Supervised and unsupervised machine learning approaches to detect drowsiness in drivers

dc.contributor.guide	Raj, A Albert
dc.coverage.spatial	Electrical Engineering
dc.creator.researcher	R, Reena Daphne
dc.date.accessioned	2019-03-18T10:06:17Z
dc.date.available	2019-03-18T10:06:17Z
dc.date.awarded	n.d.
dc.date.completed	2017
dc.date.registered	n.d.
dc.description.abstract	Abstract available
dc.description.note	Data not available
dc.format.accompanyingmaterial	None
dc.format.dimensions	30cm.
dc.format.extent	xxiv, 183p.
dc.identifier.uri	http://hdl.handle.net/10603/232612
dc.language	Others
dc.publisher.institution	Faculty of Electrical Engineering
dc.publisher.place	Chennai
dc.publisher.university	Anna University
dc.relation	174-182
dc.rights	university
dc.source.university	University
dc.subject.keyword	BeeColony
dc.subject.keyword	Bootstrap
dc.subject.keyword	Drowsiness
dc.subject.keyword	Perceptron
dc.subject.keyword	SupervisedMachine
dc.title	Supervised and unsupervised machine learning approaches to detect drowsiness in drivers
dc.title.alternative
dc.type.degree	Ph.D.