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In this study, three artificial neural network performance measuring criteria and also three important factors which affect the selected criteria have been studied.
A typical MCDM process involves two main elements: (1) measuring criteria weights, and (2) ranking of alternatives (Wright and Loosemore 2001).
It is possible to study viability as a parameter of antibiotic susceptibility at single-cell level, measuring criteria such as impermeability of membrane to dyes, maintenance of membrane potential, and the presence of metabolic activity measured by the production of a fluorescent metabolite from a nonfluorescent precursor [ 12].
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In addition, we applied two scales for measuring criterion validity.
A class separability measuring criterion C(k) is adopted in order to rank all the features.
Mutual information is used as a co-regulatory measuring criterion for efficiently selecting genes for hyperedge generation.
The area between the empirical receiver operating characteristic (ROC) curve and the random classifier slope has been proposed as a class separability measuring criterion [ 43].
Measured criteria have been related to the subjective impressions reported in the non technical literature.
We measured criteria many times in each patient until extubation.
See Table 1 for the outcome measures criteria (Salter et al. 2005).
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