Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts! # ...

Hyperspectral image (HSI) classification involves assigning unique labels to each pixel to identify various land cover categories. While deep classifiers have achieved high predictive accuracy in this ...

As a new optical machine learning framework, the diffractive deep neural network (D2NN) has attracted much attention due to its advantages such as low power consumption, parallel computing, and fast ...

This paper investigates uncertainty quantification (UQ) techniques in multi-class classification of chest X-ray images (COVID-19, Pneumonia, and Normal). We ...

This repository contains code for a binary image classification model to detect pneumothorax using the ResNet-50 V2 architecture. It includes essential steps such as dataset splitting, image ...

CIFAR-10 problems analyze crude 32 x 32 color images to predict which of 10 classes the image is. Here, Dr. James McCaffrey of Microsoft Research shows how to create a PyTorch image classification ...

The classification metrics for the prediction of PH and GY using MLP or CNN are presented in Table 2 and Supplementary Table S1. Under each prediction scenario, the loss increased from inner training ...