Convolutional Neural Networks (CNN) have shown impressive performance in computer
vision, natural language processing, and many other applications, but they exhibit high …

From tiny pacemaker chips to aircraft collision avoidance systems, the state-of-the-art Cyber-
Physical Systems (CPS) have increasingly started to rely on Deep Neural Networks (DNNs) …

Machine Learning (ML) is currently being exploited in numerous applications being one of
the most effective Artificial Intelligence (AI) technologies, used in diverse fields, such as …

Recent advances in Capsule Networks (CapsNets) have shown their superior learning
capability, compared to the traditional Convolutional Neural Networks (CNNs). However, the …

Many modern research fields increasingly rely on collecting and analysing massive, often
unstructured, and unwieldy datasets. Consequently, there is growing interest in machine …

In several machine learning (ML) based Internet of Things (IoT) systems, data are captured
by IoT devices and then transmitted over a wireless channel for remote processing. Since …

Recently, Capsule Networks (CapsNets) have shown improved performance compared to
the traditional Convolutional Neural Networks (CNNs), by encoding and preserving spatial …

Conventional recommendation systems (RSs) are typically optimized to enhance
performance metrics uniformly across all training samples. This makes it hard for data-driven …

Recent studies have shown that Machine Learning (ML) algorithm suffers from several
vulnerability threats. Among them, adversarial attacks represent one of the most critical …

Hardware accelerator-based CNN inference improves the performance and latency but
increases the time-to-market. As a result, CNN deployment on hardware is often outsourced …