• Imaging Science in Dentistry
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• v.51 no.3
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• pp.237-242
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• 2021

Purpose: The aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords(deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed. Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects. Conclusion: Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.11
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• pp.155-162
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• 2019

The construction industry is the highest safety accident causing industry as 28.55% portion of all industries' accidents in Korea. In particular, falling is the highest accidents type composed of 60.16% among the construction field accidents. Therefore, we analyzed the factors of major disaster affecting the fall accident and then derived feature importances by considering various variables. We used data collected from Korea Occupational Safety & Health Agency (KOSHA) for learning and predicting in the proposed model. We have an effort to predict the degree of occupational fall accidents by using the machine learning model, i.e., Adaboost, short for Adaptive Boosting. Adaboost is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance. Decision trees were combined with AdaBoost in this model to predict and classify the degree of occupational fall accidents. HyOperpt was also used to optimize hyperparameters and to combine k-fold cross validation by hierarchy. We extracted and analyzed feature importances and affecting fall disaster by permutation technique. In this study, we verified the degree of fall accidents with predictive accuracy. The machine learning model was also confirmed to be applicable to the safety accident analysis in construction site. In the future, if the safety accident data is accumulated automatically in the network system using IoT(Internet of things) technology in real time in the construction site, it will be possible to analyze the factors and types of accidents according to the site conditions from the real time data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.170-180
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• 2011

The purposes of this study are to find out the effectiveness of the tutoring program and the level of self-regulated learning ability in tutoring program attending students. This study has been performed for team-tutoring program attending students(n=183) who are in 15 department, D college in Daegu. Following results were drawn thought correlation analyses of variables obtained during the survey period. The results were as follows; There was statistically significant difference among perception controlling ability, purpose controlling ability, and action controlling ability. In over 20 ages, organization, meta-perception stratagem, arrangement, self-effectiveness, controlling learning time ability were higher than of lower 20 ages(p<0.05). In tutors, demonstration, organization, meta-perception stratagem, arrangement, checking, purpose intentions, self- effectiveness, achievement, controlling action, help requirement ability were higher than tutees(p<0.05). As results, the tutoring program was effective for both tutors and tutees in college students. Further studies in an education program for students in all years should be implemented to examine tutoring effects. Implementation of tutoring should address the frustrations and difficulties encountered by the students to facilitate better outcomes.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.640-650
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• 2016

This study was conducted in order to develop an education program based on simulation with problem-based learning, to apply it to nursing students, and to examine its effects on the students' learning motivation, learning strategy, and academic achievement. The subjects of this study were 69 seniors majoring in nursing. Education based on simulation with problem-based learning was applied to the students from September to October in 2015, and then a questionnaire survey was conducted on their learning motivation, learning strategy, and academic achievement. According to the results of this study, the education based on simulation with problem-based learning reduced the nursing students' other-directed motivation (external motivation), increased their self-regulation motivation (identified motivation, intrinsic motivation), and improved their use of resource management strategies. In addition, academic achievement (academic performance, and educational satisfaction) was in a positive correlation with identified motivation and learning strategies (cognitive strategy, meta cognitive strategy, and resource management strategy). In conclusion, education based on simulation with problem-based learning was found to be an effective education strategy for enhancing nursing students' autonomous motivation and improving their use of resource management strategies. Thus, it is necessary to promote the application of simulation with problem-based learning in various care situations and to study factors and parameters influencing learning related variables.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.373-379
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• 2021

This study intends to test the effects of blended learning and case based learning on learning strategies, critical thinking disposition and academic self-efficacy for undergraduate nursing students. A one group pre-post design was applied to adult nursing of 23 nursing students. Data were collected between March 2 and April 30, 2021. Data were analyzed by using SPSS/WIN 23.0. The results showed that learning strategies(t=-2.43, p=.019) and sub-factor cognitive strategy (t=-2.22, p=.031), meta cognitive strategy(t=-2.59, p=.013) and resource management strategy (z=-2.46, p=.014) were significantly higher than levels before blended learning and case based learning. Critical thinking disposition(t=-1.14, p=.262) and academic self-efficacy(t=-.34, p=.734) were higher than levels before but was no significantly. In conclusion, It was confirmed that blended learning and case based learning is an effective educational program that improves learning strategies of nursing students. In the future, it is necessary to develop a program in which blended learning and case based learning can improve critical thinking disposition and academic self-efficacy, and to verify the effectiveness.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.427-432
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• 2012

The article herein suggested a compound repository and a descriptive method to develop a compound knowledge process. A data target saved in a compound knowledge repository suggested in this article includes all compound knowledge meta data and digital resources, which can be divided into the three following factors according to the purpose: user roles, functional elements, and service ranges. The three factors are basic components to describe abstract models of repository. In this article, meta data of compound knowledge are defined by being classified into the two factors. A component stands for the property about a main agent, activity unit or resource that use and create knowledge, and a context presents the context in which knowledge object are included. An agent of the compound knowledge process performs classification, registration, and pattern information management of composite knowledge, and serves as data flow and processing between compound knowledge repository and user. The agent of the compound knowledge process consists of the following functions: warning to inform data search and extraction, data collection and output for data exchange in an distributed environment, storage and registration for data, request and transmission to call for physical material wanted after search of meta data. In this article, the construction of a compound knowledge repository for recommendation system to be developed can serve a role to enhance learning productivity through real-time visualization of timely knowledge by presenting well-put various contents to users in the field of industry to occur work and learning at the same time.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.1-10
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• 2010

For virtual education, the multimodal learning environment with haptic feedback, termed 'immersive textbook', is necessary to enhance the learning effectiveness. However, the learning contents for immersive textbook are not widely available due to the constraints in creation and playback environments. To address this problem, we propose a framework for producing and displaying the multimodal contents for immersive textbook. Our framework provides an XML-based meta-language to produce the multimodal learning contents in the form of intuitive script. Thus it can help the user, without any prior knowledge of multimodal interactions, produce his or her own learning contents. The contents are then interpreted by script engine and delivered to the user by visual and haptic rendering loops. Also we implemented a prototype based on the aforementioned proposals and performed user evaluation to verify the validity of our framework.

• Communications of Mathematical Education
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• v.31 no.4
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• pp.457-485
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• 2017

The Self-reported Evaluation tool developed in this study allows the learners to check and evaluate their own learning by determining the details that are self-assessed. Also this tool allows learners to receive feedback on their self - evaluation results. In this study pre - post test was performed to investigate the effect of self - assessment on the learners' tendency of studying math. The result showed that Self-reported evaluation improved self - confidence, self - strategy on learning mathematics, and meta-cognitive ability. Also by conducting a qualitative analysis of the Self-reported evaluation, students practiced the cognitive activities such as summarizing the contents they have learned that day. They also tried to understand and improve the learning habit, attitude, and learning state. Teachers were also able to communicate with students by providing individual questions and feedback through student's individual Self-reported Evaluation.

• Journal of The Korean Association of Information Education
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• v.7 no.2
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• pp.199-206
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• 2003

XML is becoming a new developing tool of web technology because of its ability of data management and flexibility in data presentation. So it's well researched that the reusability and integration with learning objects such as text, image, sound, video and plug-in programs of web contents in computer education. But the research for storing, extracting and integrating metadata about learning object was needed prior to implementing online learning system to integrate and manage it. Therefore this study propose a new method of using web service of DBMS for extracting learning object's metadata in database server which located in 3-tier system. To evaluate the efficiency of proposed method, The test server and two DBMSs(MS SQL Server 2000 and Oracle 9i) which have 30 metadata was implemented and the response time of it was measured. The response time of it was short, but in order to using this method the additional programming with SAX/DOM was necessary.

• The Journal of Information Systems
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• v.30 no.1
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• pp.65-84
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• 2021

Purpose Based on the development of artificial intelligence and big data technologies, the RegTech has been emerged to reduce regulatory costs and to enable efficient supervision by regulatory bodies. The word RegTech is a combination of regulation and technology, which means using the technological methods to facilitate the implementation of regulations and to make efficient surveillance and supervision of regulations. The purpose of this study is to describe the recent adoption of RegTech and to provide basic examples of applying RegTech to capital market regulations. Design/methodology/approach English-based ontology and deep learning technologies are quite developed in practice, and it will not be difficult to expand it to European or Latin American languages that are grammatically similar to English. However, it is not easy to use it in most Asian languages such as Korean, which have different grammatical rules. In addition, in the early stages of adoption, companies, financial institutions and regulators will not be familiar with this machine-based reporting system. There is a need to establish an ecosystem which facilitates the adoption of RegTech by consulting and supporting the stakeholders. In this paper, we provide a simple example that shows a procedure of applying RegTech to recognize and interpret Korean language-based capital market regulations. Specifically, we present the process of converting sentences in regulations into a meta-language through the morpheme analyses. We next conduct deep learning analyses to determine whether a regulatory sentence exists in each regulatory paragraph. Findings This study illustrates the applicability of RegTech-based ontology and deep learning technologies in Korean-based capital market regulations.