We explore how these models enable feature extraction, anomaly detection, and classification across diverse signal types, including electrocardiograms, radar waveforms, and IoT sensor data. The review ...

💸 AI-Powered Anomaly Detection in Financial Transactions This project uses machine learning models to identify anomalous (potentially fraudulent) financial transactions in structured tabular data.

This project implements an autoencoder-based anomaly detection system that: Learns the normal patterns in network traffic data; Automatically removes highly correlated features; Detects anomalies ...

However, due to the harsh and dynamic operational environment, PRSOVs are susceptible to various anomalies, which may lead to severe flight incidents such as cabin depressurization. To this end, this ...

In time series anomaly detection (TSAD), the scarcity of labeled data poses a challenge to the development of accurate models. Unsupervised domain adaptation (UDA) offers a solution by leveraging ...

Modern cyberattacks in cyber-physical systems (CPS) rapidly evolve and cannot be deterred effectively with most current methods which focused on characterizing past threats. Adaptive anomaly detection ...

Tissue staining is a cornerstone of medical diagnostics, used to highlight cellular structures and render tissue features ...