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As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy.
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Hydrotreating of RCR was modeled by a discrete kinetic model.
The clustering algorithm was preceded by a preprocessing step to select the points in time frequency domain that only one mode makes contribution in the responses.
HOSVD modeling of the decoupling signal received is employed by introducing a preprocessing scheme for dictionary construction.
In fact, by adding a preprocessing step to enhance the input grayscale image, the results on all the following tasks of the analysis chain should be improved.
Furthermore, an improved algorithm with O(n(m+k)|S|) time is proposed by applying an efficient preprocessing for the MLCS problem.
In our previous work [27], we have experimentally investigated the performance of PBCCS in an additive white Gaussian noise (AWGN) channel by employing 2-level Daubechies-2 wavelet (DB2) as a discrete wavelet transformation (DWT) to preprocess the received signal.
HNB uses a discrete structural model and hence requires the discretization for preprocessing with continuous signal attributes, for example, expression microarray data.
While discretization was originally performed as a necessary preprocessing step for certain supervised machine-learning algorithms which require discrete feature spaces [3], several additional+ advantages have since been noted.
The proposed detection framework contains a novel network architecture, which initializes with a preprocessing layer and is followed by a well-designed CNN.
The size of the formulation is further reduced by a preprocessing method that removes suboptimal switches and links from consideration.
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