  Tabular data from IIoT devices are typically analyzed using decision
tree-based machine learning techniques, which struggle with high-dimensional
and numeric data. To overcome these limitations, techniques converting tabular
data into images have been developed, leveraging the strengths of image-based
deep learning approaches such as Convolutional Neural Networks. These methods
cluster similar features into distinct image areas with fixed sizes, regardless
of the number of features, resembling actual photographs. However, this
increases the possibility of overfitting, as similar features, when selected
carefully in a tabular format, are often discarded to prevent this issue.
Additionally, fixed image sizes can lead to wasted pixels with fewer features,
resulting in computational inefficiency. We introduce Vortex Feature
Positioning (VFP) to address these issues. VFP arranges features based on their
correlation, spacing similar ones in a vortex pattern from the image center,
with the image size determined by the attribute count. VFP outperforms
traditional machine learning methods and existing conversion techniques in
tests across seven datasets with varying real-valued attributes.
