Enhanced Covid19 Prediction through a Stacked Autoencoder LSTM Framework for Anomaly Detection and Respiratory Disease Classification

dc.contributor.guide	Kushwaha, Nagendra
dc.coverage.spatial
dc.creator.researcher	Malviya, Anjali
dc.date.accessioned	2025-10-06T10:37:41Z
dc.date.available	2025-10-06T10:37:41Z
dc.date.awarded	2025
dc.date.completed	2025
dc.date.registered	2020
dc.description.abstract	Available newline
dc.description.note
dc.format.accompanyingmaterial	None
dc.format.dimensions
dc.format.extent	175 p.
dc.identifier.researcherid	0000-0002-9384-4725
dc.identifier.uri	http://hdl.handle.net/10603/666502
dc.language	English
dc.publisher.institution	Department of Electronics and Communication Engineering
dc.publisher.place	Pune
dc.publisher.university	Indian Institute of Information Technology Pune
dc.relation
dc.rights	university
dc.source.university	University
dc.subject.keyword	Autoencoder
dc.subject.keyword	Computer Science
dc.subject.keyword	Computer Science Artificial Intelligence
dc.subject.keyword	Cough Classification
dc.subject.keyword	COVID_19
dc.subject.keyword	Engineering and Technology
dc.subject.keyword	LSTM
dc.subject.keyword	Respiratory Disease Classification
dc.title	Enhanced Covid19 Prediction through a Stacked Autoencoder LSTM Framework for Anomaly Detection and Respiratory Disease Classification
dc.title.alternative
dc.type.degree	Ph.D.