Most of state of the art methods applied on time series consist of deep learning methods that
are too complex to be interpreted. This lack of interpretability is a major drawback, as several …

Lithium–ion batteries play a crucial role in clean transportation systems including EVs,
aircraft, and electric micromobilities. The design of battery cells and their production process …

The paper proposes a novel architecture for explainable artificial intelligence based on
semantic technologies and artificial intelligence. We tailor the architecture for the domain of …

Deep learning-based support systems have demonstrated encouraging results in numerous
clinical applications involving the processing of time series data. While such systems often …

Explainable artificial intelligence (XAI) has shed light on enormous applications by clarifying
why neural models make specific decisions. However, it remains challenging to measure …

Estimating causal relations is vital in understanding the complex interactions in multivariate
time series. Non-linear coupling of variables is one of the major challenges in accurate …

Several explainable AI algorithms have been proposed to help make machine learning
models more interpretable and trustworthy. However in spite of numerous methodological …

Large, varied, and time-evolving data sources can be observed across many domains and
present a unique challenge for classification problems, in which traditional machine learning …