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A full feature set and an arm-only feature set were used to compare verification performance.
Figure 8 shows the receiver operating characteristic (ROC) curves that compare verification performance of the features obtained from the Eigenfaces [13], scale-invariant feature transform (SIFT) [75], LBP [76], multiple kernel learning (MKL) [77], and the DGHMs.
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Comparing verification metrics of all models, SETAR model presents the best forecasting performance.
By comparing verification tests and predicted results, the optimized operating conditions were pH 7.1, 22 °C, 32.5 % (14,000 U/ml) loading amounts of TreS enzyme and after 24 h, along with a 70.6 % trehalose conversion rate.
Additionally, current criteria used to compare the verification phase and incremental test V̇O2max still warrant further investigation.
We compare identity verification performance when either a full feature set or an arm feature set is used in both training and testing phases.
More recently, one study attempted to compare distinct verification phases, one at 105% of the peak work rate and another at two stages prior to the end of the incremental test [42].
The experimental and computational results were then compared for verification.
Certain results obtained from the receptance method and a finite element program were compared for verification purposes and showed good agreement.
By comparing the Verification Result Table with power supply security standards for urban areas in China, following a differential and hierarchical way to check one by one, the comparison result has a high reliability.
Univariate, multivariate and stepwise models of ≥ 5mm displacements showed an increased likelihood of displacement in weeks 1 and 2 with low melting point alloy (LMPA) blocks compared with multileaf collimators, film verification compared with electronic portal imaging (EPI) and increased number of fractions imaged.
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