Fine-grained image classification tasks face challenges such as difficulty in labeling, scarcity of samples, and small category differences. To address this problem, this study proposes a novel ...

In previous research, we applied classification- integrated moving averages (CIMA) to increase the correlation between features in time-series data and their classification labels. The problem is ...

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, ...

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 images of ...

Machine learning and deep learning, as one of the most prominent fields of today are quickly improving many aspects of our life. One of the categories that provides strongest results in resolving real ...

The classification accuracies achieved with our proposed methodology were the highest compared to other classification methods for four-class EEG-based BCI and other studies on the same dataset 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.

The objective of this section is to develop a Convolutional Neural Network (CNN) to classify hand-written digits using the widely used MNIST data set. The MNIST handwritten digit classification ...