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master's thesis
Detekcija žanra igre temeljena na strojnom učenju
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:187127
Mustapić, Tina
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Mustapić, T. (2022). Detekcija žanra igre temeljena na strojnom učenju (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:187127

Mustapić, Tina. "Detekcija žanra igre temeljena na strojnom učenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:187127

Mustapić, Tina. "Detekcija žanra igre temeljena na strojnom učenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:187127

Mustapić, T. (2022). 'Detekcija žanra igre temeljena na strojnom učenju', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:187127

Mustapić T. Detekcija žanra igre temeljena na strojnom učenju [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 July 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:187127

T. Mustapić, "Detekcija žanra igre temeljena na strojnom učenju", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:187127

Metadata
TitleDetekcija žanra igre temeljena na strojnom učenju
Title (english)Game genre detection based on machine learning
AuthorTina Mustapić
MentorMirko Sužnjević (mentor)
Committee memberMirko Sužnjević (predsjednik povjerenstva)
Committee memberMaja Matijašević (član povjerenstva)
Committee memberIgor Sunday Pandžić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Cloud gaming je usluga igranja videoigri na oblaku koja radi na sličan način kao i usluge videa na zahtjev. Videoigre se pohranjuju i izvode na daljinu na hardveru poslužitelja, te se prenose kao video na ured̄aj igrača putem klijentskog softvera. Me- d̄utim, cloud gaming zahtijeva pouzdanu, brzu vezu s internetom. Loša kvaliteta, kao i zastajkivanje te veliko kašnjenje videa komplikacije su koje mogu iznimno smanjiti iskustvenu kvalitetu igrača. Jedan od načina poboljšavanja iskustvene kvalitete bi bilo prilagod̄avanje mrežnih postavki s obzirom na žanr videoigre. Ovaj rad opisuje tre- niranje i validiranje 14 različitih klasifikacijskih modela na podatkovnom skupu koji sadržava 20 videoigri podijeljenih na 5 žanrova. Model strojnog učenja koji se pokazao kao najbolji detektor žanra videoigri, s obzirom na skupljene podatke, je Extra Trees klasifikator, a najbolje klasificiran žanr je strategija. Najbolji rezultati su trenirani nad cijelim skupom podataka, ali približno jednake rezultate možemo dobiti trenirajući samo nad streaming komponentom koju je računalno jeftinije dobiti od cijelog skupa podataka.
Abstract (english)
Cloud gaming is a cloud video game service that works in a similar way to video- on-demand services. Video games are stored and played remotely on the server har- dware, and are transmitted as video to the player’s device via client software. However, cloud gaming requires a reliable, fast connection to the Internet. Poor quality, as well as stalling and long video delays are complications that can greatly reduce the quality of experience of a player. One way to improve the experiential quality would be to adjust the network settings according to the video game genre. This paper describes the training and validation of 14 different classification models on a data set containing 20 video games divided into 5 genres. The machine learning model that has proven to be the best detector of the video game genre, given the data collected, is the Extra Trees classifier, and the best classified genre is strategy. The best results are trained over the whole data set, but approximately the same results can be obtained by training only over the streaming component which is computationally cheaper to obtain than the whole data set.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:187127
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
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Created on2023-01-13 15:42:06