IEEE

Multi-Drone Path Planning Using Gradient Descent and Potential Fields For Warehouse Applications

Abstract: This study presents an approach for multi-drone path planning in warehouse environments using a combination of the Gradient Descent Method and Artificial Potential Fields (APF). The ...
Frontiers

Evaluating machine learning models for stroke prediction based on clinical variables

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 ...
IEEE

On the Convergence of (Stochastic) Gradient Descent for Kolmogorov–Arnold Networks

Abstract: Kolmogorov–Arnold Networks (KANs), a recently proposed neural network architecture, have gained significant attention in the deep learning community, due to their potential as a viable ...
GitHub

OG-HFYOLO :Orientation Gradient Guidance and Heterogeneous Feature Fusion For Deformation Table Cell Instance Segmentation

The data generator mentioned in the paper is specifically implemented in ./dataprocess/data_gen.py, and detailed usage instructions are provided at the beginning of ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
Frontiers

Sliding down the socioeconomic health gradient of COVID-19 in New York City: multinomial regression analyses of disproportionate financial hardship for Black, Latino, and Asian ...

Background: Distinct socioeconomic gradients in COVID-19 outcomes were observed across the United States, so an evaluation of individual resident characteristics related to economic deprivation (race ...