The ongoing advancements in network architecture design have led to remarkable
achievements in deep learning across various challenging computer vision tasks …

Transformer-based Deep Neural Network architectures have gained tremendous interest
due to their effectiveness in various applications across Natural Language Processing (NLP) …

Convolutional neural networks (CNN) are highly effective for image classification and
computer vision activities. The accuracy of CNN architecture depends on the design and …

Neural Architecture Search (NAS), a promising and fast-moving research field, aims to
automate the architectural design of Deep Neural Networks (DNNs) to achieve better …

Neural architecture search (NAS) aims to automate architecture engineering in neural
networks. This often requires a high computational overhead to evaluate a number of …

In recent years, deep learning has been applied to a variety of scenarios in Industrial
Internet of Things (IIoT), including enhancing the security of IIoT. However, the existing deep …

The construction of machine learning models involves many bi-level multiobjective
optimization problems (BL-MOPs), where upper-level (UL) candidate solutions must be …

Deep neural networks have been found vulnerable to adversarial attacks, thus raising
potential concerns in security-sensitive contexts. To address this problem, recent research …

Distributed deep learning is a promising approach for training and deploying large and
complex deep learning models. This paper presents a comprehensive workflow for …

Recently, Deep Learning (DL) algorithms have shown state-of-the-art performance in
numerous tasks. The design of DL algorithms is time-consuming process that requires …