Nature

A multilayer deep autoencoder approach for cross layer IoT attack detection using deep learning algorithms

IoT networks’ intrinsic complexity and variety make cross-layer-based IoT threat detection vital for defending IoT ecosystems. Security techniques that use a cross-layer approach might identify ...
Nature

An autoencoder driven deep learning geospatial approach to flood vulnerability analysis in the upper and middle basin of river Damodar

Flood vulnerability mapping has significantly progressed with the advent of Machine Learning (ML), bringing greater certainty to predictions. However, conventional supervised ML techniques may not be ...
Phys.org

A deep variational autoencoder for proteomics mass spectrometry data analysis

Jianwei Shuai's team and Jiahuai Han's team at Xiamen University have developed a deep autoencoder-based data-independent acquisition data analysis software for protein mass spectrometry, which ...
Visual Studio Magazine

Data Anomaly Detection Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...