• Journal of the Korean Society of Earth Science Education
• /
• v.5 no.3
• /
• pp.256-266
• /
• 2012

The purpose of this study is to use DARTs (Directed Activities Related to text) to foster inquiry problems while actively engaging accelerated gifted elementary students in the field of earth sciences. This study is continually evolving in the classroom on the proposition that accelerate the scientific thought whether inquiry problems show any change according to the extent of prior background knowledge through DARTs. Researchers appointed the accelerated gifted elementary students with 14 investigation problems and it was their duty to not only classify the inquiry problems, but to analyze using interviewing methods according to type classification framework. Many scientific terms were used concretely in the inquiry problems that were propose after DART. The students gave a direct effect to the inquiry problem to be proposed according to the level of the content that it is presented in the DARTs worksheet. As a result, the NP-IP type and the EC-IP, NC-IP inquiry problem type proposed above much as a whole in DARTs former and prior. Particularly, the EMC-IP type and etc. was variously proposed after the DARTs. And the students proposing the inquiry problem of above average proposed the inquiry problem of the EP-IP type much unlike the general average student after the DARTs. The EC-IP, NC-IP and NF-IP type were changed much after DARTs used. Particularly, the EC-IP and NC-IP type were changed much.

• Journal of KIISE:Computer Systems and Theory
• /
• v.37 no.6
• /
• pp.323-329
• /
• 2010

The edit distance problem is finding the minimum number of edit operations to transform a string into another one. It is one of the important problems in algorithm research and there are some algorithms that compute an optimal edit distance for the one-dimensional languages such as the English alphabet. However, there are a few researches to find the edit distance for the more complicated language such as the Korean or Chinese alphabet. In this paper, we define the measure of the edit distance for the Korean alphabet with the phoneme classification system to improve the previous edit distance algorithm and present an algorithm for the edit distance problem for the Korean alphabet.

• Journal of Korean Academy of Rural Health Nursing
• /
• v.15 no.2
• /
• pp.66-73
• /
• 2020

Purpose: This study was done to report nursing case for ADL improvement of elders who have CVA(Cerebrovascular Accident) sequelae. Methods: The client had registered in the C visiting nursing center after being decided a long-term care Grade 2. Data were collected through consultation logs for recipients, Activities of Daily Living (ADL) records, fall risk assessment (Huhn) sheets, decubitus ulcer risk assessment (Braden Scale) sheets, cognition assessment (K-MMSE) sheets, long-term care benefit provision records, and interviews with visiting nurse. Data were collected and analyzed according to the Omaha System problem classification. The intervention scheme and the problem rating scale for performance were applied to present the case for home-visit nursing. Results: The client registered in August, 2018, was provided home-visit nursing care once a week as of September 2020. ADL, cognitive levels and decubitus ulcer risks were found to have improved. Conclusion: This case report presents the value of classifying nursing problems and checking nursing intervention provided to patients with problems of ADL. The presentation of home-visit nursing cases applying a standardized nursing problem classification scheme for clients with various problems showed that a high quality level of care is guaranteed and evidence-based nursing can be provided by visiting nurses.

• Journal of Digital Convergence
• /
• v.19 no.12
• /
• pp.347-352
• /
• 2021

In this paper, we propose a method for finding feature subsets that are effective for classification in an input dataset by using a multi-agent reinforcement learning method. In the field of machine learning, it is crucial to find features suitable for classification. A dataset may have numerous features; while some features may be effective for classification or prediction, others may have little or rather negative effects on results. In machine learning problems, feature selection for increasing classification or prediction accuracy is a critical problem. To solve this problem, we proposed a feature selection method based on reinforced learning. Each feature has one agent, which determines whether the feature is selected. After obtaining corresponding rewards for each feature that is selected, but not by the agents, the Q-value of each agent is updated by comparing the rewards. The reward comparison of the two subsets helps agents determine whether their actions were right. These processes are performed as many times as the number of episodes, and finally, features are selected. As a result of applying this method to the Wisconsin Breast Cancer, Spambase, Musk, and Colon Cancer datasets, accuracy improvements of 0.0385, 0.0904, 0.1252 and 0.2055 were shown, respectively, and finally, classification accuracies of 0.9789, 0.9311, 0.9691 and 0.9474 were achieved, respectively. It was proved that our proposed method could properly select features that were effective for classification and increase classification accuracy.

• Journal of Agricultural Extension & Community Development
• /
• v.5 no.1
• /
• pp.31-44
• /
• 1998

An environmental problem is the important issue of mankind. It should be treated main discourse in our period. There are many assertions related to environment but they are not to be clearly classified because of miscellaneous paradigm. It is possible to classify into two category on the basis of human attitude toward nature and environmental problem. One of them is anthropocentrism and the other is ecocentrism. This classification is helpful to understand various environmental discourses. Owing to different paradigm approach, anthropocentrism and ecocentrism have different concept for environmental problem. Anthropocentrism is powerful to the real life in behalf of economic rationalism. But ecocentrism is important for the only settlement of environmental problem in ecocentric perspectives. Therefore a new scheme for environmental problem solving is necessary through combining the merits of anthropocentrism and ecocentrism.

• Korean Journal of Child Studies
• /
• v.31 no.5
• /
• pp.1-15
• /
• 2010

The purpose of study was to investigate internal and external problem behavior according to types of multiple-attachments exhibited to both mothers and teachers. The subjects were 120 preschool children (between 4 to, -5 years old), their mothers and teachers. The attachment classification of these 120 preschoolers was evaluated by an attachment Q-set. Data was statistically analyzed by means of one-way ANOVA, and the Duncan test with the SPSS Win 13.0 program. Our results were as follows, Firstly, significant differences were observed in the internal problem behavior according to types of multiple-attachments. Second, significant differences were also observed in the external problem behavior according to types of multiple-attachments. This study suggests that secure attachment relationships with teachers may compensate for insecure relationships with mothers.

• Journal of Korea Multimedia Society
• /
• v.22 no.3
• /
• pp.386-394
• /
• 2019

This research will describe algorithm for class diagram extraction from problem statements. Class diagram notation consist of class name, attributes, and operations. Class diagram can be extracted from the problem statement automatically by using Natural Language Processing (NLP). The extraction results heavily depends on the algorithm and preprocessing stage. The algorithm obtained from various sources with additional rules that are obtained in the implementation phase. The evaluation features using five problem statement with different domains. The application will capture the problem statement and draw the class diagram automatically by using Windows Presentation Foundation(WPF). The classification accuracy of 100% was achieved. The final algorithm achieved 92 % of average precision score.

• Journal of Intelligence and Information Systems
• /
• v.7 no.1
• /
• pp.149-159
• /
• 2001

There have been lots of researches for providing a personalized service to a customer using one-to-one marketing and collaborative filtering techniques in E-Commerce. However, there are technical difficulties for providing the recommendation of products far users, which often involve high complexity of computation. In this paper, we have presented an integrated method of classification problem solving method and constraint based configuration techniques. This method can reduce a complexity of computation by classifying a solution domain space that has a higher complexity of composition. Thereafter, we have modeled customers constraints and the components of products to configure a complete system by passing it to constraint processing module in Constraint Satisfaction Problems. Constraint-based configuration uses the constraint propagation using the constraints of buyers and the constraints among PC components to configure a proper product for a customer. We have transformed and applied vector space modeling method in the field of information retrieval to consider a customer satisfaction in addition to the CSP. Finally, we have applied our system to test data fur evaluating a customers satisfaction and performance of the proposed system.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.9 no.3
• /
• pp.73-84
• /
• 2010

This study presents a novel method to identify hazardous segments of freeway using global positioning system(GPS) based probe vehicle data. A variety of candidate contributing factors leading to higher potential of accident occurrence were extracted from the probe vehicle dataset. The research problem was defined as a classification problem, then a well-known classifier, bayesian neural network was adopted to solve the problem. A binary logistic regression technique was also used for selecting salient input variables. Test results showed that the proposed method is promising in extracting hazardous freeway sections. The outcome of this study will be effectively used for evaluating the safety of freeway sections and deriving countermeasures to prevent accidents.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.1
• /
• pp.46-51
• /
• 2004

his paper presents a novel design technique for the TS fuzzy classifier via linear matrix inequalities(LMI). To design the TS fuzzy classifier built by the TS fuzzy model, the consequent parameters are determined to maximize the classifier's performance. Differ from the conventional fuzzy classifier design techniques, convex optimization technique is used to resolve the determination problem. Consequent parameter identification problems are first reformulated to the convex optimization problem. The convex optimization problem is then efficiently solved by converting linear matrix inequality problems. The TS fuzzy classifier has the optimal consequent parameter via the proposed design procedure in sense of the minimum classification error. Simulations are given to evaluate the proposed fuzzy classifier; Iris data classification and Wisconsin Breast Cancer Database data classification. Finally, simulation results show the utility of the integrated linear matrix inequalities approach to design of the TS fuzzy classifier.