Abstract: Dynamic image degradations, including noise, blur and lighting inconsistencies, pose significant challenges in image restoration, often due to sensor limitations or adverse environmental ...

The third-ranking leader in the House of Representatives, who also happens to hail from Minnesota, demanded answers from Gov. Tim Walz after a YouTuber tried to confront employees of an alleged ...

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...

Introduction: Food price volatility continues to be a significant concern in Kenya's economic development, posing challenges to the country's economic stability. Methodology: This study examines the ...

This repository explores the concept of Orthogonal Gradient Descent (OGD) as a method to mitigate catastrophic forgetting in deep neural networks during continual learning scenarios. Catastrophic ...

Introduction: Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as ...

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as to ...