Machine Learning for Classifying Cellular Traffic

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/250233
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Type: Examensarbete för masterexamen
Master Thesis
Title: Machine Learning for Classifying Cellular Traffic
Authors: Frölich, Isabelle
Abstract: Today’s cellular network is ever growing, making the need for a mechanism that can identify overloads greater each day. In this report a design science research is conducted showcasing the possibilities to use the classification machine learning algorithm naive Bayes to identify signaling overloads in a cellular network node. The research shows that naive Bayes can be used to successfully identify the greater majority of the possible overloads that could occur in a cellular node.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2017
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/250233
Collection:Examensarbeten för masterexamen // Master Theses



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