Existing algorithms can partially reconstruct the shape of a single tree from a clean point-cloud dataset acquired by ...

Deep learning has become a transformative technology for modern weed detection, offering significant advantages over traditional machine vision in robustness, scalability, and recognition accuracy.

Background Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower economic status who present at safety-net ...

This repository provides code and workflows to test several state-of-the-art vehicle detection deep learning algorithms —including YOLOX, SalsaNext, RandLA-Net, and VoxelRCNN— on a Flash Lidar dataset ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...

Abstract: Landslides are among the most common natural disasters globally, posing significant threats to human society. In recent years, deep learning (DL) has been widely applied to rapid landslide ...

aMedical Big Data Research Center, Chinese The People’s Liberation Army General Hospital, Beijing, China bNational Engineering Research Center of Medical Big Data Application Technology, The People’s ...

1 Ambam Computer Science and Application Laboratory & Department of Computer Engineering, Higher Institute of Transport, Logistics and Commerce, University of Ebolowa, Ebolowa, Cameroon. 2 Institut ...

This repository contains the Python code for the Master's Thesis: "Uncertainty Quantification for Deep Learning in Sleep Apnea Detection: A Comparative Evaluation of Monte Carlo Dropout and Deep ...

Accurate and timely detection of diabetic retinopathy (DR) is crucial for managing its progression and improving patient outcomes. However, developing algorithms to analyze complex fundus images ...