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The subpar performance of this implementation can be attributed to the fact that the algorithm is implemented in C code running on the 50-MHz NIOS II processor.
In this paper, we share the performance of this implementation.
The over all performance of this implementation on 9 each of processors is given.
We evaluate the performance of this implementation in an indoor testbed under different network conditions in terms of link qualities, network loads and traffic types.
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The present results demonstrate that the implementation of multi-class fuzzy support vector machine can achieve 90% classification accuracy, and performance measures of this implementation are significantly superior to the others.
This technique was successfully implemented on a physical 25-node network, and the performance of this network implementation is evaluated.
The performance of this SHTB implementation is demonstrated by obtaining intermediate strain rate results for tensile yield strength of Polycarbonate in an unprecedented quality.
To see the performance of this particular implementation of (FABS-SVM), the kernel used is radial basis function and the parameters are the following: C value is 10 and the kernel parameter is 1.
The communication times are very important and any improvement on this communication would have a significant performance of the implementation.
Performance of the implementation is measured with a scaleable number of processors and compared to a sequential reference implementation.
Additionally, performance of the implementation has been evaluated from data quality evaluation point of view.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com