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undergraduate thesis
Odabir podskupa značajki za analizu kvalitete morske vode
2020. urn:nbn:hr:190:980573
Grgur, Filip
University of Rijeka
Faculty of Engineering
Department of Computer Engineering
Section of Software Engineering

Cite this document

Grgur, F. (2020). Odabir podskupa značajki za analizu kvalitete morske vode (Undergraduate thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:980573

Grgur, Filip. "Odabir podskupa značajki za analizu kvalitete morske vode." Undergraduate thesis, University of Rijeka, Faculty of Engineering, 2020. https://urn.nsk.hr/urn:nbn:hr:190:980573

Grgur, Filip. "Odabir podskupa značajki za analizu kvalitete morske vode." Undergraduate thesis, University of Rijeka, Faculty of Engineering, 2020. https://urn.nsk.hr/urn:nbn:hr:190:980573

Grgur, F. (2020). 'Odabir podskupa značajki za analizu kvalitete morske vode', Undergraduate thesis, University of Rijeka, Faculty of Engineering, accessed 12 April 2024, https://urn.nsk.hr/urn:nbn:hr:190:980573

Grgur F. Odabir podskupa značajki za analizu kvalitete morske vode [Undergraduate thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2020 [cited 2024 April 12] Available at: https://urn.nsk.hr/urn:nbn:hr:190:980573

F. Grgur, "Odabir podskupa značajki za analizu kvalitete morske vode", Undergraduate thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:190:980573

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Metadata
TitleOdabir podskupa značajki za analizu kvalitete morske vode
Title (english)Feature selection model for seawater quality analysis
AuthorFilip Grgur
MentorGoran Mauša (mentor)
Committee memberKristijan Lenac (predsjednik povjerenstva)
Committee memberGoran Mauša (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
(Department of Computer Engineering)
(Section of Software Engineering)
Rijeka
Defense date and country2020-09-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovim radom su opisane značajke koje su potrebne za proračunavanje koncentracija bakterija u moru, te svu problematiku na koju se nailazi pri radu. Osim opisanih korelacija značajki pokrivena je i problematika generiranja modela koji predviđaju koncentraciju bakterija. Generiranje preko automatskog linearnog modeliranja je napravljeno na dva različita načina preko metode koračajnog unaprijednog uključivanja i korištenja svih indikatora, zabilježeni su rezultati istraživanja. Izdvojene su najvažnije značajke koje je moguće pribaviti iz prognoze ili meteorološke web stranice koje su nužne za generiranje kvalitetnog modela. Pokriveni su modeli predviđanja kontaminirane vode preko binarne logističke regresije i stabla odluke.
Abstract (english)
This paper describes the features needed to calculate the concentration of bacteria in the sea, and all problems that occur during that task. In addition to the described correlations, the problem of generating a model that predicts the concentration of bacteria is covered. Generation by automatic linear modeling was done in two different ways: forward stepwise and using all indicators. The research results are described in the paper. The most significant features, which can be obtained from a forecast or meteorology website, that are necessary to generate a quality model are highlighted. Contaminated water prediction models over binary logistic regressions and decision trees are covered.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:980573
Study programmeTitle: Computer Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2021-09-30 09:26:39