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Existing studies on network robustness usually exploit a single network metric to generate attack strategies, which simulate intentional attacks on a network, and compute a metric-induced robustness estimation, called R. While some metrics are easy to compute, e.g. degree, others require considerable computation efforts, e.g. betweenness centrality.
Rational B-spline surfaces require considerable computation.
This saves considerable computation time and hence contributes the major advantage of our algorithm.
However, training conventional CNNs requires considerable computation and a big dataset.
Obviously, it is a considerable computation cost, especially in the setting where each sparse solution is also computationally expensive.
It is shown that considerable computation efficiency is achieved in terms of CPU time without losing accuracy.
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Considerable computational time saving is obtained compared to uniform mesh computations.
For simulation performed for analysis purposes at the engineering level, which requires a considerable amount of computation power, a discrete-time system simulation that computes significant values at every single unit time using the established mathematical model or engineering model is mainly employed.
However, the process of performing feature selection in some cases can take a considerable amount of computation time.
The experimental data obtained showed very good agreements with numerical results and considerable reduction in computation time is observed.
It was found that multi-scale method offered a considerable interest in computation efficiency while ensuring reliability in numerical simulation.
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