| dc.contributor.advisor | Potgieter, Anet | en_ZA |
| dc.contributor.author | Osunmakinde, Isaac Olusegun | en_ZA |
| dc.date.accessioned | 2014-08-13T19:31:54Z | |
| dc.date.accessioned | 2018-11-26T13:52:59Z | |
| dc.date.available | 2014-08-13T19:31:54Z | |
| dc.date.available | 2018-11-26T13:52:59Z | |
| dc.date.issued | 2006 | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/6430 | |
| dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/11427/6430 | |
| dc.description | Word processed copy. | en_ZA |
| dc.description.abstract | In this research, we present a modelling technique that can efficiently facilitate anomaly detection that will help call analysts and managers with adaptive decision-making. We developed and implemented a Data 'fransformation System (DTS), a new Hybrid Genetic Algorithm (HGA) and an Anomaly Detection System (ADS) to address this challenge. | en_ZA |
| dc.language.iso | eng | en_ZA |
| dc.subject.other | Computer Science | en_ZA |
| dc.title | Intelligent detection of anomalies in telecommunications customer behaviour | en_ZA |
| dc.type | Thesis | en_ZA |
| dc.type.qualificationlevel | Masters | en_ZA |
| dc.type.qualificationname | MSc | en_ZA |
| dc.publisher.institution | University of Cape Town | |
| dc.publisher.faculty | Faculty of Science | en_ZA |
| dc.publisher.department | Department of Computer Science | en_ZA |
