Frontiers

An abnormal traffic detection method for chain information management system network based on convolutional neural network

Chain information management system is widely used, providing convenience for the operation and management of enterprises. However, the problem of abnormal network traffic becomes increasingly ...
C&EN

Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Article Views are the COUNTER-compliant sum of full text article downloads since ...
Frontiers

Abnormal ECG detection based on an adversarial autoencoder

Automatic detection and alarm of abnormal electrocardiogram (ECG) events play an important role in an ECG monitor system; however, popular classification models based on supervised learning fail to ...
GitHub

Multi-Scale Temporal Variational Autoencoder for Anomaly Detection in Multivariate Time Series

MST-VAE is an unsupervised learning approach for anomaly detection in multivariate time series. Inspired by InterFusion paper, we propose a simple yet effective multi-scale convolution kernels applied ...
Visual Studio Magazine

Autoencoder Anomaly Detection Using PyTorch

Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...