): the Caputo-Fabrizio derivative (CFD), the Atangana-Baleanu derivative (ABD), and the Caputo derivative (CD). A comparison between these three different derivatives is given. We aimed to see which one of the new definitions (ABD and CFD) is close to one of the most important classical definitions (CD). [ABSTRACT FROM AUTHOR]

In multi-criteria decision-making problems, we may have to deal with numbers that are in interval forms, like those of membership, non-membership grades and indeterminacy grades representing unique attributes of elements. When decision-makers come across such an environment, the decisions are harder to make and the most significant factor is that we need to combine these interval numbers to generate a single real number, which can be done using aggregation operators or score functions. To overcome this hindrance, we introduce the notion of interval-valued intuitionistic neutrosophic hypersoft set. This eventually helps the decision-maker to collect the data with no misconceptions. The primary aim of this study is to establish some operational laws for interval-valued intuitionistic neutrosophic hypersoft set. Also, we present the fundamental properties of two aggregation operators, interval-valued intuitionistic neutrosophic weighted average and interval-valued intuitionistic neutrosophic weighted geometric operators. Also, we propose an algorithm for the technique of order of preference by similarity to ideal solution (TOPSIS) method based on correlation coefficients to choose a suitable employee among the alternative using Leipzig leadership model in an organization for an upcoming new project. Finally, we present a comparative study with existing similarity measures to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]