Auroral image classification has long been a focus of research in auroral physics. However, current methods for automatic auroral classification typically assume that only one type of aurora is ...

As one of the core tasks in vision recognition, image classification is widely used in various scenarios. Most existing mainstream image classification models use the Convolutional Neural Network (CNN ...

Welcome to Learn with Jay – your go-to channel for mastering new skills and boosting your knowledge! Whether it’s personal development, professional growth, or practical tips, Jay’s got you ...

While the ViT outperformed the CNN-based ResNet50 in lung cancer classification based on cross-entropy values, the performance differences were minor and may not hold clinical significance. Therefore, ...

Project Overview This project focuses on building and training a Convolutional Neural Network (CNN) for image classification using the MNIST dataset. The MNIST dataset consists of 70,000 grayscale ...

This work proposes a methodology for the design of a classification model using transfer learning and replacing the final layers by other alternative classifiers instead of using a set of ...

This study investigated the RP performance for EEG, fNIRS, and hybrid EEG-fNIRS within a deep convolutional neural network and a long short-term memory (CNN-LSTM) model for neuroimaging brain data for ...

Dr. James McCaffrey of Microsoft Research details the 'Hello World' of image classification: a convolutional neural network (CNN) applied to the MNIST digits dataset.