GitHub14d
Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach (CNN-TA)
In this study, we propose a novel algorithmic trading model CNN-TA using a 2-D convolutional neural network based on image processing properties. In order to convert financial time series into 2-D ...
IEEE15d
Transformer-Based Multi-Scale 3D Convolutional Network for Motor Imagery Classification
Abstract: Due to the variability and non-stationarity of electroencephalography (EEG) signal across different recording scenarios and subjects, it is crucial to have methods with strong generalization ...
Frontiers17d
Editorial: Deep learning for high-dimensional sense, non-linear signal processing and intelligent diagnosis
Lu et al.’s Graph Frequency Attention Convolutional Neural Network (GFACNN) for predicting depression treatment response from EEG signals represents a significant advancement in medical imaging ...
Frontiers23d
ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network
Considering that convolutional neural networks (CNNs) are widely utilized to capture the EEG features of time series data (Hu et al., 2023), we designed a base feature extractor (BaseFE) to extract ...
Nvidia announces DLSS 4 with Multi Frame Generation
Nvidia says its new frame generation model is 40 percent faster and uses 30 percent less VRAM than the old one. By replacing the old Convolutional Neural Networks with its new transformer model, the ...
IEEE26d
An Effective Deep Neural Network Architecture for EEG-Based Recognition of Emotions
Recent progress in machine learning by deep neural networks has opened up ... learning method of emotion recognition using EEG data, which prefaces a combination of two-dimensional (2D) convolutional ...