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A small and a medium to large size data set are employed.
It was also observed during the model analysis that GFMAPR model was less effective when employed for large size data forecasting.
Recently, the large size data, power and real-time processing abilities are major issues in Digital Signal Processing/multimedia applications which require an adaptable architecture.
Some other informatics challenges like the storage, transfer, and retrieval of large size data may bring additional errors [ 6, 7].
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It is a way to improve FFT especially for large-size data.
This can be seen that MCWT-SG-C faces a performance bottleneck when processing large-size data.
Although corpus-driven genre analysis is still in its early development and its accuracy at the discursive level can be unsatisfactory, its ability to handle large-size data remains extremely helpful in improving research validity and detecting less-obvious textual patterns.
The CLiPS uses several preprocessing techniques for data reduction to deal with large size of data.
To overcome such inefficiencies, some techniques split the large size of data randomly into small chunks of data blocks, so they can fit into the memory for carrying out the required computations [6].
Rectifying these potential artifacts manually is challenging due in part to the very large size of data present in FCPD and also due to the difficulties of validating individual data.
This concept is based on the idea that detecting a rare class of data, such as the needles, is hard to find in a haystack, such as a large size of data, as shown in Fig. 5.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com