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The commonly used techniques in predicting real-time crash likelihood are Neural Network (NN) [3, 15, 16] and Support vector machines (SVMs) [17, 18].
The indicator and technical index are neural network architecture parameters that assist to extrapolate the market logic and knowledge rules that influence the TAIEX futures market structure via an integral assessment of physical quantities.
Distributed associative memories are neural network models that fit perfectly well to the vision of cognition emerging from current neurosciences.
Associative memories are neural network models developed to capture some of the known characteristics of human memories [ 12, 13].
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The basis for the rock mechanical studies are neural network-derived synthetic clay content logs (SCCL), which present the clay content along the borehole in a semi-quantitative way with five groups of increasing clay content.
Deep learning models are neural networks with more layers.
Most of these AI systems are neural networks, a type of computing architecture loosely modeled after the human brain.
Those methods are neural networks (NNs), support vector machine (SVM), and random forests (RFs).
The methods of machine learning that we focus on in this review are neural networks (NNs), support vector machine (SVM), and random forests (RFs).
Among them is neural network (NN).
Currently, the most suitable technique to approach this objective is neural network inversion of radiative transfer.
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