Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Machine learning methods have been remarkably successful for a wide range of application
areas in the extraction of essential information from data. An exciting and relatively recent …

The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …

Machine learning on graphs is an important and ubiquitous task with applications ranging
from drug design to friendship recommendation in social networks. The primary challenge in …

Precision medicine seeks to maximize the quality of health care by individualizing the health-
care process to the uniquely evolving health status of each patient. This endeavor spans a …

Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …

Change detection (CD) in multitemporal images is an important application of remote
sensing. Recent technological evolution provided very high spatial resolution (VHR) …

Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be
robust to random perturbations of the input. However, these models are vulnerable to …

Genetic Algorithms are heuristic search approaches that are applicable to a wide range of
optimization problems. This flexibility makes them attractive for many optimization problems …