PWPAE-Concept-Drift-Detection-and-Adaptation and OASW-Concept-Drift-Detection-and-Adaptation
These are ecosystem siblings—both implement complementary concept drift detection approaches (PWPAE uses an ensemble framework while OASW uses a lightweight adaptation framework) for the same problem domain of streaming data, allowing practitioners to choose the method best suited to their computational constraints and use case.
About PWPAE-Concept-Drift-Detection-and-Adaptation
Western-OC2-Lab/PWPAE-Concept-Drift-Detection-and-Adaptation
Data stream analytics: Implement online learning methods to address concept drift and model drift in data streams using the River library. Code for the paper entitled "PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams" published in IEEE GlobeCom 2021.
This project helps operations engineers, security analysts, and researchers working with real-time IoT data streams maintain accurate analytics models. It takes continuous streams of sensor data or network traffic and applies online learning methods to automatically detect when the underlying data patterns change (concept drift). The output is an adapted model that remains effective even as the IoT environment evolves.
About OASW-Concept-Drift-Detection-and-Adaptation
Western-OC2-Lab/OASW-Concept-Drift-Detection-and-Adaptation
An online learning method used to address concept drift and model drift. Code for the paper entitled "A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams" published in IEEE Internet of Things Magazine.
This project helps operations engineers and security analysts maintain accurate anomaly detection in IoT systems. It takes in live streams of IoT sensor data, such as network traffic, and automatically adjusts its predictive models. The output is a continuously updated anomaly detection system that remains effective even as data patterns change over time.
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