Machine Learning for Classifying Cellular Traffic
Ladda ner
Typ
Examensarbete för masterexamen
Master Thesis
Master Thesis
Program
Software engineering and technology (MPSOF), MSc
Publicerad
2017
Författare
Frölich, Isabelle
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Data- och informationsvetenskap , Computer and Information Science