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In [4], a two-user cooperative system was considered and in that context it was shown that the AF approach performs better than the DF, with the performance gap closing as the SNR increases.
Other than the performance with respect to similarity ratio, our approach performs better than both of REVEAL and MDL approaches in elucidating the attractors.
Using the benchmark sets, Iskar et al. computed the early retrieval performance of their approach and showed that their proposed approach performs better than previous methods [ 15].
Our extensive experiments show that in both centralized and parallel settings, our proposed GP approach achieves considerably better performance than the state-of-the-art BK approach and the hybrid approach performs better than both of them.
The authors demonstrated that their propose approach performs better than wavelet applications.
It is clear that our approach performs better than the conventional method.
The history-based approach performs better than the C-State-based approach [22].
The simulation result clearly shows that our novel approach performs better than the existing ones.
Additionally we observed that at higher variations, the coordinated approach performs better.
Classifier approach performs better for the most dense tissue (ACR/BIRADS IV).
The results show that the proposed approach performs better than methods based on conventional clustering techniques.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com