  Ranking vertices of multidimensional networks is crucial in many areas of
research, including selecting and determining the importance of decisions. Some
decisions are significantly more important than others, and their weight
categorization is also imortant. This paper defines a completely new method for
determining the weight decisions using artificial intelligence for importance
ranking of three-dimensional network vertices, improving the existing Ordered
Statistics Vertex Extraction and Tracking Algorithm (OSVETA) based on
modulation of quantized indices (QIM) and error correction codes. The technique
we propose in this paper offers significant improvements the efficiency of
determination the importance of network vertices in relation to statistical
OSVETA criteria, replacing heuristic methods with methods of precise prediction
of modern neural networks. The new artificial intelligence technique enables a
significantly better definition of the 3D meshes and a better assessment of
their topological features. The new method contributions result in a greater
precision in defining stable vertices, significantly reducing the probability
of deleting mesh vertices.
