Communications of the ACM

Where Are the City Trees? Monitoring Urban Trees across the U.S. Using Generative AI

The use of generative AI enables a novel computational approach to localize individual trees in all cities, despite their ...
pv magazine International

Industry 4.0 technologies raise cyber risks for smart renewables grids

An international research team developed a multi-stage intrusion detection system that uses supervised and unsupervised AI techniques to detect and mitigate cyber threats in smart renewable energy ...
IEEE

Anomaly Detection in IoT Environment Using Machine Learning: A survey

Abstract: The explosive growth of the Internet of Things (IoT) has introduced vast amounts of data and unprecedented security challenges, making effective anomaly detection in IoT environments a ...
Frontiers

Optimizing the operation of fuel cell vehicle-to-grid systems using supervised learning

The operation of fuel cell electric vehicle-to-grid (FCEV2G) stations presents a significant challenge due to the need to manage onsite hydrogen production, storage, and vehicle dispatch in volatile ...
Visual Studio Magazine

Anomaly Detection Using K-Means Clustering with JavaScript

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, ...
Frontiers

HairSentinel: a time-aware anomaly detection framework for forecasting hairfall trends using temporal fusion transformers

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 ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

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 ...
IEEE

OSAD: Open-set Supervised Anomaly Detection in Surveillance Videos based on Margin Metric Learning

Abstract: Open-set Supervised Anomaly Detection (OSAD) strategy seeks to detect novel anomalies that are unseen during training. However, existing OSAD works fail to learn a comprehensive margin that ...