Fractional Fourier Transform and Transferred CNN Based on Tensor for Hyperspectral Anomaly Detection
Abstract: Most of the algorithms for hyperspectral anomaly detection (AD) are based on the original spectral signatures which may suffer noise contamination. In recent years, some AD algorithms based ...
Abstract: Anomaly detection modeled as a one-class classification is an essential task for tool condition monitoring (TCM) when only the normal data are available. To confront with the real-world ...
In this thesis we propose a new form of Variational Autoencoder called the Conditional Latent Space Variational Autoencoder or CL-VAE. By conditioning on a known label in a dataset we can decide what ...
Now an IChemE‑approved course. Participants will be introduced to real‑world process data challenges and how to solve them with Industrial AI and data science. You will learn how GenAI tools (ChatGPT, ...
Tech Xplore on MSN
Brain-inspired hardware brings faster, lower-power anomaly detection to AI systems
The brain's cerebellum doesn't waste energy analyzing every moment. Instead, it constantly monitors the world for the ...
These short anomaly-detection puzzles are designed to illustrate how reasoning often depends on identifying inconsistencies ...
Research-grade hybrid malware detection system combining Random Forest, XGBoost, and a Deep Neural Network in a weighted ensemble, with an Autoencoder for zero-day anomaly detection — featuring SHAP ...
Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills ...
Most existing PCA-based intrusion detection techniques focus on dimensionality reduction or general anomaly detection instead of constructing reconstruction manifolds directly from the available ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results