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master's thesis
Prepoznavanje mikroba uporabom dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:085191
Baksa, Mirna
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Baksa, M. (2020). 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:168:085191

Baksa, Mirna. "Prepoznavanje mikroba uporabom dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:085191

Baksa, Mirna. "Prepoznavanje mikroba uporabom dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:085191

Baksa, M. (2020). 'Prepoznavanje mikroba uporabom dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:085191

Baksa M. Prepoznavanje mikroba uporabom dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 June 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:085191

M. Baksa, "Prepoznavanje mikroba uporabom dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:085191

Metadata
TitlePrepoznavanje mikroba uporabom dubokog učenja
Title (english)Microbe Detection Using Deep Learning
AuthorMirna Baksa
MentorMile Šikić (mentor)
Committee memberMile Šikić (predsjednik povjerenstva)
Committee memberDamir Seršić (član povjerenstva)
Committee memberKrešimir Križanović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-09-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Mikrobi, sveprisutni mikroorganizmi nevidljivi golom oku, utječu na mnogo funkcija u ljudskom tijelu. Mogućnost njihovog prepoznavanja i klasificiranja je važna kod otkrivanja bolesti, prepisivanja lijekova i održavanja zdravog načina života. Cilj ovog rada je razvoj metode za detekciju mikroba koristeći metode dubokog učenja. Razvijena metoda pronalazi odgovarajuće reprezentacije signala očitanih za dani DNA fragment mikroba. Nakon pronalaska reprezentacija signala, odgovarajuća metrika je korištena za razdvajanje različitih vrsta u latentnom prostoru. U konačnici, očitanja su klasificirana koristeći prikladni klasifikacijski model.
Abstract (english)
Microbes, omnipresent microorganisms invisible to the naked eye, impact many functions in the human body. The ability to detect and classify them is essential in order to discover diseases, prescribe medication, and keep a healthy lifestyle. The goal of this thesis is to develop a method for microbe detection based on a deep learning architecture. The architecture is designed to find suitable representations of signals corresponding to sequenced microbe DNA fragments. After finding the signal representations, an appropriate distance metric is used to separate different species in the latent space. In the end, reads are classified using a suitable classifier.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:085191
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 11:43:32