IEEE

A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence

Abstract: Power transformers are critical components in ensuring the continuous and stable operation of power systems. Failures in these units can lead to significant power outages and costly downtime ...
C&EN

Machine-Learning-Assisted Discovery of Cr

Department of Chemistry, University of Houston, Houston, Texas 77204, United States Texas Center for Superconductivity, University of Houston, Houston, Texas 77204, United States Department of ...
Frontiers

Construction of a clinical prediction model for osteoporosis in asymptomatic elderly population based on machine learning algorithm

1 School of Information Engineering, Anhui University of Chinese Medicine, Hefei, China 2 School of Pharmaceutical Economics and Management, Anhui University of Chinese Medicine, Hefei, China ...
IEEE

Finite-Element Based State-Space Modeling of AC Winding Losses in Synchronous Machines

Abstract: This paper proposes a finite element (FE) based state-space model for evaluating AC winding losses in synchronous machines to be implemented in Simulink. The model is constructed by storing ...
Frontiers

Support vector machine-based classification of bulimia nervosa using diffusion tensor imaging

Background: Alterations in brain structure have been suggested to be associated with bulimia nervosa (BN). This study aimed to employ machine learning (ML) methods based on diffusion tensor imaging ...