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Local topology based features were used to qualitatively describe possible flux deviations.
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Hence, content based features are used to detect the attacks.
Ten different sequence based features are used to predicting the correct hyperfold.
Seven graph based features are used to characterize the topological structure of the network and utilize them to detect botnets.
Thus features based on physiochemical properties are taken into consideration, and PCA and NLF based features are used in this paper.
A 48-dimensional color histogram-based feature is used for this purpose.
Other t f image-based features were used to represent the EEG signals in [9].
Therefore, only sequence-based features were used for parameterization in this study.
Likewise, MFCC-based features were used to train HMM classifiers in [16] and reported high classification rates.
When DEP-based features were used with the same hierarchical clustering method, however, same-stage samples were clustered first.
When the extracted DEG-based features were used for the individual CD4+ samples, the samples were mixed together across the different HIV-1 stages, especially between Acute and Chronic (in orange and red, respectively) and between Non-progressive and Uninfected (in blue and green, respectively).
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