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Three-dimensional point cloud classification is fundamental but still challenging in 3-D vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and high-level ...

For graph-level classification, pooling operations, categorized into global and hierarchical methods, are crucial. Hierarchical pooling, like DiffPool, clusters nodes but has limitations addressed by ...

When inserting the edges, you can use a weighted coin, changing the odds that an edge connects two points. Imagine that the weight of the coin is controlled by a dial. Initially, the coin will always ...

With this study, a new algorithm for brain MRI tumor classification and segmentation using an equilibrium optimizer and deep learning (EOADL-BTSC) is presented. In the first stage of the presented ...

Graph neural networks help to process and analyze complex graph-structured data, unlocking new possibilities across a wide range of applications.

Understanding the equilibrium price and quantity is crucial for businesses and economists alike, as it helps in determining the optimal production and consumption levels in a market. The equilibrium ...