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These methods consider two features of a clustering: the 'intensity' and the 'grain'.
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In this work, we evaluate six normalization methods, considering two different places where this normalization is applied: before and after the feature extraction. 1. Mean removal: Subtract the mean value from each value in a feature or signal vector.
Moreover, we present a comparison of the state-of-the-art MCC authentication methods considering five evaluation metrics.
Table 6 and Table 7 present accuracy, precision, and Macro-F1 for all methods considering four datasets for the 2-class and 3-class experiments, respectively.
The processing of 2-azetidinones 10 12 in the active site of serine enzymes has been computed by ab initio methods, considering three models.
The performance of this hybrid optimization technique was evaluated by comparing optimization results of thirty benchmark functions of different dimensions with those obtained by other numerical methods considering three criteria.
The difference is that the alternative method considers two approximate first order differential equations.
Unlike uncorrected counts conducted since 1991, our estimation method considered two major sources of error: spatial variation in distribution and imperfect detection.
This clustering method considers two types of criteria as multiple objectives and optimizes them simultaneously by using a modified multi-objective evolutionary algorithm with new encoding and operators.
This model was established by finite element method considering two kinds of microorganisms' growth, internal mass transfer and bio-electrochemical kinetics.
This method considers two important evolutionary features of DNA sequence: transition/transversion rate bias and codon frequency bias.
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