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This would mean that devices would only lack simultaneous access when they fell back to older network methods like 3G.
We then cover Monte Carlo methods and numerical optimization; finally, neural network methods and nonlinear time-series analysis.
Recently, two deep neural network methods based on atom-pair features have been reported [41, 42].
Neural network methods rely on simple algebra and operator experience, and are computationally efficient.
It is compared to other neural network methods specifically designed for handling imbalanced data.
Toivonen et al. 2012 used network methods in analyzing similarity data with the aim to study Finnish emotion concepts.
The recurrent neural network methods have applications in kinematics control, support vector machine learning, and related branches of engineering.
Joshi et al. [52] used neural network methods (NN) to classify AD by considering common risk factors.
Our numerical results are compared with the solutions obtained by the wavelet and artificial neural network methods.
Our results obtained from the gradient boosting and neural network methods outperformed the state-of-the-art classification approaches.
Tensor network methods [14 18] are powerful in one spatial dimension where they track strong correlations accurately.
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