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Found 5 papers, 0 papers with code
Semi-supervised Training for Knowledge Base Graph Self-attention Networks on Link Prediction
The task of link prediction aims to solve the problem of incomplete knowledge caused by the difficulty of collecting facts from the real world.
Modeling the Social Influence of COVID-19 via Personalized Propagation with Deep Learning
The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis.
Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images
First, the Subject-Shared module in CS-DASA receives multi-frame EEG image data from both source and target subjects and learns the common feature representations.
piSAAC: Extended notion of SAAC feature selection novel method for discrimination of Enzymes model using different machine learning algorithm
The experimental results show that, probabilistic neural network algorithm with piSAAC feature extraction yields an accuracy of 98. 01%, sensitivity of 97. 12%, specificity of 95. 87%, f-measure of 0. 9812and AUC 0. 95812, over dataset S1, accuracy of 97. 85%, sensitivity of 97. 54%, specificity of 96. 24%, f-measure of 0. 9774 and AUC 0. 9803 over dataset S2.
Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification
Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years.
