A team of scientists in the United States has combined both spatial and temporal attention mechanisms to develop a new approach for PV inverter fault detection. Training the new method on a dataset ...

Here we proposed a hybrid neural network (Hybrid-NN) as a novel scheme to improve the detection performance in terms of validation accuracy and required training data amount. The idea is to insert ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

The neural net “employs a feedforward neural network with a precisely calibrated 4-60-12 architecture and sigmoid activation functions.” This leads to an approximate 85% accuracy being able to ...

By using a custom loss function that penalizes low predictions more than high predictions, you can coerce the network to make high predictions to a specified quantile value, such as 90th percentile.

Using a data set previously taken from the traffic with images categorized into 2 classes: with pits and without pits, to be used in training using the Matlab Deep Network Designer.Using different ...

• Artificial Intelligence • In this project we aim to train an artificial neural network to approximate a function of a discrete dynamical system.

A liquid neural network (LNN) is a neural network built with a dynamic architecture of neurons that can process time-series data. Read on.

Here’s an unusual concept: a computer-guided mechanical neural network (video, embedded below.) Why would one want a mechanical neural network? It’s essentially a tool to explore what i… ...