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The texture identifier model extracts thirteen features to recognize texture type and the porosity analyzer determines percentage of each type of porosity based on eleven features extracting from the thin section image.
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Classification rate Training set Test set Features extracted from spectrogram 100%49.66% Features extracted from Stage 1 100%71.00% Features extracted from Stage 2 100%98.8%%.
The result successfully represented features extracted as specific patterns.
The features extracted are used to train the artificial classifier.
The dataset is composed of several features extracted from images.
features extracted from the data stream of multiple, diverse sensors.
The lexical features extract the structure of the URLs.
Table 1 shows the 50 features extracted from "Color features" and "Composition features" section.
Features extracted are based on different performance indicators like root mean square (RMS) value and kurtosis.
Backpropagation neural-network (BPNN) based binary classifiers are developed for authentication utilizing the features extracted.
The features extracted from dynamic resistance curve were mainly influenced by welding current.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com