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Titlebook: Mathematical Analysis and Computing; ICMAC 2019, Kalavak R. N. Mohapatra,S. Yugesh,C. Kalaivani Conference proceedings 2021 The Editor(s)

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樓主: 劉興旺
41#
發(fā)表于 2025-3-28 16:50:29 | 只看該作者
42#
發(fā)表于 2025-3-28 20:39:51 | 只看該作者
A New Class of Incomplete Generalized Tri Lucas Polynomials,A new class of polynomials, namely, incomplete .(.)-. tri Lucas polynomials, is defined. Some identities involving these new polynomials are proved. A relation between incomplete .(.)-. tribonacci polynomials and incomplete .(.)-. tri Lucas polynomials is obtained. Some identities involving derivatives of these polynomials are also obtained.
43#
發(fā)表于 2025-3-29 00:10:00 | 只看該作者
44#
發(fā)表于 2025-3-29 07:08:07 | 只看該作者
45#
發(fā)表于 2025-3-29 07:38:49 | 只看該作者
,A Theorem on Coupled Fixed Point and It’s Application,In this paper, we define a new class of mappings and prove a common coupled fixed point theorem in the context of complex valued metric spaces; we give an application to show the significance of theory in solving the system of fractional differential equation with nonlocal multi-point integral boundary condition.
46#
發(fā)表于 2025-3-29 14:11:58 | 只看該作者
R. N. Mohapatra,S. Yugesh,C. KalaivaniDiscusses recent advances in areas of mathematical analysis, soft computing, approximation and optimization.Includes original, cutting-edge research articles by expert researchers from around the worl
47#
發(fā)表于 2025-3-29 15:36:43 | 只看該作者
48#
發(fā)表于 2025-3-29 21:16:05 | 只看該作者
https://doi.org/10.1007/978-981-33-4646-8real and complex analysis; harmonic analysis; fixed point theory; functional analysis; dynamical systems
49#
發(fā)表于 2025-3-30 03:48:32 | 只看該作者
es a challenge which prevents deep learning models from obtaining the results they have achieved most especially in the field of medical imaging. Recently, self-training with deep learning has become a powerful approach to leverage labelled training and unlabelled data. However, a challenge of gener
50#
發(fā)表于 2025-3-30 06:10:38 | 只看該作者
B. N. Prasad Rao,M. Rangammaogress of online social networks, how to use the additional social information for recommendation has been intensively investigated. In this article, we devise a graph embedding technology to incorporate the customers’ social network side information into conventional matrix factorization model. Mor
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