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The transformed data formulated a feature vector consisting of modular energy and modular entropy, and the feature vector was fed to an artificial neural network (ANN) classifier.
As a control the L4440 vector was fed with identical feeding conditions.
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(2) Feedback: the Gaussian vector is fed back to the transmitter during T f slots.
The th user symbol vector is fed to an -point inverse fast Fourier transform (IFFT) to generate the OFDM signal waveform.
Each feature vector is fed into a sequence of fully connected (fc) layers that finally branch into two sibling output layers: one that produces softmax probability estimates over "aircraft" class and "background" class and another layer that outputs four real-valued numbers, which encode refined bounding-box positions for each of the "aircraft" class.
Each feature vector is fed into a sequence of fully connected (fc) layers that finally branch into two sibling output layers: one that produces softmax probability estimates over aircraft class and background class, another layer outputs the bounding-box positions for each of the aircraft.
In the first step, each source of data is subject to preprocessing, and then the resulting feature vector is fed to the target classifier either unaltered or after applying certain feature selection algorithms, i.e. CFS and wrapper.
The abovementioned SPM based feature vectors were fed to the proposed classifier.
To evaluate the recognition rate achieved using mixture models, the feature vectors are fed into the GMM/SVM classifier.
These features vectors are fed to the SVM classifier, explained in the following section, for cavity candidates detection.
For concentration prediction, the extracted feature vectors were fed as input to the three methods (ANFIS, SC-FIS and BP-ANN) independently.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com