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Applications to a set of experimental tests and to a 3D building model are introduced for validation and to evaluate the computational efficiency of the model.
Furthermore, a series of experiments were conducted to evaluate the computational efficiency of the studied updating mechanisms based on several UCI datasets.
To evaluate the computational efficiency, the time used by every algorithm was recorded.
In the second experiment, several quantitative results are conducted to evaluate the computational efficiency.
To evaluate the computational efficiency of the proposed algorithm outlined in Table 2, we use the algorithm to evaluate the reliability of the WSN deployments in Table 4.
To evaluate the computational efficiency of REMS, we calculated mean transition probabilities of exchanging pairs of replicas adjoining in temperature during the course of the simulation of the thermal folding of TC5b.
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We compare and evaluate the computational cost and efficiency of LMFG applied to these two formulations using a direct solver for the preconditioners.
The computational efficiency is evaluated using the results based on the conventional bounding methods.
Furthermore, the computational efficiency is evaluated by comparing the given method with the back projection algorithm and other methods with different approximated orders.
The computational efficiency of NeuroRD is evaluated by comparison with Smoldyn for the above simulations, as well as simulations of larger numbers of molecules.
To evaluate the performance of each method, we used four basic criteria, the computational efficiency, the quality of reconstructed dynamics, the accuracy of estimated parameters and the quality of predictability.
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