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In this paper, we have considered the utility of multi-normalization and ancillary measures, for the optimal score level fusion of fingerprint and voice biometrics.
Visually evoked potential (VEP) and bright light flash electroretinography are important ancillary measures for making a preoperative decision, but these tests are not completely reliable either.
The availability of more robust data for NSI reduction rates, and broadening the scope of the model to include ancillary measures for hospital conversion to SED usage, outpatient and paramedic device use, and transmission of other blood-borne diseases, would strengthen the model.
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When using ancillary measures as benchmarks for patient improvement, the UFS-QOL was able to discriminate among levels of change in patient-reported overall treatment effect and satisfaction.
We reported summary likelihood ratios as an ancillary measure of predictive accuracy.
Integration weight optimization techniques based on multi-normalization and ancillary measures are proposed here for improving the performance of the fingerprint and voice biometrics system.
In contrast to participant-based ratings such as meaningfulness and association, all other ancillary measures were based on either text corpora (for LSA, word frequency and phrase frequency) or surface properties (number of letters).
To further enhance the usefulness of this resource, ancillary measures obtained from other sources are also provided for each pair.
These findings were confirmed with ancillary measures.
The abbreviation IWWAM in the Table 2 denotes 'integration weight without ancillary measures'.
Ancillary measures derived from the feature space and the score space are used in addition to the matching score vectors, for weighing the modalities, based on their relative degradation.
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