Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

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 …

As big data becomes ubiquitous across domains, and more and more stakeholders aspire to
make the most of their data, demand for machine learning tools has spurred researchers to …

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 …

Machine learning has evolved into an enabling technology for a wide range of highly
successful applications. The potential for this success to continue and accelerate has placed …

We explore trust in a relatively new area of data science: Automated Machine Learning
(AutoML). In AutoML, AI methods are used to generate and optimize machine learning …

In the past decade, a substantial increase in medical data from various sources, including
wearable sensors, medical imaging, personal health records, and public health …

“AI as a Service”(AIaaS) is a rapidly growing market, offering various plug-and-play AI
services and tools. AIaaS enables its customers (users)—who may lack the expertise, data …

As the field of automated machine learning (AutoML) advances, it becomes increasingly
important to incorporate domain knowledge into these systems. We present an approach for …

Feature transformation aims to generate new pattern-discriminative feature space from
original features to improve downstream machine learning (ML) task performances …