A new study developed a snore-source classification model that uses STFT spectrograms, pretrained CNN features, and an L2-regularized SVM to identify where snoring originates in the upper airway.

Deep learning techniques have been successfully applied to object classification in Synthetic Aperture Radar (SAR) images, achieving remarkable performance. However, the current Transformer ...

For decades, clinical classification systems have been central to the assessment of pattern hair loss, providing a shared framework for diagnosis and communication. Foundational scales, such as ...

Abstract: This study systematically compares the performance of three attention mechanisms, SENet, ECANet and CBAM, to improve the ResNet50 model for 100 types of motion recognition tasks. Through ...

This repository contains Python notebooks demonstrating image classification using Azure AutoML for Images. These notebooks provide practical examples of building computer vision models for various ...

The project titled "Medical Image Classification for Disease Diagnosis Using Convolutional Neural Networks" aims to develop a robust and accurate machine learning model for the automatic ...

Abstract: This article is concerned with the rapid classification issue for dynamical patterns consisting of sampling sequences in a relatively large-scale dynamical dataset constructed by benchmark ...

Practicing these movements can make everyday tasks — like carrying groceries and walking up stairs — easier. Practicing these movements can make everyday tasks — like carrying groceries and walking up ...