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 …

Abstract Machine learning studies automatic algorithms that improve themselves through
experience. It is widely used for analyzing and extracting value from large biomedical data …

Recent interest in complex and computationally expensive machine learning models with
many hyperparameters, such as automated machine learning (AutoML) frameworks and …

Several variants of the long short-term memory (LSTM) architecture for recurrent neural
networks have been proposed since its inception in 1995. In recent years, these networks …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

Human activity recognition (HAR) in ubiquitous computing is beginning to adopt deep
learning to substitute for well-established analysis techniques that rely on hand-crafted …

Modern supervised machine learning algorithms involve hyperparameters that have to be
set before running them. Options for setting hyperparameters are default values from the …

Machine learning techniques have deeply rooted in our everyday life. However, since it is
knowledge-and labor-intensive to pursue good learning performance, humans are heavily …

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 …

Abstract Long Short Term Memory (LSTM) is among the most popular deep learning models
used today. It is also being applied to time series prediction which is a particularly hard …