Objective This work aims to provide a review of the existing literature in the field of
automated machine learning (AutoML) to help healthcare professionals better utilize …

Time-series forecasting is a significant discipline of data modeling where past observations
of the same variable are analyzed to predict the future values of the time series. Its …

Abstract Machine learning (ML) has become a vital part in many aspects of our daily life.
However, building well performing machine learning applications requires highly …

Artificial intelligence (AI) is increasingly employed in health care fields such as oncology,
radiology, and dermatology. However, the use of AI in mental health care and …

In recent years, an active field of research has developed around automated machine
learning (AutoML). Unfortunately, comparing different AutoML systems is hard and often …

Comparing different AutoML frameworks is notoriously challenging and often done
incorrectly. We introduce an open and extensible benchmark that follows best practices and …

The realization that AI-driven decision-making is indispensable in today's fast-paced and
ultra-competitive marketplace has raised interest in industrial machine learning (ML) …

We perform an in-depth, systematic benchmarking study and evaluation of phishing features
on diverse and extensive datasets. We propose a new taxonomy of features based on the …

Automated machine learning (AutoML) supports the algorithmic construction and data-
specific customization of machine learning pipelines, including the selection, combination …

Convolutional models have provided outstanding performance in the analysis of
hyperspectral images (HSIs). These architectures are carefully designed to extract intricate …