Semi-supervised learning for multi-view and non-graph data using Graph Convolutional Networks.

Semi-supervised learning with a graph-based approach has become increasingly popular in machine learning, particularly when dealing with situations where labeling data is a costly process. Graph Convolution Networks (GCNs) have been widely employed in semi-supervised learning, primarily on graph-structured data like citations and social networks. However, there exists a significant gap in applying these methods to non-graph multi-view data, such as collections of images.

Optimization of Dental Devices and Tools Used on Teeth.

Taking decisions is important in every aspect of life. Decision-making has become a difficult problem in any situation where there are multiple criteria. The application of multicriteria decision-making methods that can bring mathematical and logical solutions to the problem from an analytical perspective has experienced considerable growth recently.