Hyperspectral image classification has emerged as a transformative technology in Earth observation, providing unprecedented detail through hundreds of contiguous spectral bands. Neural network ...

Recent advances in neural network methodologies have revolutionised the analysis and classification of chromosome images, streamlining traditionally labour‐intensive processes in cytogenetics ...

An example of an image classification problem is to identify a photograph of an animal as a "dog" or "cat" or "monkey." The two most common approaches for image classification are to use a standard ...

In this paper, it is proposed to have a method for classification of flowers using Artificial Neural Network (ANN) classifier. The proposed method is based on textural features such as Gray Level ...

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

Over the past decades, computer scientists have developed many computational tools that can analyze and interpret images.

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI.

Using tools from statistical physics, Hinton's produced neural networks that can spot patterns in data, enabling them to classify images or create new examples of the patterns it was trained on.

Neuroscientists are finding that deep-learning networks, often criticized as “black boxes,” can be good models for the organization of living brains.