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Machine Learning algorithms need to be limited by time in order to ingest data in sizes which can be processed meaningfully.
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In this way, the transmitted data in size will be twice of the directly encrypted image.
Current diagnosis technologies (e.g. magnetic resonance imaging, electroencephalogram) produce huge quantity data (in size and dimension) for detection, monitoring and treatment of neurological diseases.
The scale of big data in size and complexity makes it difficult to process, analyze, and extract useful information (Burkhardt 2014).
Also, the authors evaluated Contra's capability to identify contradictory data in different sizes of datasets.
ConTra's capability to enable the identification of contradictory data in different sizes of datasets is examined.
The proposed energy banding algorithm allows maximal temporal reuse of data in band sizes that can flexibly accommodate different architectural features.
We divided the data into training and test data sets in size 13616 (80%) and 3404 (20%), respectively.
Two blocks of data equal in size to the experimental data were simulated 160 times using the same values for μ and σ obtained as maximum-likelihood values fitted to the original experimental data for the control block.
Big data can be conceptualized as heterogeneous data, unprecedented in size and complexity, lacking in structure, and coming from many sources (Monteith et al. 2015).
One of the three histograms is computed by splitting the range of the data in equal size bins.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com