Machine learning has become a key driver of the digital health revolution. That comes with a
fair share of high hopes and hype. We conducted a sco** review on machine learning in …

Analysing 88 sources published from 2011 to 2021, this article presents a first systematic
review of the computer vision-based analysis of buildings and the built environment. Its aim …

Machine Learning (ML) is being used in multiple disciplines due to its powerful capability to
infer relationships within data. In particular, Software Engineering (SE) is one of those …

Introduction Machine Learning (ML) has emerged as a promising approach in healthcare,
outperforming traditional statistical techniques. However, to establish ML as a reliable tool in …

Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly
in high-intensity sports like soccer. This study used machine learning algorithms to assess …

Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming
traditional statistical techniques. However, to establish ML as a reliable tool in clinical …

The Transformer model precisely predicts soil health status from high-throughput
sequencing data. The SMOTE algorithm addresses data imbalance issues, improving model …

We leverage statistical and natural language processing (NLP) tools for a systematic
analysis of triggers of state-led mass killings. The work advances the application of statistics …

In a number of applications, data may be anonymized, obfuscated, or highly noisy. In such
cases, it is difficult to use domain knowledge or low-dimensional visualizations to engineer …

The aim of the chapter is to explain the basic concepts of Machine Learning applied to
condition monitoring in Industry 4.0. Machine learning is a common term used today in …