Clone this repository to your local machine or cloud service using the following command: To provide a comprehensive overview of the dataset generation process used in your project, it's important to ...

1 Department of Computer Science and Informatics, University of Nairobi, Nairobi, Kenya. 2 Department of Computer Science, Mountains of the Moon University, Fort Portal, Uganda. Magnetic Resonance ...

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...

Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...

A group of scientists led by researchers from the University of New South Wales (UNSW) in Australia has developed a novel deep-learning method for denoising outdoor electroluminescence (EL) images of ...

X-ray computed tomography (CT) is widely used in clinical practice for screening and diagnosing patients, as it enables the acquisition of high-resolution images of internal tissues and organs in a ...

Abstract: This study uses a hybrid deep learning technique to classify asphalt, pavement, and unpaved roads. In real-world circumstances, image data noise can damage image categorization algorithms.

Abstract: Through deep learning Autoencoder Decoders, it is possible to clean noisy or damaged image data received from satellites. Two models with a PSNR of 25.6 dB and 25.54 dB were generated using ...

N:M sparsity is becoming increasingly popular for its potential to deliver high model accuracy and computational efficiency for deep learning. However, the real-world benefit of N:M sparsity is ...