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 ...

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.

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

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 ...

Abstract: This research addresses the challenge of camera calibration and distortion parameter prediction from a single image using deep learning models. The main contributions of this work are: (1) ...

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

Canadians spend around 90% of their time indoors; consequently, their home and building quality has a definitive impact on their health outcomes. Poor quality housing—characterized by inadequate ...

Extreme weather events such as heatwaves, cyclones, floods, wildfires, and droughts are becoming more frequent due to climate change. Climate change causes shifts in biodiversity and impacts ...