Using leave-one-out cross-validation, the accuracy, sensitivity, specificity, and both false positive and false negative ratios of the proposed method were found to be 0.97, 0.98, 0.96, 0.04, and 0.02, respectively.
False negative ratios also increase when trying to discover small coding sequences as they lack splicing signals on either side of the single exon and show a decreasing signal-to-noise ratio as the size of the coding region decreases [ 30, 31].
Theoretical performance analysis is conducted to evaluate false negative ratio, false positive ratio, and compensation efficacy of the SIS.
Even though the world of biometric vocal identification constantly innovates and improves, there are still challenges and vocal identification solutions are still struggling with a bad false positives versus false negative ratio.
We evaluate the performance of our scheme both analytically and by simulations, which show that, at small overhead, the false positive ratio and the false negative ratio can both be made negligibly small.
The detection of the non-parametric attacks has a higher false negative ratio, 85.20%, confirming the theoretical and simulation-based results reported in Section 4. The detection of the parametric attacks also confirms the results obtained via numeric simulations, leading to the highest false negative ratio (about 88.63%).
We determine the values of control parameters, such as the sampling rate and window length, to minimize the false positive ratio, while keeping the false negative ratio sufficiently low and making the on-line processing possible.
Table 4 shows that the detection of the replay attack has the lowest false negative ratio, 64.06%, hence, confirming that this adversarial scenario is the most detectable situation with regard to the detection techniques reported in [4,5].
False negative ratio (FNR) is the number of members inaccurately detected as usual to the sum of members who are detected as not jammed and the number of members who are actually jammed. .
Those functions may be used to determine false positive ratio (FPR) and false negative ratio (FNR) functions which determine respectively the probability that an impostor is classified as a target and a target is classified as an impostor for a particular likelihood.
In order to analyze the effect of T1, we computed the false positive ratio (FPR) and false negative ratio (FNR) metrics as follows: FPR = FP TP + FP + FN FNR = FN TP + FP + FN (17) Figure 12 The effects of thresholds ( T 1 and T 2 ).
