How Rajesh Mattaparthi Is Using AI To Detect Hidden Faults In Standby Power Generators

Principal Data Engineer Rajesh Mattaparthi is using transformer-based AI to detect hidden faults in standby power generators ...
The Robot Report

We know how to build smarter robots. Now, we need to learn smarter ways to test them

Atharv Kolhar, a staff test automation engineer at Figure AI, says the robotics industry needs a testing philosophy that ...
IEEE

Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning

Abstract: The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologies allows ...
Dataquest

Google Cloud enters AI-native cybersecurity race against Anthropic and OpenAI

Google Cloud has expanded its enterprise cybersecurity push with AI Threat Defense, as the market shifts towards AI-native threat detection, contextual triage, and supervised remediation. The launch ...
IEEE

Self-Supervised Anomaly Detection Using Contrastive Learning for Network Security

Abstract: The growing popularity of encrypted network traffic, which was necessitated by privacy protection and safe communication-related protocols, has significantly limited the effectiveness of the ...
leewayhertz

AI in anomaly detection: Use cases, methods, algorithms and solution

In this era of digital transformation, buzzwords like ‘Industry 4.0’ and ‘digitalization’ have become part of our daily vocabulary. But behind these trendy terms lies a potent technological innovation ...
Hacker

An Overview of Log-Based Anomaly Detection Techniques

5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?
GitHub

This project proposes an end-to-end framework for semi-supervised Anomaly Detection and ...

The proposed method employs a thresholded pixel-wise difference between reconstructed image and input image to localize anomaly. The threshold is determined by first using a subset of anomalous-free ...
GitHub

CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection

Most time series anomaly detection models aim to learn normal behavior from unlabelled data, identifying anomalies as deviations from this behavior. However, the lack of labelled data makes it ...
Scientific Research Publishing

Machine Learning Models for Heterogenous Network Security Anomaly Detection ()

1 Department of Computer Science, Veritas University, Abuja, Nigeria. 2 Department of Pure and Applied Physics, Veritas University, Abuja, Nigeria. 3 Department of Computer Science, Air Force ...
leewayhertz

Anomaly detection in fraud prevention: Benefits, development and future trends

The digital age has ushered in a new era of convenience and opportunity. However, it has also opened doors for malicious actors, leading to a surge in fraudulent activities across various sectors.