Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

The adoption of machine learning (ML) and, more specifically, deep learning (DL)
applications into all major areas of our lives is underway. The development of trustworthy AI …

This paper brings deep learning at the forefront of research into time series classification
(TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of …

Most methods for time series classification that attain state-of-the-art accuracy have high
computational complexity, requiring significant training time even for smaller datasets, and …

Time series classification is a subfield of machine learning with numerous real-life
applications. Due to the temporal structure of the input data, standard machine learning …

Conventional machine learning techniques may have lesser performance when they deal
with complex data. For addressing this issue, it is important to build data mining frameworks …

Deep neural networks (DNNs) for time series classification have potential security concerns
due to their vulnerability to adversarial attacks. Previous work that perturbs time series …

In this work, we concentrate on the detection of anomalous behaviors in systems operating
in the physical world and for which it is usually not possible to have a complete set of all …

Live fuel moisture content (LFMC) is an important environmental indicator used to measure
vegetation conditions and monitor for high fire risk conditions. However, LFMC is …

Adversarial attacks represent a threat to every deep neural network. They are particularly
effective if they can perturb a given model while remaining undetectable. They have been …