• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.401-404
• /
• 2010

Researches on prodigies and education for those have recently been progressing in many fields. Education for the gifted, which was basically on Math and Science on the start, now includes Intelligence, Invention, Cultural Sciences, Art, and so on. With the progression towards extremely developed information society, interests in and importance on the courses for the talented get more and more focused. The problem is, however, choosing the gifted and educating them is not an easy matter, since the history of Intelligence Technology is relatively short and it is hard to identify prodigies and categorize what kinds of courses they need. Also, from 2010 "Science Education Institute for the Gifted" freshmen draft, paper-based admission test has been discarded and teacher-recommendation through long-term observation introduced. Therefore needs have been increasing for quality selection methods including observation records, recommendation letters, and portfolios. Reformation on teaching and creative selection methods has been accentuated because of lack of academic base for selecting candidates for education for the gifted. Because of all those mentioned above, reliances for the selection processes during the last three years and the one in 2010, observation records, recommendations and portfolios included, have been analyzed and evaluated. Several factors which can be used instead of paper-based tests were coordinated. Based on it, it was highly possible and has been successful to draft all the applicants in cognitive, sentimental, and creative fields.

• Journal of Intelligence and Information Systems
• /
• v.29 no.1
• /
• pp.327-351
• /
• 2023

This paper aims to derive success factors that successfully lead an artificial intelligence (AI) project and prioritize importance. To this end, we first reviewed prior related studies to select success factors and finally derived 17 factors through expert interviews. Then, we developed a hierarchical model based on the TOE framework. With a hierarchical model, a survey was conducted on experts from AI-using companies and experts from supplier companies that support AI advice and technologies, platforms, and applications and analyzed using AHP methods. As a result of the analysis, organizational and technical factors are more important than environmental factors, but organizational factors are a little more critical. Among the organizational factors, strategic/clear business needs, AI implementation/utilization capabilities, and collaboration/communication between departments were the most important. Among the technical factors, sufficient amount and quality of data for AI learning were derived as the most important factors, followed by IT infrastructure/compatibility. Regarding environmental factors, customer preparation and support for the direct use of AI were essential. Looking at the importance of each 17 individual factors, data availability and quality (0.2245) were the most important, followed by strategy/clear business needs (0.1076) and customer readiness/support (0.0763). These results can guide successful implementation and development for companies considering or implementing AI adoption, service providers supporting AI adoption, and government policymakers seeking to foster the AI industry. In addition, they are expected to contribute to researchers who aim to study AI success models.

• The Journal of Korean Association of Computer Education
• /
• v.9 no.1
• /
• pp.71-80
• /
• 2006

As e-commerce developing rapidly, it is becoming a research focus about how to find customer's behavior patterns and realize commerce intelligence by use of Web mining technology. One of the most successful and widely used technologies for building personalization and goods recommendation system is collaborating filtering. However, collaborative filtering have serious data sparsity problem. Traditional association rule does not consider user's interests or preferences to provide a user with specific personalized service.In this paper, we propose an goods recommendation system, which is integrated an collaborative filtering algorithm with item-to-item corelation and an improved Apriori algorithm. This system has user's interests or preferences ro provide a user with specific personalized service.

• Journal of the Korea Institute of Building Construction
• /
• v.11 no.3
• /
• pp.238-246
• /
• 2011

Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

• Journal of Intelligence and Information Systems
• /
• v.8 no.1
• /
• pp.63-74
• /
• 2002

This paper presents an application result of on-line fault diagnosis system for glass melting furnace using a fuzzy expert system. Operators maintain the furnace using the furnace Knowledge and experience, which directly influence the furnace and glass product. Firstly, knowledge and experience is achieved and analyzed to implement the furnace Knowledge and experience into fuzzy expert system. The acquired Knowledges determined as a crisp rule or a fuzzy rule to expect its characteristics. And, a linear regression is used as the input of fuzzy rule to consider the exact knowledge of human operator. The fuzzy expert system is implemented with G2 which is an on-line expert system tool of Gensym Co. The application to a production furnace of Samsung-Corning Co. in Suwon shows successful results of proposed fuzzy expert system.

• Journal of Intelligence and Information Systems
• /
• v.7 no.1
• /
• pp.47-61
• /
• 2001

Lazy loaming methods including CBR have relative advantages in comparison with eager loaming methods such as artificial neural networks and decision trees. However, they are very sensitive to irrelevant features. In other words, when there are irrelevant features, larry learning methods have difficulty in comparing cases. Therefore, their performance can be degraded significantly. To overcome this disadvantage, feature weighting methods for lazy loaming methods have been studied. Most of the existing researches, however, were focused on global feature weighting. In this research, we propose a new local feature weighting method, which we shall call CBDFW. CBDFW stores classification performance of randomly generated feature weight vectors. Then, given a new query case, CBDFW retrieves the successful feature weight vectors and designs a feature weight vector fur the query case. In the test on credit evaluation domain, CBDFW showed better classification accuracy when compared to the results of previous researches.

• Korean Management Science Review
• /
• v.17 no.3
• /
• pp.19-30
• /
• 2000

Customization and personalization services are considered as a critical success factor to be a successful Internet store or web service provider. As a representative personalization technique, personalized recommendation techniques are studied and commercialized to suggest products or services to a customer of Internet storefronts based on demographics of the customer or based on an analysis of the past purchasing behavior of the customer. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customers data. however, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed knowledge base. In this paper, we proposed a marketing rule extraction technique for personalized recommendation on Internet storefronts using market basket analysis technique, a well-known data mining technique. Using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store. An experiment has been performed to evaluate the effectiveness of proposed approach comparing with preference scoring approach and random selection.

• Journal of Information Technology Services
• /
• v.7 no.3
• /
• pp.71-85
• /
• 2008

The rapid growth of blog has caused information overload where bloggers in the virtual community space are no longer able to effectively choose the blogs they are exposed to. Recommender systems have been widely advocated as a way of coping with the problem of information overload in e-business environment. Collaborative Filtering (CF) is the most successful recommendation method to date and used in many of the recommender systems. In this research, we propose a CF-based recommender system for bloggers to find their similar bloggers or preferable virtual community without burdensome search effort. For such a purpose, we apply the "Interest Value" to CF recommender systems. The Interest Value is the quantity value about users' transaction data in virtual community, and can measure the opinion of users accurately. Based on the Interest Value, the neighborhood group is generated, and virtual community list is recommended using the Community Likeness Score (ClS). Our experimental results upon real data of Korean Blog site show that the methodology is capable of dealing with the information overload issue in virtual community space. And Interest Value is proved to have the potential to meet the challenge of recommendation methodologies in virtual community space.

• Journal of Korean Academy of Nursing
• /
• v.49 no.6
• /
• pp.771-782
• /
• 2019

Purpose: To determine the effects of a child and parent program on developing social skills for preventing violent behaviors in children aged 60~72 months through a specially developed pre and posttest, control group, quasi-experimental study. Methods: A social skills development program based on Gardner's Multiple Intelligence Theory was used. The data were collected using the Social Skills Assessment Scale (SSAS), a Chart to Monitor Verbal and Behavioral Violence in Children, the Parental Attitude Scale and the Parent Interview Form. This quasi-experimental study that included a pretest, posttest, and control group had a sample comprising 67 children and parents, with 36 in the experimental group, and 31 in the control group. Results: Over a six-month period, while the social skill scores of the children in the experimental and control groups increased, their violent behaviors decreased (p<.050). Increase in social skill scores and decrease in violent behaviors were higher in the experimental than in the control group children (p<.050). The parents in the experimental group stated that they had started to empathize with their children, using "I" language, and applied rules more consistently after the program. Conclusion: This program was successful in preventing violent behaviors in children through the development of social skills. Hence, it can be effectively implemented through a teacher/nurse collaboration.

• The Journal of Korean Association of Computer Education
• /
• v.8 no.2
• /
• pp.33-40
• /
• 2005

It has been found that the learning method based on conventional CAI(Computer Assisted Instruction) to be inadequate and inefficient as it is designed without considering the individual learning characteristics of the learners. In order to rectify and remedy the problem, the development of an ITS(Intelligent Tutoring System) that is adequately equipped with an artificial intelligence that successfully interprets the individual learning ability characteristics through accumulated individual data is in order. This study attempts to verify the individual acquisition ability and the possible error committed by learners in the process of learning in order to present the elements to be considered for designing a successful student module that enables the effective learning through the 'learner ability grouping' for learning Electronic Calculator Architecture.