• The Mathematical Education
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• v.45 no.3 s.114
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• pp.295-313
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• 2006

University teacher education programs have sought for ways of how to improve student teaching in order to supply mathematics teachers with practical theory to achieve the goals of the current educational reform in school mathematics. In this context, the purpose of this research is to investigate the effect of student teachers' teaching experience in the after-school mathematics programs and the ways of how to develop the after-school learning programs as an effective site for learning to teach based on the inquiry into student teachers' own teaching experience. For the purpose, data were collected through the interviews with the student teachers who had taught after-school mathematics class. In addition, data were collected through survey, class observation, and seminal meetings with the student teachers in order to supplement the findings from the interview analysis. Data analysis focused on the student teachers' experience with teaching in after-school mathematics classes, that is, what and how they had learned as teachers, what kinds of difficulties they encountered in their teaching and supports that they expect to improve their learning through teaching. The analysis shows that the teaching experience in the after-school programs had positively contributed to their development as future mathematics teachers. Specifically, the after-school programs provide the site for learning through teaching at the early stage of teacher education program. The after-school programs provided the students teachers for the opportunity to participate peripherally in educational practice of school. Through the participation, the student teachers developed positive attitudes toward teaching career and became to have more solid ideas about how to teach mathematics. Based on the analysis, this research provides following suggestions concerning how to improve student teaching. First, it is necessary to provide student teachers to participate into the practice of teaching at the early stage of teacher education programs. Second, it is important to give students teacher opportunity to participate in teaching at peripheral and legitimate positions. Finally, it is necessary to construct mentoring networks to support student teachers to move from a peripheral position toward a center of teaching practice.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.169-186
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• 2023

The research aimed to identify the effectiveness of an educational program using 3D glasses as a technological innovation on academic achievement and attitude towards elearning in science in the preparatory stage. The research relied on the analytical descriptive approach and the semi-experimental approach. The research tools were the achievement test and the scale of attitude towards e-learning. An educational program was designed and produced using 3D glasses. The study sample consisted of 60 students from the second grade in the preparatory stage at the Rural Jeddah School. The research concluded to the following results: There was a satistically sigificant difference at the level of sig. (0.05) among the -mean scores of the experiemtal and control group students in the post assessment atthe level of achievement in favor of the experiemental group and therewas a satistically sigificant difference at the level of sig. (0.05) among mean scores of the experiemtal and control group students in the post assessment at the level of attitude towards e-learning in favor of the experiemental group. And it was found that the positive effect of the 3D educational program for improving the level of achievement and the attitude towards e-learning for the students. The program allowed the experimental group students to practice self-learning, interaction, and achievement according to the individual differences among them.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.678-700
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• 2023

Diagnosing breast diseases using ultrasound (US) images remains challenging because it is time-consuming and requires expert radiologist knowledge. As a result, the diagnostic performance is significantly biased. To assist radiologists in this process, computer-aided diagnosis (CAD) systems have been developed and used in practice. This type of system is used not only to assist radiologists in examining breast ultrasound images (BUS) but also to ensure the effectiveness of the diagnostic process. In this study, we propose a new approach for breast lesion localization and segmentation using a multi-scale pyramid of the ultrasound image of a breast organ and a convolutional semantic segmentation network. Unlike previous studies that used only a deep detection/segmentation neural network on a single breast ultrasound image, we propose to use multiple images generated from an input image at different scales for the localization and segmentation process. By combining the localization/segmentation results obtained from the input image at different scales, the system performance was enhanced compared with that of the previous studies. The experimental results with two public datasets confirmed the effectiveness of the proposed approach by producing superior localization/segmentation results compared with those obtained in previous studies.

• Nutrition Research and Practice
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• v.17 no.6
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• pp.1255-1266
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• 2023

BACKGROUND/OBJECTIVES: This study aimed to predict the association between nutritional intake and diabetes mellitus (DM) by developing an artificial neural network (ANN) model for older adults. SUBJECTS/METHODS: Participants aged over 65 years from the 7th (2016-2018) Korea National Health and Nutrition Examination Survey were included. The diagnostic criteria of DM were set as output variables, while various nutritional intakes were set as input variables. An ANN model comprising one input layer with 16 nodes, one hidden layer with 12 nodes, and one output layer with one node was implemented in the MATLAB^® programming language. A sensitivity analysis was conducted to determine the relative importance of the input variables in predicting the output. RESULTS: Our DM-predicting neural network model exhibited relatively high accuracy (81.3%) with 11 nutrient inputs, namely, thiamin, carbohydrates, potassium, energy, cholesterol, sugar, vitamin A, riboflavin, protein, vitamin C, and fat. CONCLUSIONS: In this study, the neural network sensitivity analysis method based on nutrient intake demonstrated a relatively accurate classification and prediction of DM in the older population.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.751-760
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• 2021

Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.315-321
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• 2022

Many students with disabilities and special needs are enrolled in higher education, which substantiated the need for research regarding faculty members' knowledge and willingness to implement supportive strategies in higher education in Saudi Arabia. This study explored Saudi university faculty members' knowledge and willingness to apply UDL (Universal Design for Learning) principles in their teaching practice. Surveys were used for data collection for this descriptive research. The findings indicated faculty members felt that they were knowledgeable regarding UDL and were willing to use UDL principles in teaching their students. Furthermore, there were no statistically significant differences between faculty members' knowledge levels regarding UDL based on their current position and years of experience. The findings indicated there was a significant relationship between gender and knowledge, with males having a significantly higher mean knowledge, although further analyses revealed it was a small effect. Finally, the results suggest more years of experience are related to greater willingness to use UDL principles, and this is particularly true for those in a lecturing position. These findings could be helpful, particularly for the Ministry of Education in Saudi Arabia to shed light on faculty members' UDL knowledge. Further research is needed to substantiate the findings.

• Knowledge Management Research
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• v.19 no.1
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• pp.97-118
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• 2018

The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.463-469
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• 2023

The aim of this study is to provide basic data that can contribute to improving successful clinical adaptation and reducing turnover of new graduate nurses by analyzing research related to reality shock experienced by new graduate nurses using text network analysis. The topics of reality shock experienced by new graduate nurses were extracted from 115 papers published in domestic and foreign journals from January 2002 to December 2021. Articles were retrieved from 6 databases (Korean DB: DBpia, KISS, RISS /International DB: Web of science, Springer, Scopus). Keywords were extracted from the abstract and organized using semantic morphemes. Network analysis and topic modeling for subject knowledge structure analysis were performed using NetMiner 4.5.0 program. The core keywords included 'new graduate nurses', 'reality shock', 'transition', 'student nurse', 'experience', 'practice', 'work environment', 'role', 'care' and 'education'. In recent articles on reality shock experienced by new graduate nurses, three major topics were extracted by LDA (Latent Dirichlet Allocation) techniques: 'turnover', 'work environment', 'experience of transition'. Based on this research, the necessity of interventional research that can effectively reduce the reality shock experienced by new graduate nurses and successfully help clinical adaptation is suggested.

• Journal of Korean Society of Forest Science
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• v.104 no.4
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• pp.615-621
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• 2015

This study was conducted to analyze accessibility and reduction effect of forest operations cost before and after forest road construction based on actual silvicultural operations in 5 regional forest service(8 management planning district) where forest operations was enacted consistently. The result show the accessibility and the reduction effect of the operation cost, the accessibility of forest operation area within 500 m from forest road or public road increased about 3.2 times after the forest road construction. The reduction effect of the operation cost was averagely 576,000 won/km/year. According to the results, forest road had an effect on improvement of accessibility and reduction effect of forest operation cost. This result could be offered basic information to support policy of forest road expansion.

• Journal of Distribution Research
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• v.11 no.3
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• pp.55-78
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• 2006

In business-to-business setting, dyadic relationships between firms are of paramount interest. Recent developments in business practice strongly suggest that to understand these business relationships, greater attention must be directed to the embedded context within which dyadic business relationships take place. As a result, it is important to develop an understanding which is to move from dyadic business relationship to business networks. The purpose of the current study is (1) to identify types of network benefits: information acquisition, access to tangible resources, cooperative support from network members, (2) to model network benefit efforts on a customer orientation for customer firm, (3) to examine the relationship between network characteristics and network benefits. Adopting resource-based view and social capital theory, the conceptual model is proposed. In order to develop and test a model, this paper was empirically to examine how network characteristics can reinforce network benefits leading to focal firm's customer orientation using 123 firms. Through structural equation modeling, this research found that (1) density influences information acquisition, access to tangible resources, cooperative support from network members, (2) centrality increases information acquisition, access to tangible resources. We also find that information acquisition, access to tangible resources, cooperative support from network members enhances focal firm's customer orientation. Finally, We discuss several theoretical and practical implications, and suggest limitations for the research and future research issue.