Titlebook: Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy; Proceedings of the S Manoj Sahni,José M. Merigó,Rajkumar

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lored areas such as data mining based on optimization techniques from biological behavior of animals and applications in Traditional Chinese Medicine clinical research and health care management.978-3-642-28319-2978-3-642-28320-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

實現(xiàn)

Anulika Sharma,Ram Naresh Saraswatics (EmSemi2023) focuses on developing a ”multimodal semiotic” approach to the existing semiotics discussed in the fields of linguistics and philosophy of langu978-3-031-60510-9978-3-031-60511-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

沉積物

積云

Conference proceedings 2022ations of fuzzy sets and intelligent computing. The book is a collection of best selected research papers presented at the Second International Conference on “Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy (MMCITRE 2021),” organized by the Department of Mathematics

COLON

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喃喃訴苦

Cesàro-Riesz Product Summability , Factor for an Infinite seriese used to reduce inaccuracy. By using the appropriate conditions, previous results can be easily obtained. Like this, the Bounded Input Bounded Output (BIBO) stoutness of drive is enhanced by absolute summability because it is necessary and sufficient condition for BIBO stability.

和平主義

Energy of 2-Corona of Graphslus of latent roots of the adjacency matrix obtained from graph ., and its largest eigen value is known to be spectral radius. The energy upper bound for 2-corona product of same graphs and different graphs in subsequent products are acquired.

Rankle

Optimal Mild Solutions of Time-Fractional Stochastic Navier-Stokes Equation with Rosenblatt Process e aid of stochastic analysis, fractional calculus, fixed point theorem of condensing maps, and by establishing an appropriate measure of non-compactness. Further, an optimal mild solution is established.

宇宙你

Shape Preserving Hermite Interpolation Reproducing Ellipseite interpolant preserves the monotonicity. We introduce the notion of .-convex and show that convexity of .-convex data is preserved by our Hermite interpolant. The biggest advantage of our method is that it reproduces ellipse. We also provide some numerical examples to validate our theory.