Abstract
Fractal analysis is one of interesting research areas of computer science and engineering, which depicts a precise description of phenomena in modeling. Visual beauty and self-similarity has made it an attractive field of research. The fractal sets are the effective tools to describe the accuracy of the inequalities for convex functions. In this paper, we employ linear fractals mathbb{R}^{alpha } to investigate the (s,m)-convexity and relate them to derive generalized Hermite–Hadamard (HH) type inequalities and several other associated variants depending on an auxiliary result. Under this novel approach, we aim at establishing an analog with the help of local fractional integration. Meanwhile, we establish generalized Simpson-type inequalities for (s,m)-convex functions. The results in the frame of local fractional showed that among all comparisons, we can only see the correlation between novel strategies and the earlier consequences in generalized s-convex, generalized m-convex, and generalized convex functions. We obtain application in probability density functions and generalized special means to confirm the relevance and computational effectiveness of the considered method. Similar results in this dynamic field can also be widely applied to other types of fractals and explored similarly to what has been done in this paper.
Highlights
Introduction and prelimnariesFractional calculus based on differential and difference equations is of considerable importance due to their connection with real-world problems that depend on the instant time and on the previous time, in particular, modeling the phenomena by means of fractals, random walk processes, control theory, signal processing, acoustics, and so on
Fractal analysis is an entirely new field of research based on fractional calculus
It is interesting that the authors [21, 22] investigated the local fractional functions on fractal space deliberately, which comprises of local fractional calculus and the monotonicity of functions
Summary
Consider a random variable χ with generalized probability density function p : [λ1, λ2] → [0α∗ , 1α∗ ], which is generalized convex and has the cumulative distribution function. For more information related to probability density functions, see [49]. The following results are associated with Sect. 4. 5.2 Generalized special means Considering the following α∗-type special means [50]. For λ1 < λ2 and λ1, λ2 ∈ Rα∗ , we have: I.
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