prikaz prve stranice dokumenta Identifikacija tipova 1D-signala dubokog učenja
No public access
master's thesis
Identifikacija tipova 1D-signala dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:825125
Floreani, Filip
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Floreani, F. (2019). Identifikacija tipova 1D-signala dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:825125

Floreani, Filip. "Identifikacija tipova 1D-signala dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:825125

Floreani, Filip. "Identifikacija tipova 1D-signala dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:825125

Floreani, F. (2019). 'Identifikacija tipova 1D-signala dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 02 September 2024, https://urn.nsk.hr/urn:nbn:hr:168:825125

Floreani F. Identifikacija tipova 1D-signala dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 September 02] Available at: https://urn.nsk.hr/urn:nbn:hr:168:825125

F. Floreani, "Identifikacija tipova 1D-signala dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:825125

Metadata
TitleIdentifikacija tipova 1D-signala dubokog učenja
Title (english)Classification of 1D-Signal Types Using Deep Learning
AuthorFilip Floreani
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 country2019-07-04, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
De-novo sastavljanje genoma proces je temeljen na preklapanju i analizi kratkih genetskih očitanja. Uslijed raznolikih tehničkih izazova, određene vrste lažnih očitanja i preklapanja mogu također biti sintetizirane, što uvelike otežava is- pravno sastavljanje originalne sekvence. Jedna od metoda za otkrivanje takvih preklapanja je i pretvorba istih u 1D-signal, pomoću kojih se onda može jed- nostavnije zaključiti koji tip preklapanja predstavlja. U okviru ovog rada, pred- stavljeno je nekoliko metoda duboko učenja za klasifikaciju ovih signala, točnije 1D-konvolucijske mreže, povratne mreže, kao i primjena autoenkodera. Dodatno, prikazana je i njihova primjena na stvarnim podacima, kao i usporedba usp- ješnosti.
Abstract (english)
The de novo genome assembly process is based on overlapping and analyzing short reads of genetic information. Due to various technical challenges, certain types of false overlaps can also be generated, which greatly impedes successful reconstruction. One of the methods for detecting such overlaps is by generating a 1D-signal for each read, which can then be used to determine its exact overlap type. This thesis proposes several deep learning methods for classifying these signals, including 1D-convolutional & recurrent networks, as well as autoencoders. A detailed comparison of their application on real-world data is also included.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:825125
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
Terms of useLicence In copyright icon
Public note
Created on2020-01-07 17:56:17