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 ...

Recent studies have shown that the deep domain adaptation (DA) technique has achieved remarkable results in cross-domain hyperspectral image (HSI) classification task. However, these DA methods assume ...

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 ...

Accurate classification of otoscopic ear images is crucial for early diagnosis of ear pathologies such as Chronic Otitis Media, Earwax Plug, and Myringosclerosis. In this study, we propose a novel ...

Irene Gilbert is a 76-year-old retired state employee on a mission, fighting energy projects like large wind farms in Oregon’s rural communities. Renewable energy advocates and lawmakers treat ...

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 ...

Microsoft Excel now lets you run Python scripts on images to detect sharpness, edit visuals, and analyze metadata.

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

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

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 ...