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dc.contributor.advisorRafiee, Mehranen
dc.contributor.authorThakur, Parth
dc.date.accessioned2021-04-28T19:57:06Z
dc.date.available2021-04-28T19:57:06Z
dc.date.issued2020
dc.identifier.citationThakur, P. (2020). Comparative analysis of machine learning algorithms for detection of fast radio bursts. Masters Thesis, Dublin Business School.en
dc.identifier.urihttps://esource.dbs.ie/handle/10788/4246
dc.description.abstractFast radio bursts are a new astronomical phenomenon that have scientists baffled. To detect and analyse these FRBs, upcoming radio telescopes will capture tremendous amount of data. At the scales of these new telescopes, traditional methods of manually analysing the data fail. There is a need for extensive research in the use of machine learning for this task. This dissertation compares the performance of various machine learning algorithms, so they can be scaled to meet the demands of these new telescopes. To that extent, this research explores ways to simulate FRBs for analysis. The study compares four algorithms, namely, K-Nearest Neighbours, Random Forests, Deep Learning, and Convolutional Neural Networks.en
dc.language.isoenen
dc.publisherDublin Business Schoolen
dc.rightsItems in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.en
dc.rights.urihttp://esource.dbs.ie/copyrighten
dc.subjectMachine learningen
dc.subjectRadio telescopesen
dc.subjectDeep Learningen
dc.titleComparative analysis of machine learning algorithms for detection of fast radio burstsen
dc.typeThesisen
dc.rights.holderCopyright: The publisheren
dc.type.degreenameMSc in Data Analyticsen
dc.type.degreelevelMScen


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