A distributed and energy-efficient KNN for EEG classification with dynamic money-saving policy in heterogeneous clusters

Due to energy consumption’s increasing importance in recent years, energy-time efficiency is a highly relevant objective to address in High-Performance Computing (HPC) systems, where cost significantly impacts the tasks executed. Among these tasks, classification problems are considered due to their...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Computing Jg. 105; H. 11; S. 2487 - 2510
Hauptverfasser:	Escobar, Juan José, Rodríguez, Francisco, Prieto, Beatriz, Kimovski, Dragi, Ortiz, Andrés, Damas, Miguel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Vienna Springer Vienna 01.11.2023 Springer Nature B.V
Schlagworte:	Artificial Intelligence Classification Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Datasets Electroencephalography Energy consumption Energy management Energy policy High performance systems Information Systems Applications (incl.Internet) K-nearest neighbors algorithm Parallel programming Redundancy Regular Paper Software Engineering Sustainability Task complexity Heterogeneous clusters Energy-aware computing EEG classification KNN 68W15 Parallel and distributed programming Money-saving
ISSN:	0010-485X, 1436-5057
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!