The application of machine learning (ML) techniques in the lithium battery field is relatively
new and holds great potential for discovering new materials, optimizing electrochemical …

Medical time series data are indispensable in healthcare, providing critical insights for
disease diagnosis, treatment planning, and patient management. The exponential growth in …

Structured sequences are popularly used to describe graph data with time-evolving node
features and edges. A typical real-world scenario of structured sequences is that unknown …

In modern information systems, real-time streaming data are generated in various vertical
application scenarios, such as industrial security cameras, household intelligent devices …

Little is known about how big data analytics affects decision-making and how choices have
an impact on organizational performance. The research model presented in this study …

Recently, the question of adversarially robust streaming, where the stream is allowed to
depend on the randomness of the streaming algorithm, has gained a lot of attention. In this …

The demand for more secure, available, reliable, and fast networks emerges in a more
interconnected society. In this context, 5G networks aim to transform how we communicate …

Edge computing enables efficient data aggregation for services like data sharing and
analysis in distributed IoT applications. However, uploading dynamic high-dimensional data …

Anomaly detection in distributed systems such as High-Performance Computing (HPC)
clusters is vital for early fault detection, performance optimisation, security monitoring …

Addressing the challenges of irregularity and concept drift in streaming time series is crucial
in real-world predictive modelling. Previous studies in time series continual learning often …