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ABSTRACT: This study proposes a new and highly efficient dynamic combinatorial auction mechanism--the N-bilateral optimized combinatorial auction (N-BOCA). N-BOCA is a flexible iterative combinatorial auction model that offers more optimized trading for multiple suppliers and purchasers in the supply chain than one-sided combinatorial auction. We design the N-BOCA model from the perspectives of market architecture, trading rules, and decision strategy for winner determination, the decision strategy for winner determination needs flexible optimization modeling capability. Thus rule-based reasoning was applied for reflecting the flexible decision strategies. We also show the viability of N-BOCA through Paired Samples T-test experimentation. It shows that N-BOCA yields higher purchase efficiency and effectiveness than the one-auctioneer to multi-bidders (1-to-N) combinatorial auction mechanism.
Omega. 01/2009; 37(2):482-493.
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Expert Syst. Appl. 01/2008; 34:150-158.
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Expert Syst. Appl. 01/2008; 34:1583.
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Expert Syst. Appl. 01/2007; 33:984-995.
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Expert Syst. Appl. 01/2007; 33:181-191.
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ABSTRACT: Integration of machine and human knowledge is more effective rather than a single kind of knowledge in solving unstructured
problems. This paper proposes the knowledge integration of machine and human knowledge to achieve a better reasoning performance
in the stock price index prediction problem. Causal model and the evaluation by experts generate the machine and human knowledge
about the stock price index of next month, respectively. The machine and human knoledge are integrated by fuzzy logic-driven
framework to generate the integrated knowledge. The conflicts among the integrated knowledge are solved by fuzzy rule base.
The experimental results show that the propsed knowledge integrtion significantly improves the reasoning performance.
11/2006: pages 260-271;
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ABSTRACT: Understanding user acceptance of the Internet, especially the usage intention of virtual communities, is important in explaining the fact that virtual communities have been growing at an exponential rate in recent years. This paper studies the trust of virtual communities to better understand and manage the activities of E-commerce. A theoretical model proposed in this paper is to clarify the factors as they are related to the Technology Acceptance Model. In particular the relationship between trust and Intentions is hypothesized. Using the Technology Acceptance Model, this research showed that the importance of trust in virtual communities. According to the research, different ways of stimulating the members are necessary in order to facilitate participation in activities of virtual communities. The effect of trust in members on intention to use is stronger than that of trust in service providers. The intention to purchase is more sensitive to trust in service providers than trust in members.
System Sciences, 2006. HICSS '06. Proceedings of the 39th Annual Hawaii International Conference on; 02/2006
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Expert Syst. Appl. 01/2006; 31:241-247.
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Expert Systems. 01/2006; 23:290-301.
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Expert Systems. 01/2006; 23:127-144.
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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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Int. Syst. in Accounting, Finance and Management. 01/2004; 12:43-60.
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Computational and Information Science, First International Symposium, CIS 2004, Shanghai, China, December 16-18, 2004, Proceedings; 01/2004
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ABSTRACT: We propose the rough set approach to the extraction of trading rules for discriminating between bullish and bearish patterns in the stock market. Rough set theory is quite valuable for extracting trading rules because it can be used to discover dependences in data while reducing the effect of superfluous factors in noisy data. In addition, it does not generate a signal to trade when the pattern of the market is uncertain because the selection of reducts and the extraction of rules are controlled by the strength of each reduct and rule. The experimental results are encouraging and show the usefulness of the rough set approach for stock market analysis with respect to profitability.
Expert Systems 08/2001; 18(4):194 - 202. · 0.68 Impact Factor
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ABSTRACT: Suggests a new clustering forecasting system to integrate change-point detection and artificial neural networks. The basic concept of the proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to involve them in the forecasting model. The proposed models consist of two stages. The first stage (the clustering neural network modeling stage) detects successive change points in a data set and forecasts the change-point group with backpropagation neural networks (BPN). In this stage, three change-point detection methods are applied and compared: (1) the parametric method, (2) the nonparametric approach, and (3) the model-based approach. The next stage is to forecast the final output with a BPN. Through an application to financial economics, we compare the proposed models with a neural network model alone and, in addition, determine which of the three change-point detection methods can perform better. This article then examines the predictability of the integrated neural network model based on change-point detection.
System Sciences, 2001. Proceedings of the 34th Annual Hawaii International Conference on; 02/2001
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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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Telecommunication Systems. 01/2000; 14:331-337.
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IRMJ. 01/2000; 13:25-33.
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ABSTRACT: Advances in EDI (Electronic Data Interchange) demand appropriate
controls in order to realize the potential benefits from it. Formal,
informal, and automated controls are basic parts of EDI controls. The
state of one of three controls is suggested to affect performance
indirectly through their effect on another controls in the research
model. The causal relationships are tested using structural equation
modeling approach with LISREL. Informal controls turn out to play an
important role in the causal relationships, as they significantly affect
formal and automated controls to have indirect effect on performance.
The results of the study indicate that the interrelationships among
controls are closely related to system performance
System Sciences, 1999. HICSS-32. Proceedings of the 32nd Annual Hawaii International Conference on; 02/1999
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ABSTRACT: We propose a knowledge integration mechanism that yields a
cooperated knowledge by integrating user knowledge, expert knowledge and
machine knowledge within the fuzzy logic-driven framework, and then
refines it with a fuzzy associative memory (FAM) to enhance the
reasoning performance. The proposed knowledge integration mechanism is
applied for the prediction of Korea stock price index (KOSPI).
Experimental results show that the FAM-driven approach can enhance the
reasoning performance by refining the cooperated knowledge of fuzzy
logic-driven framework. This result means that the FAM-driven approach
can be a robust guidance for knowledge integration
Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE '99. 1999 IEEE International; 02/1999
