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 ...

Class-specific classification accuracy (%) using different methods on the Yellow River Estuary dataset (bold and underlined values indicate optimal and suboptimal indicators respectively).

However, existing research largely focuses on single-task modeling, lacking comprehensive solutions that integrate tumor segmentation with classification diagnosis. This study aims to develop a ...

This project demonstrates a deep learning approach for multi-class image classification using Convolutional Neural Networks (CNNs). The model classifies images into predefined categories such as ...

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 ...

Convolutional Neural Networks (CNNs) have achieved significant success in image classification and object detection. CNN models generally consist of a single-stream and process single image data at ...

Overview In this project, we develop a CNN architecture tailored specifically for multiclass image classification tasks. The CNN is trained on a diverse dataset comprising images of different ...

Wrapping Up When using the scikit library for multi-class classification, the main alternative to the MLPClassifier neural network module is the scikit DecisionTree module. Decision trees are useful ...