Fractional Hermite–Hadamard–Mercer-Type Inequalities for Interval-Valued Convex Stochastic Processes with Center-Radius Order and Their Related Applications in Entropy and Information Theory

Ahsan Fareed Shah, Serap Özcan, Miguel Vivas-Cortez, Muhammad Shoaib Saleem, A. Kashuri
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Abstract

We propose a new definition of the γ-convex stochastic processes (CSP) using center and radius (CR) order with the notion of interval valued functions (C.RI.V). By utilizing this definition and Mean-Square Fractional Integrals, we generalize fractional Hermite–Hadamard–Mercer-type inclusions for generalized C.RI.V versions of convex, tgs-convex, P-convex, exponential-type convex, Godunova–Levin convex, s-convex, Godunova–Levin s-convex, h-convex, n-polynomial convex, and fractional n-polynomial (CSP). Also, our work uses interesting examples of C.RI.V(CSP) with Python-programmed graphs to validate our findings using an extension of Mercer’s inclusions with applications related to entropy and information theory.
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具有中心-半径阶的区间值凸随机过程的分数赫米特-哈达玛-默塞尔式不等式及其在熵和信息论中的相关应用
我们利用中心和半径(CR)阶与区间值函数(C.RI.V)的概念,提出了 γ 凸随机过程(CSP)的新定义。利用这一定义和均方分式积分,我们为凸函数、tgs-凸函数、P-凸函数、指数型凸函数、Godunova-Levin 凸函数、s-凸函数、Godunova-Levin s-凸函数、h-凸函数、n-多项式凸函数和分式 n-多项式(CSP)的广义 C.RI.V 版本推广了分式 Hermite-Hadamard-Mercer 型夹杂。此外,我们的工作还使用了 C.RI.V(CSP)与 Python 编程图的有趣实例,利用与熵和信息论相关应用的梅塞尔夹杂的扩展来验证我们的发现。
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