Abstract: Anomaly detection for time-series data has been viewed widely in many practical applications and caused lots of research interests. A popular solution based on deep learning techniques is ...

This project implements a GAN-based approach for detecting anomalies in smart meter readings using the Large-scale Energy Anomaly Detection (LEAD) dataset. The model uses LSTM-based Generator and ...

ThreatForge is an enterprise-grade detection pipeline that enriches security alerts with threat intelligence, applies ML-based anomaly detection, and integrates seamlessly with existing Splunk ...

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other 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?

In today's fast-paced world, where new AI evolutions emerge daily and companies grapple with monster-sized datasets, creating space for innovation isn't optional—it's essential. In 2024, Hydrolix ...

Hairfall is a primary concern for many individuals worldwide today. Hair strands may fall due to various conditions such as hereditary factors, scalp health issues, nutritional deficiencies, hormonal ...

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...

Abstract: Anomaly detection is a critical problem with a variety of applications since anomalies (which are unexpected observations that deviate significantly from other observations) pervasively ...