Mogućnosti primjene metoda strojnog učenja u području telekomunikacija

undergraduate thesis

• Title (english): Opportunities for Applying Machine Learning Methods in Telecommunications
• Author: David Aleksić
• Mentor: Ivan Cvitić (mentor)
• Committee member: Dragan Peraković (predsjednik povjerenstva)
• Committee member: Siniša Husnjak (član povjerenstva)
• Committee member: Marko Periša (član povjerenstva)
• Granter: University of Zagreb; Faculty of Transport and Traffic Sciences; (Division of Transport); (Department of Information and Communications Traffic); Zagreb
• Defense date and country: 2021-08-31, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Traffic and Transport Technology; Postal and Telecommunications Traffic

Abstract

Ovaj završni rad prikazuje metode strojnog učenja i mogućnosti primjene strojnog učenja u području telekomunikacija. Uz primjere primjene strojnog učenja također je definiran pojam umjetne inteligencije koja je danas u sve češćoj upotrebi u svijetu. Strojno učenje kao podsustav umjetne inteligencije ima veliki broj vlastitih algoritama koji se koriste za različite primjere. Sa sve većim razvojem tehnologija kao što su veće brzine interneta, 5G mobilne mreže itd. iz npr. telekomunikacijskog područja dolazi do potrebe za boljim upravljanjem i podrškom za sustave koji se koriste novim tehnologijama. Metode strojnog učenja mogu se koristiti za poboljšanje korisničke usluge prikupljanjem podataka, za prikupljanje podataka o prometu u mreži i općenito u svim sustavima u kojima se zahtjeva prikupljanje, grupiranje i analiza podataka.

Abstract (english)

This bachelor's thesis shows methods of machine learning and opportunities of using it in telecommunications. With the examples of using machine learning methods, the term of artificial intelligence, which is today used more than ever before, is defined in this thesis as well. Machine learning as a subset of artificial intelligence has a lot of its own algorithms and methods. With the improvement of the new technologies such as higher internet speeds, 5G mobile networks etc. for e.g., in telecommunication, comes the need for the new and improved management and support for the systems that are using those new technologies. Some types of machine learning can be used for collecting data to improve user’s quality of service, some are used for collecting data of traffic in networks or generally for any system that has the need to collect data, cluster data points and analysing data.

• Keywords: umjetna inteligencija; strojno učenje; nadzirano strojno učenje; nenadzirano strojno učenje; algoritmi strojnog učenja
• Keywords (english): Artificial Intelligence; Machine Learning; Supervised Machine Learning; Unsupervised Machine Learning; Machine Learning algorithms
• Language: croatian
• URN:NBN: urn:nbn:hr:119:003361
• Study programme: Title: Traffic and Transport; specializations in: Road Traffic and Transport, Railway Traffic and Transport, Water Traffic and Transport, Air Traffic and Transport, Postal Traffic, Information and Communication Traffic, Urban Traffic and Transport; Course: Road Traffic and Transport; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka prometa (sveučilišni/a prvostupnik/prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka prometa)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2021-09-03 14:08:11