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Titlebook: Computational Sciences - Modelling, Computing and Soft Computing; First International Ashish Awasthi,Sunil Jacob John,Satyananda Panda Con

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51#
發(fā)表于 2025-3-30 11:44:17 | 只看該作者
Sustainable Intensification—An Overviewy of the images captured by this modality. Among the plentiful researches that happened in the field of despeckling, the non-local total variation methods have demonstrated promising results by maintaining relevant details and edges present in images. Nevertheless, the model is computationally expen
52#
發(fā)表于 2025-3-30 14:27:40 | 只看該作者
https://doi.org/10.1007/978-3-030-61817-9In this paper, we study the time-fractional diffusion equation on a metric star graph. The existence and uniqueness of the weak solution are investigated and the proof is based on eigenfunction expansions. Some priori estimates and regularity results of the solution are proved.
53#
發(fā)表于 2025-3-30 18:28:28 | 只看該作者
54#
發(fā)表于 2025-3-31 00:12:45 | 只看該作者
Interval Valued Fuzzy Graph and Complement Numbernotion of complement number of an edge and prove that given a complement IVFG . along with its complement numbers, the IVFG for which . acts as the complement can be uniquely determined. We also study the range of variation of complement number and some of its properties.
55#
發(fā)表于 2025-3-31 03:08:16 | 只看該作者
Fermatean Fuzzy Soft Sets and Its Applicationssoft set. A few basic operations are specified such as union, intersection and complement. Further an algorithm to solve decision-making problem is developed using the aggregation operators defined for this structure.
56#
發(fā)表于 2025-3-31 08:32:01 | 只看該作者
Conference proceedings 2021uting, held in Kozhikode, Kerala, India, in September 2020.?.The 15 full papers and 6 short papers presented were thoroughly reviewed and selected from the 150 submissions. They are organized in the topical secions on computing; soft computing; general computing; modelling..
57#
發(fā)表于 2025-3-31 11:34:23 | 只看該作者
58#
發(fā)表于 2025-3-31 13:24:08 | 只看該作者
Learning Numerical Viscosity Using Artificial Neural Regression Network work, we attempt to learn the numerical viscosity of underlying three-point shock-capturing schemes using non-linear regression neural network in a supervised learning paradigm. Details on network architecture, used data type and training are elaborated. Computed results by underlying schemes using
59#
發(fā)表于 2025-3-31 20:55:04 | 只看該作者
Computational Study of Some Numerical Methods for the Generalized Burgers-Huxley Equation of advective, dissipative and reactive terms. We considered a numerical experiment where the coefficients of advective and reactive parameters are equal. These methods are two versions of non-standard finite difference and an explicit exponential finite difference method. Satisfactory results are o
60#
發(fā)表于 2025-3-31 23:36:02 | 只看該作者
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