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master's thesis
Identifikacija tipova 1D-signala pomoću nenadziranog dubokog učenja
2017. urn:nbn:hr:168:474486
Tomljanović, Jan
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Tomljanović, J. (2017). Identifikacija tipova 1D-signala pomoću nenadziranog dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:474486

Tomljanović, Jan. "Identifikacija tipova 1D-signala pomoću nenadziranog dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:474486

Tomljanović, Jan. "Identifikacija tipova 1D-signala pomoću nenadziranog dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:474486

Tomljanović, J. (2017). 'Identifikacija tipova 1D-signala pomoću nenadziranog dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 15 April 2024, https://urn.nsk.hr/urn:nbn:hr:168:474486

Tomljanović J. Identifikacija tipova 1D-signala pomoću nenadziranog dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2017 [cited 2024 April 15] Available at: https://urn.nsk.hr/urn:nbn:hr:168:474486

J. Tomljanović, "Identifikacija tipova 1D-signala pomoću nenadziranog dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:168:474486

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Metadata
TitleIdentifikacija tipova 1D-signala pomoću nenadziranog dubokog učenja
Title (english)Identification of 1D-Signal Types Using Unsupervised Deep Learning
AuthorJan Tomljanović
MentorMile Šikić (mentor)
Committee memberMile Šikić (predsjednik povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
Committee memberKrešimir Križanović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2017-07-10, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Tijekom de novo procesa sastavljanja genoma određene tipovi očitanja mogu uzrokovati probleme prilikom rekonstrukcije genoma. Cilj ovog rada je naučiti više o mogućim tipovima očitanja te kako klasificirati očitanja koristeći nenadzirano učenje. Koristeći preklapanja među očitanjima, za svako očitanje generiran je graf pokrivenosti i oni su dalje analizirani. Autoenkoder je korišten s ciljem sažimanja signala, tj. grafa pokrivenosti, i zatim je nad sažetim prikazom podataka obavljeno grupiranje. Korišeni su varijacijski i denoising autoencoder te algoritmi grupiranja k-means i spektralno grupiranje. Nađene grupe signala su vizualizirane te je provedena semantička analiza grupa. Procjenjena je kvaliteta klasifikacije signala korištenjem nenadziranog učenja.
Abstract (english)
During de novo genome assembly process, certain types of sequenced reads can cause problems during genome reconstruction. Goal of this thesis is to learn more about possible types of reads and classification of those reads using unsupervised learning. Coverage graphs of reads are generated using read overlaps and those coverage graphs are further analysed. Autoencoder is used to compress the signal, i.e. the coverage graph, and clustering algorithm is then applied to the compressed data. Variational and denoising autoencoders along with k-means and spectral clustering algorithms are used. Visualisation of found clusters is performed along with semantic analysis. Signal classification quality using unsupervised learning is estimated.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:474486
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 on2019-03-14 01:37:49