Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

Abstract Machine learning plays an important role in accelerating the discovery and design
process for novel electrochemical energy storage materials. This review aims to provide the …

In recent years, graph neural networks (GNNs) have been successfully applied in many
fields due to their characteristics of neighborhood aggregation and have achieved state-of …

This work provides a data-oriented overview of the rapidly growing research field covering
machine learning (ML) applied to predicting electrochemical corrosion. Our main aim was to …

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

During the last decade, deep neural networks have shown a great performance in many
machine learning tasks such as classification and clustering. One of the most successful …

In the process of production, automobile steel forgings are prone to various cracks, which
affect the product quality. At present, forgings defects are mainly detected by fluorescent …

Deep learning has been proved to be one of the most effective method in feature encoding
for different intelligent applications such as video-based human action recognition …

Evolutionary neural networks (ENNs) are an adaptive approach that combines the adaptive
mechanism of Evolutionary algorithms (EAs) with the learning mechanism of Artificial Neural …