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Found 4 papers, 1 papers with code
Classification of Hyperspectral Images by Using Spectral Data and Fully Connected Neural Network
In classification of the hyperspectral images, it is observed that almost all of the researchers used 2D or 3D convolution filters on the spatial data beside spectral data (features).
Generating Ten BCI Commands Using Four Simple Motor Imageries
A maximum of ten different BCI commands can be generated by using four motor imageries in pairs. This study aims to achieve high classification performances for BCI commands produced from four motor imageries by implementing a small-sized deep neural network (DNN). The presented method is evaluated on the four-class datasets of BCI Competitions III and IV, and an average classification performance of 81. 8% is achieved for ten classes.
Classification of Motor Imagery EEG Signals by Using a Divergence Based Convolutional Neural Network
In this study, we have investigated the effect of the augmentation process on the classification performance of MI EEG signals instead of using a preceding transformation such as the CSP, and we have demonstrated that by resulting in high success rates for the classification of MI EEGs, the augmentation process is able to compete with the CSP.
Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning
Finally, the third study shows a precision of 98. 29% on the noisy PASCAL dataset with transfer learning approach.
