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master's thesis
Klasifikacija mikroba uporabom dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:983166
Pongračić, Kristijan
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Pongračić, K. (2022). Klasifikacija mikroba uporabom dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:983166

Pongračić, Kristijan. "Klasifikacija mikroba uporabom dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:983166

Pongračić, Kristijan. "Klasifikacija mikroba uporabom dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:983166

Pongračić, K. (2022). 'Klasifikacija mikroba uporabom dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 23 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:983166

Pongračić K. Klasifikacija mikroba uporabom dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 June 23] Available at: https://urn.nsk.hr/urn:nbn:hr:168:983166

K. Pongračić, "Klasifikacija mikroba uporabom dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:983166

Metadata
TitleKlasifikacija mikroba uporabom dubokog učenja
Title (english)Microbe Classification Using Deep Learning
AuthorKristijan Pongračić
MentorMile Šikić (mentor)
Committee memberMile Šikić (predsjednik povjerenstva)
Committee memberIvica Botički (član povjerenstva)
Committee memberKrešimir Križanović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Mikrobi su sveprisutni, golim okom nevidljivi organizmi. Oni utječu i međudjeluju s ljudima na više načina. Stoga je važno moći prepoznati i klasificirati mikrobe kako bi otkrili bolesti, propisali liječenje i zadržali zdrav način života. Cilj ovog rada bio je razviti klasifikator koji bi mogao odrediti kojoj klasi pripada sekvencionirano očitanje nekog mikroba. Arhitektura modela temelji se na modelu transformera. Za poboljšanje performansi koriste se dvije različite metode ekstrakcije značajki i četiri tehnike povečanja podataka. Model je testiran na Zymo mock community skupu podataka.
Abstract (english)
Microbes are omnipresent organisms invisible to the naked eye. They affect and interact with humans in multiple ways. The ability to detect and classify microbes is essential to discover diseases, prescribe medication, and keep a healthy lifestyle. The goal of this thesis was to develop a classifier that could predict the class membership of an individual sequenced read that corresponds to some microbe. Model architecture is based on the transformer model. Two different feature extraction methods and four data augmentation techniques are used to improve performance. The model was tested on Zymo mock community dataset.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:983166
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-01-13 15:55:18