Anomaly Detection

Humans in conversation also look for anomalies to build a model of the interaction and its direction and value. 

Explainable AI (XAI) design for unsupervised deep anomaly detector

Interpretable prototype for detecting Out-of-Distribution Samples and Adversarial Attacks

By Ajay Arunachalam  in TowardDataScience  (Technical)

An interpretable prototype of unsupervised deep convolutional neural network & lstm autoencoders based real-time anomaly detection from high-dimensional heterogeneous/homogeneous time series multi-sensor data

Hello, friends. In this blog post, I will take you through the new features of the package “msda”. More details can be found on the GitHub page here

What’s new in MSDA v1.10.0?

MSDA is an open source low-code Multi-Sensor Data Analysis library in Python that aims to reduce the hypothesis to insights cycle time in a time-series multi-sensor data analysis & experiments. It enables users to perform end-to-end proof-of-concept experiments quickly and efficiently. The module identifies events in the multidimensional time series by capturing the variation and trend to establish a relationship aimed towards identifying the correlated features helping in feature selection from raw sensor signals. Also, it provides a provision to precisely detect the anomalies in real-time streaming data an unsupervised deep convolutional neural network & also a lstm autoencoders based detectors are designed to run on GPU/CPU. Finally, a game theoretic approach is used to explain the output of the built anomaly detector model.   ...  '