Announcement for Downloading full text filePlease respect the Copyright Act.
All digital full text dissertation and theses from this website are authorized the copyright owners. These copyrighted full-text dissertation and theses can be only used for academic, research and non-commercial purposes. Users of this website can search, read, and print for personal usage. In respect of the Copyright Act of the Republic of China, please do not reproduce, distribute, change, or edit the content of these dissertations and theses without any permission. Please do not create any work based upon a pre-existing work by reproduction, Adaptation, Distribution or other means.
URN etd-0712104-092221 Statistics This thesis had been viewed 2427 times. Download 1573 times. Author Hsiu-Ping Yang Author's Email Address email@example.com Department Computer Science and Enginerring Year 2004 Semester 2 Degree Master Type of Document Master's Thesis Language English Page Count 65 Title The Application of Grey Model and Artificial Intelligence Techniques to Drive Revenue Assurance of the Telecommunication Operator Keyword Revenue assurance Artificial intelligence Grey prediction Grey prediction Artificial intelligence Revenue assurance Abstract This paper presents an intelligent inference system for revenue assurance of a telecommunication company, where artificial intelligence (AI) techniques such as grey prediction, fuzzy logic (FL), genetic algorithms (GAs), and neural networks (NNs) will be combined to achieve a more accurate problem detector with a higher availability than those traditional risk management and audit approaches could provide. In the telecommunication companies, revenue leakage can take the form of actual loss like billing errors, fraud and bad debts, or from opportunity loss like billings foregone through incorrect calls. It costs hundred millions of dollars loss annually for each company. However, most practical problems in revenue assurance are complex, full of uncertainty and can only be solved by human resources. Due to such a difficult situation, the problems cannot be effectively prevented and overcome.
A better prediction model from grey system is introduced and different AI techniques for synergism of FL, GAs, and NNs are presented. The optimization mechanisms for our prediction model and the measures of performance with precision and coverage are also discussed. The thesis finally exploits the empirical errors of revenue leakage for measuring prediction ability by using our proposed methods to demonstrate the system potential of revenue assurance.
Advisor Committee Yo-Ping Huang - advisor
Files Date of Defense 2004-06-11 Date of Submission 2004-07-12