Recently, contrastive learning (CL) is a promising way of learning discriminative
representations from time series data. In the representation hierarchy, semantic information …

This article proposes an efficient federated distillation learning system (EFDLS) for multitask
time series classification (TSC). EFDLS consists of a central server and multiple mobile …

Over the years, a number of semisupervised deep‐learning algorithms have been proposed
for time‐series classification (TSC). In semisupervised deep learning, from the point of view …

Time series data usually contains local and global patterns. Most of the existing feature
networks focus on local features rather than the relationships among them. The latter is also …

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted
increasingly more research attention. The performance of a DL-based MTSC algorithm is …

Multivariate time series classification has become popular due to its prevalence in many real-
world applications. However, most state-of-the-art focuses on improving classification …

In recent years, time series classification methods based on shapelet features have attracted
significant research interest because they are interpretable. Although researchers have …

Over the past few decades, harmful algal blooms (HABs) have occurred frequently
worldwide. The application of harmful algal bloom detection when based solely on water …

A major challenge in ecological risk assessment is estimating chemical-induced effects
across taxa without species-specific testing. Where ecotoxicological data may be more …

Subject matter experts (SMEs) often rely on attribute importance rankings to verify machine
learning models, acquire insights into their outcomes, and gain a deeper understanding of …