Thesis (Ph. D.)--State University of New York at Buffalo, 2002. / "May 10, 2002." Includes bibliographical references (p. 140-150). Also available in print.

Thesis (M. Phil.)--University of Hong Kong, 1999. / Includes bibliographical references (leaves 123-127).

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2002. / Thesis advisor(s): Tri T. Ha, Jovan Lebaric. Includes bibliographical references (p. 63). Also available online.

Thesis (Ph. D.)--University of Chicago, Graduate School of Business. / Includes bibliographical references. Also available on the Internet.

Thesis (MSc)--University of Stellenbosch, 2005. / Bibliography. Also available via the Internet.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 80-82). Also available via the Internet.

Magister Scientiae - MSc / Huge amounts of data are being collected as a result of the increased use of mobile telecommunications. Insight into information and knowledge derived from these databases can give operators a competitive edge in terms of customer care and retention, marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing purpose are unlabeled. Further analysis is thus, required to be able to isolate fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process. This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a period of time in a mobile telecommunication network. Our study provides a comparative analysis and application of Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) recurrent neural networks algorithms to user call data records in order to conduct a descriptive data mining on users call patterns. Our investigation shows the learning ability of both techniques to discriminate user call patterns; the LSTM recurrent neural network algorithm providing a better discrimination than the SOM algorithm in terms of long time series modelling. LSTM discriminates different types of temporal sequences and groups them according to a variety of features. The ordered features can later be interpreted and labeled according to specific requirements of the mobile service provider. Thus, suspicious call behaviours are isolated within the mobile telecommunication network and can be used to to identify fraudulent call patterns. We give results using masked call data from a real mobile telecommunication network. / South Africa

Recent advancements in Large Scale Integration Technology have made available devices, such as microprocessors, analog gates and “three state” logic, that provide the designer with a wide range of possibilities in the design of telecommunication systems. A microprocessor and analog gates are utilized in this design to demonstrate the feasibility of implementing a flexible Telecommunication System. The microprocessor is programmed to control, through software, the system functions. The feasibility of systems highly adaptable to the needs of individual subscribers is thus demonstrated.

No description available.

Several alternate systems for use in implementing the 911 emergency system in Orange County, Florida were investigated. The proposed systems were compared with the already existing systems on the basis of reliability, response time, and cost. System reliability was the prime consideration of the study. Flow charts of each system were formulated in order to determine the functions required of equipment and humans. The reliability was then determined by examining the difficulty of the tasks that each was required to perform. Reliability models of the present and proposed systems were constructed. By examining these models and comparing the reliabilities, response times, and costs, the most advantageous system configurations were determined. Two systems, Emergency Operator Call Transfer and Emergency Operator Call Relay, were selected as the most desirable; there were no appreciable differences in their reliability, response time, and cost.