• Journal of the military operations research society of Korea
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• v.30 no.2
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• pp.122-132
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• 2004

The aim of this paper is to show the process of R&D Project-Priority Ordering in the Defense Technology field. We propose specific methods (Delphi, Analytic Hierarchy Process (AHP), Scoring) to order the R&D Project-Priority. In general, to decide the priority of R&D projects most of researches depend on questionnaires which are surveyed by experts. However, it is criticized that this process cannot reflect the limitation of experience and knowledge of experts. In this process, we separate evaluators in two parts: the first is strategic experts, the second is technical experts. Evaluators can choose and evaluate the alternatives which they are familiar with, so we can obtain reliable results. Finally, based on our process of the R&D Project-Priority Ordering we formulate policy implications for managing the defense technology.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.227-249
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• 2003

Prediction of corporate failure using past financial data is a well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as model construction process. Irrespective of the efficiency of a teaming procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network model. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables fur neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.