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Scientific Research Publishing3h
Machine Learning-Based Outlier Detection in Long-Term Climate Data: Evidence from Burkina Faso’s Synoptic Network ()
Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...
IEEE18h
VG-Net: Sensor Time Series Anomaly Detection with Joint Variational Autoencoder and Graph Neural Network - IEEE Xplore
Intelligent Cyber-Physical Systems typically utilize sensors to gather a significant amount of raw data which often possesses temporal and high-dimensional characteristics. Ensuring the data ...
Techopedia4d
Can AI Models Blackmail Humans? New Study Reveals the Risk
A new study shows AI models like Claude and Gemini may use blackmail and deception when threatened. Discover what agentic ...
Scientific Research Publishing22d
Hajjouz, S. and Avksentieva, N. (2022) Autoencoder-Based Anomaly Detection for IoT DDoS Attack Identification. Journal of Network Security, 24, 512-525.
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 ...
IEEE23d
A Review of Neural Networks for Anomaly Detection
Anomaly detection is a critical issue across several academic fields and real-world applications. Artificial neural networks have been proposed to detect anomalies from different input types, but ...
C&EN26d
Noise-Consistent Hypergraph Autoencoder Based on Contrastive Learning for Cancer ceRNA Association Prediction in Complex Biological Regulatory Networks - ACS Publications
Competitive endogenous RNA (ceRNA) regulatory networks (CENA) have advanced our understanding of noncoding RNAs’ roles in complex diseases, providing a theoretical basis for disease mechanisms.