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The results show that the proposed method outperforms general IT2-FLCs in terms of speed, computational cost, and robustness.
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It is shown that the proposed method outperforms the general Markovian-based prediction schemes under various traffic load scenarios.
Compared with the general approaches such as direct table lookup or direct programminglevel solution, our method outperforms them either in physical gate count or throughput.
In addition, our method outperforms previous molecular methods aimed at determining only dreissenid DNA presence4,28,29.
Analysis of simulation data showed that this method outperforms existing methods.
This new method outperforms previous works [18].
Table 8 shows that HBE method outperforms other classifiers using all validation methods.
Therefore, the network-based method outperforms the hybrid-property based method.
Our method outperforms Dengs MRF method in efficiency of both parameter estimation and prediction performance.
The proposed method outperforms above mentioned methods.
Nevertheless, our method outperforms the GP classifier.
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