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 ...

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 ...

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 ...

Introduction: Recent advances in artificial intelligence have transformed the way we analyze complex environmental data. However, high-dimensionality, spatiotemporal variability, and heterogeneous ...

Editor’s Note: This story is a part of Peak, The Athletic’s new desk covering leadership, personal development and success through the lens of sports. Peak aims to connect readers to ideas they can ...

A few years back, one of us sat in a school district meeting where administrators and educators talked about the latest student achievement results. The news was not good. Students’ test scores hadn’t ...

Abstract: We introduce a novel structure empowered by deep learning models, accompanied by a thorough training methodology, for enhancing channel estimation and data detection in multiple input ...

Background: The integration of deep learning (DL) and time-lapse imaging technologies offers new possibilities for improving embryo assessment and selection in clinical in vitro Fertilization (IVF).

Deep learning (DL) is a type of artificial intelligence (AI) that utilizes artificial neural networks (ANNs) to process data through two or more layers, each of which can recognize complex features of ...