Abstract: In hyperspectral image (HSI) classification, Transformer and CNN are widely used because they complement each other in extracting features. Nevertheless, existing Transformer-based methods ...
Abstract: With the advancement of autonomous driving technologies, passengers increasingly engage in non-driving activities. However, these activities are often limited by motion sickness (MS), which ...
Abstract: In remote sensing (RS), convolutional neural networks (CNNs) are well-recognized for their spatial–spectral feature extraction capabilities, whereas vision transformers (ViTs), which ...
Abstract: Timely and accurate identification of plant diseases is essential for sustainable agricultural practices and food security. This study presents a deep learning-based diagnostic framework ...
Abstract: As skin diseases continue to emerge worldwide, there is a growing need for fast and accurate diagnosis. However, access to dermatologists remains limited, especially in remote and ...
Abstract: Conventional standalone approaches for diagnosing individual diseases often fail to achieve robust generalization because they are severely impacted by overfitting. This results in poor ...