Human activity recognition (HAR) is crucial to infer the activities of human beings, and to
provide support in various aspects such as monitoring, alerting, and security. Distinct …

In the rapidly evolving domain of machine learning, the ability to adapt to unforeseen
circumstances and novel data types is of paramount importance. The deployment of Artificial …

As the profit and safety requirements become higher and higher, it is more and more
necessary to realize an advanced intelligent analysis for abnormity forecast of the …

The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is
gaining popularity due to its ability to enhance AIS performance, but the existing summaries …

In many real-world applications, data are continuously accumulated within open
environments. For instance, in disease diagnosis, the prevalence of diseases can vary …

Since the classification methods mentioned in previous studies are currently unable to meet
the accuracy requirements for fault diagnosis in large-scale chemical industries, these …

In the network environment, data from various industries is dynamic and large-scale.
Traditional clustering algorithms struggle to effectively utilize existing clustering results when …

Few-shot class-incremental learning (FSCIL) aims to continually learn novel data with
limited samples. One of the major challenges is the catastrophic forgetting problem of old …

Streaming data mining can be applied in many practical applications, such as social media,
market analysis, and sensor networks. Most previous efforts assume that all training …

Abstract Typically, Deep Neural Networks (DNNs) are not responsive to changing data.
Novel classes will be incorrectly labelled as a class on which the network was previously …