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master's thesis
Brzo prepoznavanje mikroba uporabom dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:935178
Bakić, Sara
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Bakić, S. (2021). Brzo prepoznavanje 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:168:935178

Bakić, Sara. "Brzo prepoznavanje mikroba uporabom dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:935178

Bakić, Sara. "Brzo prepoznavanje mikroba uporabom dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:935178

Bakić, S. (2021). 'Brzo prepoznavanje mikroba uporabom dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 08 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:935178

Bakić S. Brzo prepoznavanje mikroba uporabom dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 November 08] Available at: https://urn.nsk.hr/urn:nbn:hr:168:935178

S. Bakić, "Brzo prepoznavanje mikroba uporabom dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:935178

Metadata
TitleBrzo prepoznavanje mikroba uporabom dubokog učenja
Title (english)Rapid Microbe Detection Using Deep Learning
AuthorSara Bakić
MentorMile Šikić (mentor)
Committee memberMile Šikić (predsjednik povjerenstva)
Committee memberTomislav Petković (član povjerenstva)
Committee memberKrešimir Križanović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Mikrobi su mikroskopski organizmi nevidljivi golim okom, ali s vrlo važnom ulogom u svakodnevnom životu. Mogućnost njihovog prepoznavanja i točne klasifikacije osnova je u otkrivanju bolesti, prepisivanju lijekova i održavanju zdravog načina života generalno. Glavni cilj ovoga rada bio je razviti metodu baziranu na dubokom učenju koja na brz i točan način otkriva mikrob na temelju kratkih fragmenata te vrste. Arhitektura mreže bazirana je na novoj arhitekturi prezentiranoj u sklopu obrade prirodnog jezika, pod nazivom transformator, koja je za potrebe detekcije mikroba prilagođena i korištena u svrhe dobivanja sažetih reprezentacija sekvencioniranih fragmenata DNA. Kada su sažete reprezentacije pronađene, različite klasifikacijske metode korištene su za finalnu predikciju vrste mikroba.
Abstract (english)
Microbes are microscopic organisms invisible to the naked eye with a significant role in everyday life. The ability to detect and accurately classify them is essential to discover diseases, prescribe medication, keep a healthy lifestyle in general. The main goal of this thesis is develop a deep learning method that can in a fast and accurate way detect a microbial species from short fragments of that species. The architecture was based on a novel NLP architecture called the Transformer, adapted to the microbe detection task and used to obtain compressed representations of the sequenced DNA fragments. Once the compressed representation were found, different classification methods were applied to output predictions of microbial species.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:935178
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
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Created on2023-01-13 11:42:22