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master's thesis
Identifikacija tipova 1D-signala pomoću polu-nadziranog dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. urn:nbn:hr:168:377071
Šebrek, Tomislav
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Šebrek, T. (2017). Identifikacija tipova 1D-signala pomoću polu-nadziranog dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:377071

Šebrek, Tomislav. "Identifikacija tipova 1D-signala pomoću polu-nadziranog dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:377071

Šebrek, Tomislav. "Identifikacija tipova 1D-signala pomoću polu-nadziranog dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:377071

Šebrek, T. (2017). 'Identifikacija tipova 1D-signala pomoću polu-nadziranog dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 29 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:377071

Šebrek T. Identifikacija tipova 1D-signala pomoću polu-nadziranog dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2017 [cited 2024 August 29] Available at: https://urn.nsk.hr/urn:nbn:hr:168:377071

T. Šebrek, "Identifikacija tipova 1D-signala pomoću polu-nadziranog 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:168:377071

Metadata
TitleIdentifikacija tipova 1D-signala pomoću polu-nadziranog dubokog učenja
Title (english)Classification of 1D-Signal Types Using Semi-Supervised Deep Learning
AuthorTomislav Šebrek
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
U okviru ovog rada, predložili smo metode za detekciju tipova grafova pokrivenosti na temelju polu-nadziranih modela dubokog učenja: autoenkodera i generativnih suparničkih mreža. Testirali smo performanse svih modela koristeći bazu koja sadržava očitanja s više referentnih genoma. Ručno smo označili dio podataka i usporedili rezultate s obzirom na količinu označenih primjera koji su bili korišteni prilikom treniranja. Ovu detekciju smo ugradili u proces sastavljanja i dobili dobre rezultate. Izvorni kod je dostupan na https://github.com/tomislavsebrek/diplomski.
Abstract (english)
In this thesis, we proposed methods for detecting the type of the coverage graph based on semi-supervised deep learning models: autoencoders and generative adversarial networks. We evaluated the performance of each model based on the dataset that contained reads from multiple reference genomes. We have manually labeled some of the data and compared the results of all models with respect to the number of labeled examples that were used during the training process. We have embedded this detection in the assembly process and achieved good results. The source code is available at https://github.com/tomislavsebrek/diplomski.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:377071
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 01:10:11