A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques
Last decade demonstrate the massive growth in organizational data which keeps on
increasing multi‐fold as millions of records get updated every second. Handling such vast …
increasing multi‐fold as millions of records get updated every second. Handling such vast …
[HTML][HTML] TWIN-ADAPT: Continuous Learning for Digital Twin-Enabled Online Anomaly Classification in IoT-Driven Smart Labs
In the rapidly evolving landscape of scientific semiconductor laboratories (commonly known
as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical …
as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical …
A comprehensive analysis of concept drift locality in data streams
Adapting to drifting data streams is a significant challenge in online learning. Concept drift
must be detected for effective model adaptation to evolving data properties. Concept drift …
must be detected for effective model adaptation to evolving data properties. Concept drift …