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master's thesis
Korisnost pretvorbe vremenskih podataka u sliku u svrhu klasifikacije
2022. urn:nbn:hr:166:286875
Katić, Josip
University of Split
Faculty of Science
Department of Informatics

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Katić, J. (2022). Korisnost pretvorbe vremenskih podataka u sliku u svrhu klasifikacije (Master's thesis). Split: University of Split, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:166:286875

Katić, Josip. "Korisnost pretvorbe vremenskih podataka u sliku u svrhu klasifikacije." Master's thesis, University of Split, Faculty of Science, 2022. https://urn.nsk.hr/urn:nbn:hr:166:286875

Katić, Josip. "Korisnost pretvorbe vremenskih podataka u sliku u svrhu klasifikacije." Master's thesis, University of Split, Faculty of Science, 2022. https://urn.nsk.hr/urn:nbn:hr:166:286875

Katić, J. (2022). 'Korisnost pretvorbe vremenskih podataka u sliku u svrhu klasifikacije', Master's thesis, University of Split, Faculty of Science, accessed 23 April 2024, https://urn.nsk.hr/urn:nbn:hr:166:286875

Katić J. Korisnost pretvorbe vremenskih podataka u sliku u svrhu klasifikacije [Master's thesis]. Split: University of Split, Faculty of Science; 2022 [cited 2024 April 23] Available at: https://urn.nsk.hr/urn:nbn:hr:166:286875

J. Katić, "Korisnost pretvorbe vremenskih podataka u sliku u svrhu klasifikacije", Master's thesis, University of Split, Faculty of Science, Split, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:166:286875

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Metadata
TitleKorisnost pretvorbe vremenskih podataka u sliku u svrhu klasifikacije
Title (english)Utility of converting time series data into an image for classification purposes
AuthorJosip Katić
MentorSaša Mladenović (mentor)
Committee memberSaša Mladenović (predsjednik povjerenstva)
Committee memberDivna Krpan (član povjerenstva)
Committee memberGoran Zaharija (član povjerenstva)
GranterUniversity of Split
Faculty of Science
(Department of Informatics)
Split
Defense date and country2022-09-26, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Duboko učenje je jedno od područja računalstva koje je postalo sve popularnije i razvijenije posljednjih godina i to je područje koje se promatra unutar ovog rada. Točni aspekti koji se promatraju je usporedba dvije arhitekture neuronskih mreža. Promatramo rekurentnu i konvolucijsku neuronsku mrežu (RNN i CNN) s naglaskom na konvolucisjku neuronsku mrežu u smislu da koristimo podatke koji nisu originalno namijenjeni za tu arhitekturu te hoće li ta mreža s prilagodbom podataka postići zadovoljavajuće rezultate koji su usporedivi s arhitekturom i podatcima korištenim za rekurentnu neuronsku mrežu. Korišteni podatci su snimke moždanih valova dobivene koristeći EEG namijenjeni za rad s RNN arhitekturom, te se prilagodba podatka za CNN arhitekturu izvodila koristeći GAF (GASF i GADF) i MTF algoritme za generiranje slika iz vremenskih podataka.
Abstract (english)
Deep learning is one of the fields of computer science that has become ever more popular and advanced in recent years and that is the field which is observed within this paper. Exect aspect that is observed is the comparison of two neural network architectures. We observe the recurrent and the convolutional neural network (RNN and CNN) while concentrating on the convolutional neural network in the sense that we use data that is not originally suitable for that architecture and will that network with the adjusted data give a satisfying results that are comparable with the architecture and data used for a recurrent neural network. The used data are recordings of brain waves that are acquired by using an EEG and are intended for RNN architecture but for the CNN architecture the data was adjusted by using GAF (GASF and GADF) and MTF algorithms for generating images from time series data.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:166:286875
Study programmeTitle: Computer Science; specializations in: major in teaching
Course: major in teaching
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra edukacije informatike (magistar/magistra edukacije informatike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) icon
Created on2023-01-12 13:16:36