No abstract

Abstract_ -_ m-banking has become one of the most familiar banking service providing technologies in different western countries. Now-a-days billions of inhabitants of Bangladesh are within a network through mobile network coverage. But in the commercial sectors like banking, m-commerce technology has not been adopted broadly yet. Considering m-commerce perspective in Bangladesh a SMS based m-banking system has been proposed which is able to provide several essential banking services only sending SMS to bank server from any remote location. This proposed system is divided into five major phases: Interfacing Module, SMS Technology Adoption Module, SMS Banking Registration Module, Service Generation Module, and Data Failover Module. This system facilitates bank customers by providing four major services like balance enquiry , balance transfer between authenticated customers, DPS payment and bill payment without going to bank physically and save their precious time. At least, after evaluating each module of this developed system a satisfactory accuracy rate 93.18 % is obtained.

It is well-known that the observation of a variable in a Bayesian network can affect the effective connectivity of the network, which in turn affects the efficiency of inference.Unfortunately, the observed variables may not be known until runtime, which limits the amount of compile-time optimization that can be done in this regard. This thesis considers how to improve inference when users know the likelihood of a variable being observed. It demonstrates how these probabilities of observation can be exploited to improve existing heuristics for choosing elimination orderings for inference. Empirical tests over a set of benchmark networks using the Variable Elimination algorithm show reductions of up to 50% and 70% in multiplications and summations, as well as runtime reductions of up to 55%. Similarly, tests using the Elimination Tree algorithm show reductions by as much as 64%, 55%, and 50% in recursive calls, total cache size, and runtime, respectively. iv Acknowledgments I take much pleasure to express my profound gratitude to my supervisor Dr. Kevin Grant for his persistent and inspiring supervision and helping me learn the ABC of Bayesian Networks. Without his endless help and continuous reassurance at the most difficult times, this thesis would not have been a reality. I am indebted to him for his support, encouragement, suggestions, generosity, and the invaluable knowledge he shared with me.