Key considerations for discovery in AI-focused intellectual property (IP) litigation, including an examination of a hypothetical patent infringement and trade secret misappropriation case on highly ...

Building recognition from images is a challenging task since pictures can be taken from different angles and under different illumination conditions. Most of the building recognition methods use local ...

This study integrated deep convolutional neural networks with image processing techniques to indirectly distinguish weeds from vegetables by focusing on vegetable recognition.

A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher. Artificial neurons—the fundamental building blocks of deep neural networks—have survived almost ...

Recently, deep learning has transformed machine learning by significantly enhancing its artificial intelligence as Artificial Neural Networks (ANN) have become increasingly prevalent. Due of its ...

According to the characteristics of images with coal wall water seepage, a bilinear neural network was used to extract the image features and enhance the network’s fine-grained image recognition.

A subsequent article, “ Training convolutional neural networks ” discusses how CNN models are trained. Part 3 will examine a specific use case to test the model using a dedicated AI microcontroller.

Learn how to create, train, evaluate, predict, and visualize a CNN model for image recognition and classification in Python using Keras and TensorFlow.