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Credit scoring is an important process in every financial institution and bank. Its high accuracy in classifying customers helps decrease the credit risk and increase reliability and profit. In this paper, we propose a binary classification approach that can classify customers who apply for loans. A statistical technique called Stepwise Regression (SR) is used as a pre-process to select important...
As listed firms' financial distress is not always occasional, it is necessary to consider dynamic change of the firms' financial condition when the firms' financial distress is pre-warned. In this paper, a longitudinal data envelopment analysis is employed to consider dynamically the listed firms' financial condition, and every listed firm in various periods is viewed as various decision making unit...
Developing sustainable urban environments is complex as it requires consideration of interacting social, economic and environmental sustain ability factors. The task is made even more difficult by the wide variety of stakeholders (e.g. planners, architects, businesses and the public) that may be involved in the process and the lack of a common language for all to understand. This paper describes a...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
For the past 20 years, Seattle University's Master of Software Engineering program has incorporated industry sponsored projects into its capstone course sequence. Starting in 2005 the program was expanded to leverage projects that would cut across the themes presented in various courses to enable students to experience continuity and a common pedagogical application in various topics. The projects...
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