Abstract: Decoding motor imagery (MI) from electroencephalogram (EEG) signals is a cornerstone of brain–computer interface (BCI) systems. However, existing methods often face a critical tradeoff ...

Abstract: Combining brain–computer interface (BCI) technology with the Internet of Things (IoT) for practical motor rehabilitation applications is a critical research direction aimed at enhancing the ...

Abstract: Medical image analysis offers valuable visual support for clinical decision-making, yet the incorporation of quantitative data is essential for deeper diagnostic insight. The radiomics ...

Abstract: Depression is most common mental disorder that is affecting approximately 280 million individuals in the world. The stigma and lack of acceptance and awareness is still influencing people ...

Abstract: Current CNN-Transformer hybrid methods for remote sensing change detection aim to address the limitations of CNNs’ constrained receptive fields and Transformers’ local detail insensitivity.

Abstract: With the emergence of various large-scale deep-learning models, in remote sensing images, the object detection effect is also plagued by complex calculations, high costs, and high ...

(CNN) — It was just like watching television. Huddled in a draped-off room at Mar-a-Lago around screens set up for his viewing pleasure — including, according to photos released by the White House, a ...

(CNN) — It was just like watching television. Huddled in a draped-off room at Mar-a-Lago around screens set up for his viewing pleasure — including, according to photos released by the White House, a ...

A PyTorch-based CRNN (CNN + BiLSTM + Attention) model for predicting stock price movements. The model learns patterns from S&P 500 stocks using technical indicators, candlestick patterns, and external ...

Abstract: Building extraction from remote sensing images is a challenging task due to the complex structure variations of the buildings. Existing methods employ convolutional or self-attention blocks ...