PV Tech

AI in PV plant operations: the road ahead

Shallem, Greg Ravikovich and Eitan Har-Shoshanim examine how AI addresses the challenge of data overload in solar PV.
pv magazine International

New framework promises unprecedented level of cybersecurity for SCADA systems

An international research team developed CyberSentry, a software framework using advanced deep learning and optimization techniques to enhance cybersecurity in SCADA systems for power plants and ...
IEEE

Learning Local Semantic Signals and Inter-class Discrepancy for Weakly Supervised Video Anomaly Detection

Abstract: Weakly supervised video anomaly detection (WSVAD) aims to retrieve temporal intervals containing anomalous events within untrimmed videos, leveraging video-level annotations. The existing ...
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

Self-Supervised Learning Meets Custom Autoencoder Classifier: A Semi-Supervised Approach for Encrypted Traffic Anomaly Detection

Abstract: The widespread adoption of encryption in computer networks has made detecting malicious traffic, especially at network perimeters, increasingly challenging. As packet contents are concealed, ...