Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …

Abstract Machine learning is increasingly used in mental health research and has the
potential to advance our understanding of how to characterize, predict, and treat mental …

Over the last decades, the natural disturbance is increasingly putting pressure on European
forests. Shifts in disturbance regimes may compromise forest functioning and the continuous …

Abstract Machine learning and artificial intelligence (ML/AI), previously considered black box
approaches, are becoming more interpretable, as a result of the recent advances in …

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic
options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and …

We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …

Species distribution models (SDMs) constitute the most common class of models across
ecology, evolution and conservation. The advent of ready‐to‐use software packages and …

Artificial intelligence has been applied in wildfire science and management since the 1990s,
with early applications including neural networks and expert systems. Since then, the field …

Intensifying wildfire activity and climate change can drive rapid forest compositional shifts. In
boreal North America, black spruce shapes forest flammability and depends on fire for …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …