Quantifying agglutination process under different analyte concentration using a hybrid convolutional neural network and comparative deep learning models

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Title	Quantifying agglutination process under different analyte concentration using a hybrid convolutional neural network and comparative deep learning models
Creator	Juberi, Hridi
Supervisor	Foulds, Ian G.
Publisher	University of British Columbia
Date Issued	2025
Description	The full abstract for this item is available in the body of the item, and will be available when the embargo expires.
Genre	Thesis/Dissertation
Type	Text
Language	eng
Date Available	2026-12-25
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0451007
URI	http://hdl.handle.net/2429/93168
Degree (Theses)	Master of Applied Science - MASc
Program (Theses)	Electrical Engineering
Affiliation	Applied Science, Faculty of; Engineering, School of (Okanagan)
Degree Grantor	University of British Columbia
Graduation Date	2026-02
Campus	UBCO
Scholarly Level	Graduate
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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Attribution-NonCommercial-NoDerivatives 4.0 International