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      • A Prediction Model to Support a MultilayerNetwork-based Universal Electronic CashTransaction Framework

        Fahim Irfan Alam,Muhammad Iqbal Hasan Chowdhury,Md. Reza Rabbani 한국산학기술학회 2013 SmartCR Vol.3 No.4

        Tremendous developments in the telecommunications sector in Bangladesh has been experienced recently and with this overwhelming success, each and every part of Bangladesh is now under cellular coverage. This success motivated us to develop a more reliable and transparent economic infrastructure through the use of cellular services. We proposed a multilayer network-supported framework that completely eliminates the need for paper money for all kinds of economic transactions. This paper proposes an extension of the framework by introducing embedded intelligence through the use of machine learning in order to facilitate the concept of data mining in our application. Because data mining is an important step in knowledge discovery, we can extract hidden and non-trivial information from raw data and predict a pattern that adds additional intelligence in our framework to make it more transparent and free of corruption. In this paper, we have utilized the significant functionalities of decision-tree classifiers in order to predict a transaction pattern for every concerned entity followed by handling all kinds of transactions by electronic means, and continuously monitoring them over the entire network. Satisfactory experimental results on an artificially created data set support the possibilities in real-world implementation.

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