Titlebook: Database and Expert Systems Applications; 33rd International C Christine Strauss,Alfredo Cuzzocrea,Ismail Khalil Conference proceedings 202

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Fault Detection in Seismic Data Using Graph Attention Networkl in the graph domain based on seismic amplitude similarity. Then, we apply GAT to classify the faults. Both the training and testing sets contain both synthetic and real data. The proposed methodology gives good accuracy when applied to field data.

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Reconciliation of Mental Concepts with Graph Neural Networksdict structures in the form of usage links with high accuracy and assist in the reconstruction of missing information. We evaluate this model on a new knowledge management dataset and show that it is superior to traditional embedding methods. Further, we show that it outperforms related work in an established general link prediction task.

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Clustering-Based Cross-Sectional Regime Identification for Financial Market Forecasting market forecasting. Our approach makes use of a nonlinear model to account for the cross-sectional regime dependencies, neglected by most existing studies, that can improve the performance of a forecasting model significantly. Experimental results on both synthetic and real-world dataset demonstrat