Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction ... CNN-LSTM, CNN-GRU, ELM, ANN and SVR. They were tested on eight cases ...

By utilizing multi-task learning, the model can share relevant information across tasks ... The programming environment utilized Python 3.9 and PyTorch 2.1. The set of parameters for training the ...

The textbook meaning of an artificial neural network (ANN) is a deep learning model made up of neurons that emulate the structure of the human brain. These neurons are designed to mimic the way nerve ...

It is centered around a recurrent neural network (RNN) optimized through Bayesian Optimization (BO). The RNN architecture includes—among others—a feature input layer, a Long Short-Term Memory (LSTM) ...

Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...

Abstract: The steel industry, characterized by complex and costly production processes, stands to gain significantly from the integration of intelligent systems for automation. This study details the ...

A Fortran-based feed-forward neural network library. Whilst this library currently has a focus on 3D convolutional neural networks (CNNs), it can handle most standard hidden layer forms of neural ...

The increasing complexity of deep neural networks (DNN) and their proliferating applications in embedded computing ... Likewise, recurrent layers such as gated recurrent units (GRU) simultaneously ...