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The main advantage of these solutions is to offer powerful visualization with numerous types of graph for data representation and allow high interactivity with these data for changing parameters or zooming.
The Box-Cox transformation provides a powerful tool for developing a parsimonious model for data representation and interpretation when the distribution of the dependent variable, or outcome measure, of interest deviates from the normal distribution.
Wavelet-based multiscale representation of data has been shown to be a powerful data analysis, modeling, and feature extraction tool due to its ability to provide efficient separation of deterministic and stochastic features.
Therefore, the Box-Cox power transformation provides a powerful tool for developing parsimonious models (i.e. applying linear mixed modeling) for data representation and interpretation.
In the later case, data transformation is one of the powerful tools for developing parsimonious models for detecting structural effects or predictive factors and for better data representation and interpretation [ 4- 6].
It's just a data representation problem.
This data model enhances interoperability by requiring apps to operate on a common data representation.
Common types of data representation are illustrated.
The quality data representation is divided into measured data representation, non-measured data representation, and summarized data representation.
To incorporate geographic features from the real world into digital data representations, modeling is a powerful abstraction mechanism.
Autoencoder is a very powerful method for the unsupervised learning of high-level data representations.
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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