A variety of methods have been applied to the architectural configuration and learning or
training of artificial deep neural networks (DNN). These methods play a crucial role in the …

The COVID-19 pandemic has changed the way we practice medicine. Cancer patient and
obstetric care landscapes have been distorted. Delaying cancer diagnosis or maternal-fetal …

Anomaly detection in high dimensional data is a critical research issue with serious
implication in the real-world problems. Many issues in this field still unsolved, so several …

Most of the neural network architectures are based on human experience, which requires a
long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect …

This paper highlights the contribution of utilizing ensemble deep learning with auto-
encoders (AEs) for out-of-distribution data detection. The key innovation is treating …

Many of the most important questions being tackled by researchers today require large
amounts of both data and computational resources. We are not limited in access to the …