An Automated Machine Learning Approach for Smart Waste Management Systems

This paper presents the use of automated machine learning for solving a practical problem of a real-life Smart Waste Management system. In particular, the focus of the paper is on the problem of detection (i.e., binary classification) of emptying of a recycling container using sensor measurements. N...

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Veröffentlicht in:	IEEE transactions on industrial informatics Jg. 16; H. 1; S. 384 - 392
Hauptverfasser:	Rutqvist, David, Kleyko, Denis, Blomstedt, Fredrik
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accelerometers Acoustics Algorithms Automated machine learning (AutoML) Automated machines Automation Binary classification Classification Classification (of information) Classification accuracy Classification algorithm classification algorithms Containers Conventional machines data mining Decision trees Dependable Communication and Computation Systems Emptying emptying detection grid search Informatics Kommunikations- och beräkningssystem Learning algorithms Machine learning Management systems Measurement methods Random forest classifier Recycling Smart Waste Management Ultrasonic variables measurement Waste management Waste management systems
ISSN:	1551-3203, 1941-0050, 1941-0050
Online-Zugang:	Volltext
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