Abstract: Flood mapping using remote sensing data is critical to disaster response, especially in real-time monitoring and edge deployment. However, existing deep-learning (DL) models often face ...

Abstract: The integrity of water quality data has an important impact on water quality prediction and analysis, so it is necessary to impute the missing values in the data. However, at present, most ...

Deep learning algorithms for ultra-widefield fundus photos can identify retinal detachments with precision, supporting early diagnoses in varied settings. Deep learning (DL) models applied to ...

Learn about DenseNet, one of the most powerful deep learning architectures, in this beginner-friendly tutorial. Understand its structure, advantages, and how it’s used in real-world AI applications.

Nothing dominates the technology news cycle more than AI in its many forms, and for data professionals, the discussion often mentions deep learning. But what are the use cases for this technology? How ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator.

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 ...

This project presents a complete workflow for cone detection in Formula Student Driverless scenarios using deep learning. It demonstrates how to use MATLAB® and Simulink® for data preparation and ...

This study aimed to develop a hybrid deep learning model for classifying multiple fundus diseases using ultra-widefield (UWF) images, thereby improving diagnostic efficiency and accuracy while ...