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ABSTRACT: The purpose of this paper is to propose new evaluation criteria and an analytic hierarchy process (AHP) model to assess the expanded national immunization programs (ENIPs) and to evaluate two alternative health care policies. One of the alternative policies is that private clinics and hospitals would offer free vaccination services to children and the other of them is that public health centers would offer these free vaccination services. Our model to evaluate the ENIPs was developed using brainstorming, Delphi techniques, and the AHP model. We first used the brainstorming and Delphi techniques, as well as literature reviews, to determine 25 criteria with which to evaluate the national immunization policy; we then proposed a hierarchical structure of the AHP model to assess ENIPs. By applying the proposed AHP model to the assessment of ENIPs for Korean immunization policies, we show that free vaccination services should be provided by private clinics and hospitals rather than public health centers.
Vaccine 12/2008; 27(5):792-802. · 3.77 Impact Factor
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ABSTRACT: Information technology and the Internet have been major drivers for changes in all aspects of business processes and activities. They have brought major changes to the financial statements audit environment as well, which in turn has required modifications in audit procedures. There exist certain difficulties, however, with current audit procedures especially for the assessment of the level of control risk. This assessment is primarily based on the auditors' professional judgment and experiences, not on objective rules or criteria.To overcome these difficulties, we propose a prototype decision support model named CRAS-CBR using case-based reasoning to support auditors in making their professional judgment on the assessment of the level of control risk of the general accounting system in the manufacturing industry.To validate the performance, we compare our proposed model with benchmark performances in terms of classification accuracy for the level of control risk. Our experimental results show that CRAS-CBR outperforms a statistical model and staff auditor performance in average hit ratio.
Expert Systems 01/2004; 21(1):22 - 33. · 0.68 Impact Factor
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ABSTRACT: Data filtering methods are so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. In particular, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters without frequency information. We study the issues of integrated methods of joint time frequency analysis and neural network techniques as the application of multi-cyclic decomposition methods to the neural networks for short-term point forecast decision making The issues include the appropriate selection of neural network model architecture, for example, what type of neural network learning architecture is selected and what input size should be selected for our time series forecasting. We analyze these problems in particular with recurrent neural network learning and embedding dimension as chaos analysis. This study is also applied to a case study of daily Korean won/U.S. dollar exchange returns. Finally we suggest an integration framework for future research from our experimental results.
System Sciences, 2000. Proceedings of the 33rd Annual Hawaii International Conference on; 02/2000
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ABSTRACT: Detecting the features of significant patterns from their own historical data is crucial in obtaining optimal performance, especially in time series forecasting. Wavelet analysis, which processes information effectively at different scales, can be very useful in accomplishing this. One of the most critical issues to be solved in the application of wavelet analysis is to choose the correct filter types and filter parameters. If the threshold is too small or too large, the wavelet shrinkage estimator will tend to overfit or underfit the data. The threshold is often selected arbitrarily or by adopting certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced to solve that problem. In this study, we propose an integrated thresholding design of the optimal wavelet transform by genetic algorithms (GAs) to represent a significant signal that is most suitable in neural network models, especially for use in chaotic financial markets. The results show that a hybrid system using GAs has better performance than any other method
Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on; 02/1999
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New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, 7th International Workshop, RSFDGrC '99, Yamaguchi, Japan, November 9-11, 1999, Proceedings; 01/1999
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ABSTRACT: Detecting the features of significant patterns from historical data is crucial for good performance in time-series forecasting. Wavelet analysis, which processes information effectively at different scales, can be very useful for feature detection from complex and chaotic time series. In particular, the specific local properties of wavelets can be useful in describing the signals with discontinuous or fractal structure in financial markets. It also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. However, one of the most critical issues to be solved in the application of the wavelet analysis is to choose the correct wavelet thresholding parameters. If the threshold is small or too large, the wavelet thresholding parameters will tend to overfit or underfit the data. The threshold has so far been selected arbitrarily or by a few statistical criteria.This study proposes an integrated thresholding design of the optimal or near-optimal wavelet transformation by genetic algorithms (GAs) to represent a significant signal most suitable in artificial neural network models. This approach is applied to Korean won/US dollar exchange-rate forecasting. The experimental results show that this integrated approach using GAs has better performance than the other three wavelet thresholding algorithms (cross-validation, best basis selection and best level tree).
Expert Systems with Applications.
