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Here, we propose a flexible method for functional alignment, "neural code converter," which converts one subject's brain activity pattern into another's representing the same content.
The performance of code converter is 44.81%.
The code converter acts as a level one classifier.
Considering the code converter output, let us take a set of unit vectors to be as ?
This scenario impacts the optimization of code converter outputs using postclassifier to accomplish a singleton result.
Table 6 shows the rhythmicity of the code converter classifier for hard thresholding of each wavelet.
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Code converters were considered as a level one classifier.
Performance index of code converters output using different wavelet transforms for hard thresholding methods are tabulated in Table 5.
The paper provides an efficient design and layout of code converters based on quantum-dot cellular automata using QCADesigner tool.
Code converters were found to have a performance index and quality value of 33.26 and 12.74, respectively, which is low.
Due to the nonlinearity obtained and also the poor performance found in the code converters, an optimization was vital for the effective classification of the signals.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com