Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 152-156). Also available via the Internet.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1991. / Vita. Abstract. Includes bibliographical references (leaves 126-130). Also available via the Internet.

Thesis (M.B.A.)--University of Hong Kong, 1995. / Includes bibliographical references (leaves 116-121).

Thesis (M.B.A.)--University of Hong Kong, 1995. / Includes bibliographical references.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 128-132). Also available via the Internet.

Thesis (Ph. D.)--Northwestern University, 2003. / Includes bibliographical references (leaves 379-411).

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,<br /> 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.<br /> <br /> 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.<br /> <br /> 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<br /> from a real mobile telecommunication network.

The subject of this thesis is a group of emergent leading firms in developing countries pursuing a ‘dual frontier', achieving technology supremacy and establishing market autonomy, and entering a newly emerging market in the context of the latter half of the 20th century. Whilst the previous literature on catching-up and transition generally centres on the area of technological development of latecomer firms, this thesis extends the scope of analysis to a broader issue of technological development and marketing strategies of latecomer firms in transition. The thesis builds upon two different streams of literature: first the catching-up process in latecomer firms for the theoretical and empirical rationales, and second the boundaries of a firm and inter-firm coordination in technological frontier firms for the theoretical guidance to a systematic analysis. Using industry case studies of the Korean mobile handset manufacturers Samsung and LG Electronics, the thesis first shows that there could be windows of opportunity available for international technology transfer to emergent leading firms in the emerging stage of a new industry from competition to achieve a dominant compatibility standard among technology leaders. However, the research stresses that the characteristic of these technologies is cutting-edge but technologically incomplete and commercially unproven, which highlights the importance of previous experience and capacity for successful commercialisation. Moreover, the thesis shows that Korean firms pursuing a dual frontier overcame their uneven development between technological and marketing capabilities through intensive inter-firm collaborations with intermediary users, that is Mobile Network Operators (MNOs). In the thesis, it is stressed that Korean firms competed against technology leaders like Nokia in export markets by complementing weak marketing capabilities based on continuous collaborations with MNOs, evolving from von Hippelian to Teecean inter-firm relationships. Lastly, the thesis introduces to the literature on industry organisation a new form of an outsourcing organisation, termed a ‘contract developer' (CD), which has been identified as a group of firms that is unilaterally specialised in, and that carries out development outsourcing projects for, mobile handset Own Brand Manufacturers (OBMs). The thesis reveals that CDs emerged from the industry shake-out and the co-specialised structure between mobile handset OBMs and MNOs in the industry and served as one of main mechanisms that supported the successful globalisation of the Korean firms. Therefore, the thesis argues that the key strategy that Korean emergent leading firms adopted to compete at the world frontier can be described as a ‘quasi' extension of firm boundaries in terms of development resources (the CDs) and in terms of downstream capabilities (the MNOs).

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 122-126). Available also in a digital version from Dissertation Abstracts.

Thesis (M.S. in Computer Science)--Naval Postgraduate School, September 2003. / Thesis advisor(s): Bert Lundy, Scott Coté. Includes bibliographical references (p. 79-82). Also available online.