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In the second phase, single frame-based features were utilized for the detection followed by a third phase where the detection was completed by features based on the group of frames reflecting the overall characteristics of the video.
Dependency parsing, gene mention tagging and term-based features are utilized along with a Huber classifier.
Seven graph based features are used to characterize the topological structure of the network and utilize them to detect botnets.
In total, 12 features are utilized from MIDI files.
In most speech processing tasks, log spectral envelope (cepstral) features are utilized as effective features.
To select lines containing features a simple statistics based approach was utilized.
Taking these coefficients as feature vectors, K means based clustering is utilized to realize the automatic classification of bearing fault status.
Three feature extraction methods were utilized to seek for sensitive fault features.
For cell based responses, unpaired t-tests were utilized.
The reduced features from each category were utilized as the inputs to homogenous base classifiers.
A computer based, validated DAI was utilized during interviews.
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