Eurosystem Publishes New Framework For Overseeing Electronic Payments
This study considers two emotions namely ‘Valence’ and ‘Arousal’ for classification. The experimental results demonstrate that the proposed approach produced accuracies of 96.1% for low/high valence and 99.6% for low/high arousal classification. A further comparison of the proposed method is also carried out and it is seen that the proposed method outperforms other compared methods. Electron behaviour and movement, particularly as observed in the first electron tubes. In the proposed framework, the signals are pre-processing for removing noises by low-pass filtering and then delta rhythm is extracted. After that, the extracted rhythm signals are converted into the EEG rhythm images by employing the continuous wavelet transform and then deep features are discovered by using a pre-trained convolutional neural networks model. Afterwards, MobileNetv2 is used for deep feature selection to obtain the most efficient features and finally, long short term memory method is employed for classi...