The increasing availability of healthcare data requires accurate analysis of disease
diagnosis, progression, and real-time monitoring to provide improved treatments to the …

The discovery of adversarial examples has raised concerns about the practical deployment
of deep learning systems. In this paper, we demonstrate that adversarial examples are …

Medical IoT devices are rapidly becoming part of management ecosystems for pandemics
such as COVID-19. Existing research shows that deep learning (DL) algorithms have been …

In the past years, deep neural networks (DNN) have become popular in many disciplines
such as computer vision (CV), natural language processing (NLP), etc. The evolution of …

Convolutional Neural Networks (CNNs) have advanced existing medical systems for
automatic disease diagnosis. However, a threat to these systems arises that adversarial …

Machine learning algorithms are widely utilized in cybersecurity. However, recent studies
show that machine learning algorithms are vulnerable to adversarial examples. This poses …

Deep neural networks (DNNs) have become popular for medical image analysis tasks like
cancer diagnosis and lesion detection. However, a recent study demonstrates that medical …

Although deep learning systems trained on medical images have shown state-of-the-art
performance in many clinical prediction tasks, recent studies demonstrate that these systems …

As the prevalence and everyday use of machine learning algorithms, along with our reliance
on these algorithms grow dramatically, so do the efforts to attack and undermine these …

Machine learning has witnessed tremendous growth in its adoption and advancement in the
last decade. The evolution of machine learning from traditional algorithms to modern deep …