Deep learning approaches have demonstrated great achievements in the field of computer-
aided medical image analysis, improving the precision of diagnosis across a range of …

Deep learning techniques have achieved superior performance in computer-aided medical
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …

In the rapidly evolving landscape of cybersecurity, model extraction attacks pose a
significant challenge, undermining the integrity of machine learning models by enabling …

Deep learning models are widely used in healthcare systems. However, deep learning
models are vulnerable to attacks themselves. Significantly, due to the black-box nature of the …

We present an effective application of quantum machine learning in histopathological
cancer detection. The study here emphasizes two primary applications of hybrid classical …

The advancement of hardware and computing power has enabled deep learning to be used
in a variety of fields, particularly in AI medical applications in intelligent medicine and …

The healthcare data requires accurate disease detection analysis, real-time monitoring, and
advancements to ensure proper treatment for patients. Consequently, Machine Learning …

Using digital data gathering and analytics in healthcare brings benefits and risks to patients
and practitioners. Smart Health Information Systems, such as Clinical Decision Support …

Balancing the trade-off between accuracy and robustness is a long-standing challenge in
time series forecasting. While most of existing robust algorithms have achieved certain …

As artificial intelligent models continue to grow in their capacity and sophistication, they are
often trusted with very sensitive information. In the sub-field of adversarial machine learning …