Krishna Raj Raghavendran and Ahmed Elragal
In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparamet...
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Khaled Chahine
Machine learning (ML) techniques have permeated various domains, offering intelligent solutions to complex problems. ML has been increasingly explored for applications in active power filters (APFs) due to its potential to enhance harmonic compensation, ...
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Antonio Jesús Banegas-Luna and Horacio Pérez-Sánchez
As machine learning (ML) transforms industries, the need for efficient model development tools using high-performance computing (HPC) and ensuring interpretability is crucial. This paper presents SIBILA, an AutoML approach designed for HPC environments, ...
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Afnan Alotaibi and Murad A. Rassam
Concerns about cybersecurity and attack methods have risen in the information age. Many techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs), that help achieve security goals, such as detecting malicious attacks befo...
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Chongchong Xu, Zhicheng Liao, Chaojie Li, Xiaojun Zhou and Renyou Xie
In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the inc...
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