CNN is a successful image classification that uses hierarchical feature extraction, ViTs capture the global context but require substantial data and computation. In this research, we have used ...

Transformers have been widely adopted in the field of hyperspectral image (HSI) classification. However, a significant drawback of transformers lies in their excessive number of parameters and the ...

Vision transformers (ViTs) and convolutional neural networks (CNNs) have demonstrated remarkable performance in classifying complicated hyperspectral images (HSIs). However, these models require a lot ...

Traditional brain tumor diagnosis and classification are time-consuming and heavily reliant on radiologist expertise. The ever-growing patient population generates vast data, rendering existing ...

David A. Graham discusses his book, reviewing how much Project 2025 has reshaped the U.S.

IndyCar said they were not aware of the Department of Homeland Security plan to release an AI-generated image of an IndyCar featuring ICE livery.

Classification of pathological images is the basis for automatic cancer diagnosis. Despite that deep learning methods have achieved remarkable performance, they heavily rely on labeled data, demanding ...

Hyperspectral image classification (HSIC) aims to identify land-cover categories by leveraging the spectral and spatial information contained in hyperspectral images (HSIs). Currently, many deep ...

Enhancing classification accuracy in hyperspectral image classification under small sample scenarios remains a critical challenge. This study introduces a novel framework based on Conditional ...

The executive subcommittee of the State Building Commission on Thursday approved a temporary lease for Elon Musk’s The Boring Company to use a downtown parking lot.

One-year-old Mohammed al-Matouq is becoming the face of the humanitarian crisis and information war playing out in the Gaza Strip, ...

Our method involves modeling image features as a nine-dimensional feature vector, followed by a dimensionality reduction process to identify the most influential features and dominant areas within the ...