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A multi-condition AM was retrained using the proposed features.
Using the proposed features, a visualization framework is developed for word collocation analysis.
In the proposed algorithm, different episodes of a video file starting by key frames are classified independently by using the proposed features.
The average performance in the classification of speech under stress was improved by 10%to15%5% using the proposed features, compared to traditional methods of stressed speech classification.
The results obtained using the proposed features are compared with other existing texture descriptors (e.g., local binary pattern and homogeneous texture descriptors).
The results show that a high classification rate, close to 90%, is obtained for this family in this Furnariidae group using the proposed features and classifiers.
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We observed that the results using the proposed feature are almost the least deformation on the specific zones.
As a result, the sample information extracted by using the proposed feature extraction method is very suitable for identifying SIPs and the WELM classifier plays an important role for improving prediction accuracy.
The results obtained using the proposed feature were compared with the results obtained using the gradient magnitude (GM), the balanced energy map (BEM) [16], the prohibited points (PP) [17], and the context-aware saliency (CAS) [19] (We used the MATLAB implementation from [24]).
Using the proposed feature selection approach described in Section 2.1, select the set of control points P = { p i } in the live image L by following steps 1 7 of Section 2.1.
The use of the proposed features for predicting the handedness of writers is also planned.
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CEO of Professional Science Editing for Scientists @ prosciediting.com