ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection

ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection

Anomaly detection, crucial for identifying issues such as financial fraud or medical malfunctions, has advanced significantly with machine learning (ML) and deep learning (DL). However, a major problem in the field is that no single model works best with diverse datasets and problem domains. To addr...

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Bibliographic Details
Main Authors: Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Saad Alqahtany, Juan M. Corchado
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Anomaly detection
deep learning
financial fraud detection
healthcare anomalies
machine learning
meta-features
Online Access:https://ieeexplore.ieee.org/document/10819404/

Internet

https://ieeexplore.ieee.org/document/10819404/

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