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The algorithm shows high accuracy and robustness in experiments.
Thus it shows high accuracy, efficiency and stability in shock/turbulence interactions.
Conclusions: The diagnostic procedure shows high accuracy and is technically simple and rapid.
It shows high accuracy and robustness for a wide range of flow regimes.
The quadratic model developed using RsM shows high accuracy and canbe used for predicting within limit of factor investigated.
To further the practicability of this method, we utilize a dynamic grey prediction, which shows high accuracy for decision making.
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The comparison shows higher accuracy of the proposed algorithm, even for image data with significant noise.
However, the KNN classifier shows higher accuracy than other methods.
The GBS was the best of all, showing high accuracy at predicting the need for hospital-based intervention or death.
The results obtained from the FPGA show high accuracy in comparison to those computed with Matlab®.
The theoretical predictions agreed well with experimental results, showing high accuracy of this model.
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