ESA-GCN: An Enhanced Graph-Based Node Classification Method for Class Imbalance Using ENN-SMOTE Sampling and an Attention Mechanismdoi:10.3390/app14010111COMPUTATIONAL complexityCLASSIFICATIONERROR ratesCLASSIFICATION algorithmsSAMPLING methodsIn recent years, graph neural networks (GNNs) have a...
(HS).Fromadifferentper-spective,aninherentcorrelationbetweendistortionandspatialdirectionthroughstatisticalanalysisisfound.Basedontheobserveddistortiondistribution,anovelenhancedhexagonal-basedsearchwithdirection-orientedinnersearch(EHS-DIOS)isproposedtoavoidrealdistortioncalculationandthusreducehighcomputation.Experimental...
Finally, a combinatorial attention mechanism is devised. Specifically, our spatial-temporal (ST) attention module and limb attention module (LAM) are integrated into a multi-input branch and a mainstream network of the proposed model, respectively. Extensive experiments on three large-scale datasets,...
Participants performed the first five cognitive tasks; the Rey's Auditory Verbal Learning Task (RAVLT), Picture Recognition Task, Corsi Block Task, Stroop Task, and Modified Attention Network Task (MANT). There was then a short break where blood pressure and pulse were recorded three times in ...
Mobi-Sync introduces spatial correlation of the nodes' velocities to estimate the varying time propagation delay. In the protocol, all nodes are classified into three categories: surface nodes, super nodes and ordinary nodes. Surface nodes are equipped with a global positioning system (GPS) to ...