The risk of bias was evaluated using the Quality In Prognosis Studies tool and the Prediction model Risk Of Bias Assessment Tool by 2 investigators (MMS and MAS) independently. A narrative synthesis ...

Outdated targeting data may have resulted in a mistaken missile strike, according to the ongoing military investigation, which undercuts President Trump’s assertion that Iran could be to blame. By ...

Abstract: Traditional fault detection systems in power networks face significant challenges in accurately identifying complex fault patterns, particularly in multi-bus configurations with varying ...

Abstract: Fault detection in power systems is critical for ensuring system reliability and stability. This study presents a rule-based classification approach for identifying fault types, including ...

Code, configuration templates, and documentation for the PSCC 2026 paper Comparison of Deep Learning Methods for Fault Analysis in Power System Protection. An end-to-end machine learning pipeline for ...

Speaking at a recent conference panel, officials identified a top use case on their wish list: assisting with the classification of records and the authorization to access those records. “I would ...

MATLAB code for dimensionality reduction, feature extraction, fault detection, and fault diagnosis using Kernel Principal Component Analysis (KPCA). A benchmark fault diagnosis dataset comprises ...

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

IC manufacturers are increasingly relying on intelligent data processing to prevent downtime, improve yields, and reduce scrap. They are integrating that with fault detection and classification (FDC) ...