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In terms of computational complexity, the proposed approach makes the computation of biomarker tractable.
Several filtering methods are compared in terms of computational complexity.
The results are promising in terms of computational efficiency.
The performance of supervised learning algorithms is discussed in terms of computational effort.
The proposed and developed model is superior in terms of computational speed and accuracy.
The method is described in terms of computational domain, grid density, turbulence model and boundary conditions.
Successful use of surrogates shall result in significant savings in terms of computational time and resources.
Experiments indicate the effectiveness of this approach in terms of computational resources and solution quality.
The proposed micromodel is also examined in terms of computational efficiency and accuracy.
Meanwhile, the SSD method is found to be very efficient in terms of computational cost.
NAFSA shows good performance in terms of computational accuracy and computational efficiency for large scale RAP.
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