The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

Abstract Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL)
research for their architectural advantages. CNN relies heavily on hyperparameter …

Deep neural networks (DNNs), which are extensions of artificial neural networks, can learn
higher levels of feature hierarchy established by lower level features by transforming the raw …

Deep learning (DL) has become an important machine learning approach that has been
widely successful in many applications. Currently, DL is one of the best methods of …

Automated construction of deep neural networks (DNNs) has become a research hot spot
nowadays because DNN's performance is heavily influenced by its architecture and …

Nature-inspired algorithms, especially those based on swarm intelligence, have been
successfully applied to solve a variety of optimization problems in real-world applications …

Deep neural networks have been widely fostered throughout the last years, primarily on
account of their outstanding performance in various tasks, such as objects, images, faces …

The present study proposes a new efficient methodology for optimal model identification of
the Proton-exchange membrane fuel cell (PEMFC) stacks based on an improved version of …

Much has been said about the fusion of bio-inspired optimization algorithms and Deep
Learning models for several purposes: from the discovery of network topologies and …

This article investigates the negative effect of land use and climate changes on water
resources by the SWAT and SWAT‐DEEP∖ LMSFO model. Due to the importance of runoff …