Over the past few years, larger and deeper neural network models, particularly convolutional
neural networks (CNNs), have consistently advanced state-of-the-art performance across …

The enormous data and computational resources required by Convolutional Neural
Networks (CNNs) hinder the practical application on mobile devices. To solve this restrictive …

Feature Selection (FS), a ubiquitous technique for mitigating data dimensionality, has
garnered extensive adoption within the realm of machine learning. Conventionally …

High-quality printing is a longstanding objective in the printing and replication industry.
However, the methods used to evaluate print quality suffer from subjectivity and …

Abstract Deep Convolutional Neural Networks (CNNs), continue to demonstrate remarkable
performance across various tasks. However, their computational demands and energy …

Abstract Trained Artificial Intelligence (AI) models are challenging to install on edge devices
as they are low in memory and computational power. Pruned AI (PAI) models are therefore …

To reduce computational overhead while maintaining model performance, model pruning
techniques have been proposed. Among these, structured pruning, which removes entire …

Convolutional neural networks (CNNs) have been successfully implemented in various
computer vision tasks. However, the remarkable achievements are accompanied by high …

Traditional wireless image transmission methods struggle to balance rate efficiency and
reconstruction quality under varying channel conditions. To address these challenges, we …

In deep learning, the size and complexity of neural networks have rapidly increased to
achieve higher performance. However, this poses a challenge when utilized in resource …