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Application of SIREN on the in silico network, as shown in Figure 3a, indicated that the scoring functions S1 and S2 have comparable performance while out-performing S3 and S4.
Figure 9 shows that the algorithms perform well in terms of estimating the states and have comparable performance to the KF. Figure 10 shows us that the estimated observation from KF is near real; however, the corresponding state estimates are not near the true values.
The DVC systems have comparable performance at lower rates, superior to H.264/AVC Intra.
Additionally, the results indicate that both versions of the STM have comparable performance.
However, there are 45% participants considering that two methods have comparable performance.
A high-performance FPGA can have comparable performance and rival the GPGPU in some cases.
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The two methods have comparable performances when applied to simulated signals (Figures 2 and 3).
It is interesting to note from mode 1 that PSSM based model and hybrid-6 have comparable performances (∼98% accuracy) and better than hybrid-8 (∼95% accuracy).
Of course, on the preference-specific datasets (i.e., approach-specific datasets), the preferred method should have the best performance; meanwhile, some other methods would have comparable performances.
In this paper, we will compare our method to the PrivateSVM algorithm, since DPERM has comparable performance with PrivateSVM.
MLP and linear regression had comparable performance and robustness.
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