Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Quality inspection and defect detection remain critical challenges across diverse industrial
applications. Driven by advancements in Deep Learning, Convolutional Neural Networks …

The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) is greatly
improved by an early and accurate diagnosis. Several studies have created automated …

In recent years different types of Residual Neural Networks (ResNets, for short) have been
introduced to improve the performance of deep Convolutional Neural Networks. To cope …

Clinical image processing plays a significant role in healthcare systems and is a widely used
methodology of the current era. The Intracranial tumor affects children and adults as it is the …

The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …

The field of deep learning has witnessed significant progress, particularly in computer vision
(CV), natural language processing (NLP), and speech. The use of large-scale models …

The prevalence of computer vision systems necessitates hardware-based approaches to
relieve the high computational demand of deep neural networks in resource-limited …

Recent advancements in machine learning achieved by Deep Neural Networks (DNNs)
have been significant. While demonstrating high accuracy, DNNs are associated with a …