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The code for the data transformation and the two-level CNV-association tests was written in the C/C++ programming language.
Similar associations were also confirmed using SEM and different approaches to data transformation and the treatment of missing data.
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A development strategy will be presented for integrating CUMULVS with the given visualization libraries and environments, including discussion of the various data transformations and the visualization pipeline necessary for converting raw CUMULVS data into fully rendered graphical entities.
By applying two preprocessing methods, data transformation and clustering, the data were prepared for the learning algorithms.
The analysis was performed in both MNI space and the subject's OIS, due to potential concerns regarding the correctness of the measurements after data transformation and interpolation during the spatial normalization procedure.
Obviously more programming is needed for reading the data, outlier control, data transformation, and post-processing the results to calculate confidence intervals, equivalence limits and plotting the graphs.
Because a fuzzy-set score can be any number between 0 and 1, it can avoid information loss in the process of data transformation and more accurately reflect the situations of the chosen cases.
Explicit statements on data transformation and descriptions of the normalisation techniques applied prior to data analyses (e.g. classification) were not reported in 57 (37.5%) and 104 (68.4%) of the methodology papers respectively.
In this paper, we concentrate on the initial data transformation and its effect on the prediction of surface roughness in high-torque face milling operations.
Utilizing clustering and data transformation and reduction led to the accuracy of the model to improve by 50%.
This effectively enables the much needed back-tracking of data transformation and processing operations to the original raw data files (Baggerly and Berry, 2011; Deus et al., 2012).
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