• 간호행정학회지
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• 제20권2호
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• pp.227-236
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• 2014

Purpose: This study was designed to develop and evaluate the a web-based simulation program on patient rights education using integrated decision making model into values clarification for nurse students. Methods: The program was designed based on the Aless & Trollip model and Ford, Trygstad-Durland & Nelms's decision model. Focus groups interviews, surveys on learning needs for patient rights, and specialist interviews were used to develop for simulation scenarios and decision making modules. The simulation program was evaluated between May, 2011 and April, 2012 by 30 student nurses using an application of the web-based program evaluation tools by Chung. Results: Simulation content was composed of two scenarios on patient rights: the rights of patients with HIV and the rights of psychiatric patients. It was composed of two decision making modules which were established for value clarifications, behavioral objective formations, problems identifications, option generations, alternatives analysis, and decision evaluations. The simulation program was composed of screens for teacher and learner. The program was positively evaluated with a mean score of $3.14{\pm}0.33$. Conclusion: These study results make an important contribution to the application of educational simulation programs for nurse students' behavior and their decision making ability in protecting the patient rights.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1296-1301
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy -neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• 한국지능시스템학회논문지
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• 제11권7호
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• pp.633-640
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• 2001

In this paper we examine the machine learning issues raised by the domain of the Intrusion Detection Systems(IDS), which have difficulty successfully classifying intruders. There systems also require a significant amount of computational overhead making it difficult to create robust real-time IDS. Machine learning techniques can reduce the human effort required to build these systems and can improve their performance. Genetic algorithms are used to improve the performance of search problems, while data mining has been used for data analysis. Data Mining is the exploration and analysis of large quantities of data to discover meaningful patterns and rules. Among the tasks for data mining, we concentrate the classification task. Since classification is the basic element of human way of thinking, it is a well-studied problem in a wide variety of application. In this paper, we propose a classifier system based on genetic algorithm, and the proposed system is evaluated by applying it to IDS problem related to classification task in data mining. We report our experiments in using these method on KDD audit data.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 추계학술대회 학술발표 논문집 제14권 제2호
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• pp.303-308
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• 한국가정과교육학회지
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• 제14권2호
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• pp.79-95
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• 2002

The Purposes of this study are first to develop the teaching and learning plan for the dress part in high school's Home Economics by the application of the values clarification theory then to apply it to the classroom activities. and lastly to analyze its effects. We developed the master plan for teaching and learning, and developed the 12 hour sub plans including 7 activities and learning materials. The effects of the teaching are as followings: First, When the self-esteem was compared with the whole classes, there was no difference between the twos, but a boy and a girl who were observed as not making a value-oriented life marked higher score in answering the self-esteem. Second. It was found that values clarification theory made student's degree of participation and interest higher and helped them to choose their dresses in the real life.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.156-166
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• 2020

In this paper, to improve spectral efficiency and mitigate interference in coordinated millimeter-wave systems, we proposes an optimal user group and beam selection scheme. The proposed scheme improves spectral efficiency by mitigating intra- and inter-cell interferences (ICI). By examining the effective channel capacity for all possible user combinations, user combinations and beams with minimized ICI can be selected. However, implementing this in a dense environment of cells and users requires highly complex computational abilities, which we have investigated applying multiclass classifiers based on machine learning. Compared with the conventional scheme, the numerical results show that our proposed scheme can achieve near-optimal performance, making it an attractive option for these systems.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.214-222
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• 2021

Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure.

• Educational Technology International
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• 제7권1호
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• pp.99-110
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• 2006

Rapid innovation in ICT is transforming the way we work, the way we interact, the way we learn, and the way we live. In the education and training sector, ICT increases access to learning by making it possible for workers to fit their education into family and work schedules and by providing a greater programmatic choice of quality courses. ICT allows multiple workers to simultaneously enrol in training programs and work in their workplace in order to achieve their particular learning goals in a timelier manner. This paper deals with the ICT conditions, role of ICT, application of ICT, and effectiveness of ICT in the area of workforce development.

• International Journal of Oral Biology
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• 제48권4호
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• pp.45-49
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• 2023

Digital dentistry has witnessed significant advancements in recent years, driven by extensive research following the introduction of cutting-edge technologies such as CAD/CAM and 3D oral scanners. Until now, 2D images obtained via x-ray or CT scans were critical to detect anomalies and for decision-making. This review describes the main principles and applications of supervised, unsupervised, and reinforcement learning in medical applications. In this context, we present a diverse range of artificial intelligence networks with potential applications in dentistry, accompanied by existing results in the field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.