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Machine learning for anomaly detection is crucial in identifying unusual patterns or outliers within data. It plays a vital role in cybersecurity, finance, healthcare, and industrial monitoring.
An unsupervised autoencoder approach achieves moderate success for anomaly detection (accuracy = 0.881) but struggles with recall (0.070). These findings highlight the trade-off between detection ...
In this research, we propose a novel hybrid approach that combines rule-based and machine learning techniques to enhance the interpretability of anomaly detection systems. Our method integrates a rule ...
Most of the AI anomaly-detection use cases are typically on edge AI applications. Anomalies need to be quickly detected, and then identify the cause and report it accordingly to take appropriate ...
In recent years, the widespread use of the internet across various sectors has led to an increase in network traffic and a rise in the complexity of traffic analysis. There are a number of techniques ...
The goal of the Patterns and Anomalies pattern of AI is to use machine learning and other cognitive approaches to learn patterns in the data and discover higher order connections between that data ...
Anomaly detection is one of the more difficult and underserved operational areas in the asset-servicing sector of financial institutions. Broadly speaking, a true anomaly is one that deviates from ...