• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.413-420
• /
• 2022

The review of known methods and decision support systems regarding the possibility of using the reproductive technologies showed that currently there are no methods and decision support systems regarding the possibility of using reproductive technologies taking into account civil law. Although the analyzed methods and systems have great potential for use in different contexts, these methods and systems do not take into account the civil law requirements of any country. The paper has developed a decision support system regarding the possibility of using the reproductive technologies taking into account civil law, which automatically and free of charge determines the possibility/impossibility of surrogate motherhood or in vitro fertilization. If it is determined that surrogate motherhood or in vitro fertilization is impossible, the sufficiency of the information in the analyzed contract is evaluated, and the reasons for the impossibility of surrogate motherhood or in vitro fertilization are presented to the user.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.11a
• /
• pp.19-22
• /
• 2000

In this paper, we present forecasting ozone concentration with decision support system. Forecasting ozone concentration with decision support system is acquired to information from human knowledge and experiment data. Fuzzy clustering method uses the acquisition and dynamic polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation and self-organization.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.4
• /
• pp.817-824
• /
• 2003

We employed a hierarchical document classification method to classify a massive collection of electronic medical records(EMR) written in both Korean and English. Our experimental system has been learned from 5,000 records of EMR text data and predicted a newly given set of EMR text data over 68% correctly. We expect the accuracy rate can be improved greatly provided a dictionary of medical terms or a suitable medical thesaurus. The classification system might play a key role in some clinical decision support systems and various interpretation systems for clinical data.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.11a
• /
• pp.357-358
• /
• 2022

이 논문은 Deep Learning 을 이용해 의사의 진단의 도움을 줄 수 있는 Clinic Decision Support System(CDSS) Framework 를 제안한다. 당뇨병, 고혈압, 고지혈증 같은 대사질환은 증상이 있는 경우도 있지만 없는 경우가 대부분이다.[1] 그렇기 때문에 원격으로 진료할 경우 대사질환에 대한 부분을 놓칠 수 있다. 이러한 부분을 챗봇이 의사에게 Deep Neural Network(DNN)으로 예측된 정보를 제공해 도움을 준다.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.361-371
• /
• 1999

This paper is concerned with designing and implementing the Internet shopping mall by using virtual reality-driven avatar and web decision support system. Traditionally, the Internet shopping mall has been designed based on the combination of several hyperlinks, images, and tents. However, this sort of approach results in a lower performance because possible customers cannot make more accurate shopping decisions. To overcome this kind of pitfalls facing the current Internet shopping malls, we propose using a combination of virtual reality and web DSS. The main virtues of our proposed approach to designing the Internet shopping mall are as follows: First, the virtual reality technique is emerging as one of alternatives guaranteeing a sense of reality for customers' part and facilitating the complex process of shopping decision makings. Especially, the avatar, which is an artificially designed man working on the Internet, can make easy and absorbing the Internet shopping-related decision making processes. Second, the web DSS approach can provide an effective decision support mechanism for customers. Especially, we design a set of intelligent agents for the proposed web DSS. Experimental results with an illustrative example showed that our proposed approach can yield a new Internet shopping mall paradigm with which customers can benefit from a high level of decision support functions.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.03a
• /
• pp.361-371
• /
• 1999

This paper is concerned with designing and implementing the Internet shopping mall by using virtual reality-driven avatar and web decision support system. Traditionally, the Internet shopping mall has been designed based on the combination of several hyperlinks, images, and texts. However, this sort of approach results in a lower performance because possible customers cannot make more accurate shopping decisions. To overcome this kind of pitfalls facing the current Internet shopping malls, we propose using a combination of virtual reality and web DSS. The main virtues of our proposed approach to designing the Internet shopping mall are as follows: First, the virtual reality technique is emerging as one of alternative guaranteeing a sense of reality for customers part and facilitating the complex process of shopping decision makings. Especially, the avatar, which is an artificially designed man working on the Internet, can make easy and absorbing the Internet shopping-related decision making processes. Second, the web DSS approach can provide an effective decision support mechanism for customers. Especially, we design a set of intelligent agents for the proposed web DSS. Experimental results with an illustrative example showed that our proposed approach can yield a new Internet shopping mall paradigm with which customers can benefit from a high level of decision support functions.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.23 no.6
• /
• pp.650-658
• /
• 2020

The research of the AI-based command decision support system was insufficient both quantitatively and qualitatively. In particular, in Korea, there was no research on concrete concept design at the current concept research level. This paper proposed the conceptual design of a tactical echelon command decision support system based on artificial intelligence(AI) according to the current army's doctrine of the operation process. The suggested conceptual design clarified the problem and proposed an appropriate process for design, and applied the function analysis method among rational techniques that enable conceptual design systematically.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2003.06a
• /
• pp.22-25
• /
• 2003

It is usually difficult for a single user to have all the essential knowledge on various Rapid Prototyping processes and techniques. It is therefore necessary to capture knowledge and experience of users of expert level into a decision-support system which provides quicker and more interactive way to select proper RP process and/or machine. rather than reading reports on benchmarking studies and comparing tables and graphs. In this paper two algorithms are presented, which may be used in such a decision-support system. together with its applications. The one is an extended PRES(Project Evaluation and Selection) algorithm which applies weighting factors of each attribute. The other is a LCE(Linear Confidence Equation) algorithm which is proposed to apply user's input requirements as well as weighting factors.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.11 s.176
• /
• pp.202-209
• /
• 2005

In this paper, benchmark tests of Rapid Prototyping(RP) are presented to evaluate characteristics of various RP Systems and Processes, and several decision-support systems are developed to select RP Machine/Process suitable to user's requirements. Results of the RP benchmark tests are applied to the recently developed RP machines for the purpose of analyzing attributes such as dimensional accuracy, surface roughness, build cost, build time, and etc. Decision-making support systems are also developed, which contain not only new LCE (Linear Confidence Equation) algorithm but also modified PRES and MDS algorithm. Those algorithms are proved to be effective in that reasonably acceptable results are obtained on several cases of different inputs.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2006.05a
• /
• pp.615-616
• /
• 2006

Many manufacturing companies manage their production-related data for quality management and production management. Nevertheless, production related-data should be closely related to each other Stored data is mainly used to monitor their process and products' error. In this paper, we provide an enterprise-wide production data model for decision support system and product automation. Process data, quality-related data, and test data are integrated to identify the process inter or intra dependency, the yield forecasting, and the trend of process status. In addition, it helps the manufacturing decision support system to decide critical manufacturing problems.