Abstract: Accurately detecting human attention levels is a key challenge in cognitive neuroscience, with broad application value in improving productivity. Although Electroencephalography (EEG) ...
Abstract: Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter ...
Abstract: Aerial image classification plays a vital role in applications such as building footprint extraction, water/soil analysis, 3D reconstruction. Accurate classification enables timely ...
Abstract: In recent years, uncrewed aerial vehicle (UAV) technology has shown great potential for application in hyperspectral image (HSI) classification tasks due to its advantages of flexible ...
Abstract: As hyperspectral images (HSIs) continue to increase in data resolution and information richness, current deep learning models need to enhance their feature extraction and understanding ...
Abstract: Eye diseases represent a critical global health concern, affecting approximately 2.2 billion individuals with visual impairments or blindness and underscoring the urgent need for accessible ...
Abstract: Chronic total occlusion (CTO) is a critical determinant of treatment efficacy in coronary artery disease, but its accurate diagnosis remains heavily reliant on the expertise of experienced ...
(CNN) — State Rep. James Talarico will win the Democratic primary for US Senate in Texas, CNN’s Decision Desk projects, placing a once little-known state legislator at the top of the party’s ticket ...
Pharr, Texas (CNN) — Tejano singer Bobby Pulido will win the Democratic primary for Texas’ 15th Congressional District, CNN projects, defeating emergency room physician Ada Cuellar. Pulido will face ...
Abstract: The Land Use (LU) classification of remote sensing (RS) images has broad applications in various fields. In recent years, hybrid CNN-Transformer models have been widely applied to the LU ...
Abstract: Hyperspectral image (HSI) classification presents inherent challenges due to high spectral dimensionality, significant domain shifts, and limited availability of labeled data. To address ...