The ability to detect single photons (the smallest energy packets constituting electromagnetic radiation) in the infrared range has become a pressing need across numerous fields, from medical imaging ...

Learn what VGG networks are, how they work, and why they’re important in deep learning and computer vision. #DeepLearning #VGG #MachineLearning ...

First, the difference image (DI) of the bitemporal hyperspectral images (Bi-HSIs) is obtained through pixel-by-pixel and band-by-band subtraction operations. Then, the LRSR model is designed as a deep ...

The mixed pixel problem, arising from the complex vegetation types of peatlands, poses a significant challenge for remote sensing-based peatland mapping. A convolution and transformer-based ...

The Vera C. Rubin Observatory will make the study of stars and galaxies more like the big data-sorting exercises of contemporary genetics and particle physics.

Using an accessible deep learning (DL) system to classify smartphone images of skin lesions may improve early detection of skin cancer, according to researchers of an experimental study published ...

KEYWORDS: Image Classification, Unsupervised Feature Learning, Auto-Encoder JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.3, March 28, 2025 ABSTRACT: Convolutional auto-encoders have ...

In this paper, we developed a deep-learning model called ASD-SAENet for classifying patients with ASD from typical control subjects using fMRI data. We designed and implemented a sparse autoencoder ...

In the work of Zhang et al. (2017), a deep learning framework consisting of the sparse autoencoder (SAE) and logistic regression was used to classify EEG emotion status. The sparse autoencoder was ...

In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor depth completion, ...