• Journal of agricultural medicine and community health
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• v.30 no.1
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• pp.101-111
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• 2005

Objectives: This study was to evaluate the compliance of hypertensive patients and its related factors registered at Community Health Practitioner Post(CHCP). Methods: 304 patients were interviewed by trained nursing students during one month(June~July 2004). The questionnaire included general charactristics, knowledge of hypertension, health education experience, constructs of Health Belief Model, self efficacy and so on. Compliance group was defined "having regularly medication and good life style". Good life style included regular exercise, non-smoking, little alcohol, low salt diet, weight control. Results: In compliance group 90.3% of man and 93.3% of woman were regularly taking hypertensive medicine, and 45.2% of man and 56.4% of woman were having good life style (compliance group). In both man and woman, the group of higher education were more compliance group, but were statistically significant were in man(p<0.05). In woman, the compliance group have significantly higher score in knowledge of hypertension(p(0.05). The compliance group have significantly higher self-efficacy score in both man and woman (p<0.05). In Health Belief Model, susceptibility and benefit were statistically significant in man, seriousness, benefit and barrier in woman(p<0.05). In multiple logistic regression analysis, education level and self efficacy in man and knowledge of hypertension, self-efficacy and benefit in woman were significant variables (p<0.05). Conclusions: It is very important to evaluate and modify life-style adding to having regularly medication in hypertensive patients registered at CHCP. To this, health education programs about benefit to compliance and the methods to improve self-efficacy should be developed for this patients.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.111-129
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• 2022

Insomnia is a chronic disease in modern society, with the number of new patients increasing by more than 20% in the last 5 years. Insomnia is a serious disease that requires diagnosis and treatment because the individual and social problems that occur when there is a lack of sleep are serious and the triggers of insomnia are complex. This study collected 5,699 data from 'insomnia', a community on 'Reddit', a social media that freely expresses opinions. Based on the International Classification of Sleep Disorders ICSD-3 standard and the guidelines with the help of experts, the insomnia corpus was constructed by tagging them as insomnia tendency documents and non-insomnia tendency documents. Five deep learning language models (BERT, RoBERTa, ALBERT, ELECTRA, XLNet) were trained using the constructed insomnia corpus as training data. As a result of performance evaluation, RoBERTa showed the highest performance with an accuracy of 81.33%. In order to in-depth analysis of insomnia social data, topic modeling was performed using the newly emerged BERTopic method by supplementing the weaknesses of LDA, which is widely used in the past. As a result of the analysis, 8 subject groups ('Negative emotions', 'Advice and help and gratitude', 'Insomnia-related diseases', 'Sleeping pills', 'Exercise and eating habits', 'Physical characteristics', 'Activity characteristics', 'Environmental characteristics') could be confirmed. Users expressed negative emotions and sought help and advice from the Reddit insomnia community. In addition, they mentioned diseases related to insomnia, shared discourse on the use of sleeping pills, and expressed interest in exercise and eating habits. As insomnia-related characteristics, we found physical characteristics such as breathing, pregnancy, and heart, active characteristics such as zombies, hypnic jerk, and groggy, and environmental characteristics such as sunlight, blankets, temperature, and naps.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• The korean journal of orthodontics
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• v.30 no.5 s.82
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• pp.565-578
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• 2000

The aim of this study was development of the Straight-Wire Appliance(SWA) suitable lot the treatment or Korean. To accomplish the object of this study, Korean adult with normal occlusion were selected with following criteria : 1) no functional abnormality in the craniofacial area, 2) good dental arch form and posterior occlusal relationship, 3) Angle Class I occlusal relationship, 4) no experience of orthodontic, nor prosthodontic treatment, especially, no dental treatment on labial and buccal surfaces of teeth, 5) good racial profile. Impression were taken for upper and lower dental arches or the selected normal occlusion samples and the orthodontic dental stone models were fabricated. 5 well-trained orthodontists had examined the acquired dental stone models to select study samples which satisfy the Six keys to optimal occlusion of Andrews. 155 pairs of dental stone models (92 pairs of Male, 63 of Female) were finally selected. 3 dimensional digitization were performed with the Coordinate Measuring Machine(CMM, MPC802, WEGU-Messtechnik, Germany) and measuring of Angulation, Inclination, In-and-Out, Molar offset angle and Arch form were accomplished with a measuring software to achieve data for the development of SWA. Before the measurement, error study was performed on the 3 dimensional digitization with CMM, and the analysis of reliability of computerized measuring method adapted in this study and conventional manual method Presented by Andrews was performed. Results of this study were as to)lows : 1. Equi-distance digitization with mesh size 0.25 mm, 0.5 mm and 1.0 mm were acceptable in 3 dimensional digitization of dental stone model with the CMM, and the digitization with 1.0 mm mesh size was recommendable in terms of efficiency. 2. Computerized measuring method with 3 dimensional digitization was more reliable than manual measuring method of Andrews. 3. Data were collected for the development of SWA suitable for the morphological characteristics of Korean with the computerized measuring method with 3 dimensional digitization.

• Korean Journal of Applied Biomechanics
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• v.12 no.2
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• pp.159-177
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• 2002

The purpose of this study was to investigate the relations between the segments of the body, the three dimensional anatomical angle and the angular velocity of the air born phase and understand the control mechanism of the high-bar movement, the somersault, the double somersault, the double somersault with full twist. For this study seven well trained university gymnastic volunteered, Zatsiorky and Seluyanov(1983, 1985)'s sixteen segment system anatomical model was used for this study. For the movement analysis three dimensional cinematographical method(Arial Performance Analysis System : APAS) was used and for the calculation of the kinematic variables a self developed program was used with the LabVIEW 5.1 graphical profromming(Johnson, 1999) program. By using Eular's equations the three dimensional anatomical Cardan angles of the joint and angular velocity were defined. As a result of this study 1. As the rotation of the body increased in the air born phase the projection angle of the CM of the total increased, this resulted the increased of the max hight of the CM. 2. In three dimensional angular velocity the Z axis(vertical direction) projection angular velocity increased as the rotation of the body increased in the airborn phase, but the Y axis and the X axis projection angular velocity did not show significant differences. 3. As the rotation of the body increased in the air born phase the angular movement of the shoulder and the hip showed significant change. These movement act as the starter in the preparation phase. 4. The somersault angle, the twist angle, the tilt angle of the upper body related to the global reference frame in the releas phase the average somersault angle of the three types of high-bar movement was $57.7^{\circ}$, $38.8^{\circ}$, $39.7^{\circ}$, the average tilt angle was $-1.5^{\circ}$, $-5.4^{\circ}$, $-8.4^{\circ}$, the average twist angle was $13.4^{\circ}$, $10.6^{\circ}$, $23.3^{\circ}$. This result showed that the somersault with full twist had the largest movement.

• Journal of Distribution Science
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• v.8 no.3
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• pp.37-47
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• 2010

CRM refers to the operating activities that always maintain and promote good relationship with customers to ultimately maximize the company's profits by understanding the value of customers to meet their demands, establishing a strategy which may maximize the Life Time Value and successfully operating the business by integrating the customer management processes. In our country, many big businesses are introducing CRM initiatively to use it in marketing strategy however, most medium and small sized companies do not understand CRM clearly or they feel difficult to introduce it due to huge investment needed. This study is intended to present CRM promotion strategy and activities plan fit for the medium and small sized companies by analyzing the success factors of the leading companies those have already executed CRM by surveying the precedents to make the distributors out of the industries have close relation with consumers to overcome their weakness in scale and strengthen their competitiveness in such a rapidly changing and fiercely competing market. There are 5 stages to build CRM such as the recognition of the needs of CRM establishment, the establishment of CRM integrated database, the establishment of customer analysis and marketing strategy through data mining, the practical use of customer analysis through data mining and the implementation of response analysis and close loop process. Through the case study of leading companies, CRM is needed in types of businesses where the companies constantly contact their customers. To meet their needs, they assertively analyze their customer information. Through this, they develop their own CRM programs personalized for their customers to provide high quality service products. For customers helping them make profits, the VIP marketing strategy is conducted to keep the customers from breaking their relationships with the companies. Through continuous management, CRM should be executed. In other words, through customer segmentation, the profitability for the customers should be maximized. The maximization of the profitability for the customers is the key to CRM. These are the success factors of the CRM of the distributors in Korea. Firstly, the top management's will power for CS management is needed. Secondly, the culture across the company should be made to respect the customers. Thirdly, specialized customer management and CRM workers should be trained. Fourthly, CRM behaviors should be developed for the whole staff members. Fifthly, CRM should be carried out through systematic cooperation between related departments. To make use of the case study for CRM, the company should understand the customer and establish customer management programs to set the optimal CRM strategy and continuously pursue it according to a long-term plan. For this, according to collected information and customer data, customers should be segmented and the responsive customer system should be designed according to the differentiated strategy according to the class of the customers. In terms of the future CRM, integrated CRM is essential where the customer information gathers together in one place. As the degree of customers' expectation increases a lot, the effective way to meet the customers' expectation should be pursued. As the IT technology improved rapidly, RFID (Radio Frequency Identification) appears. On a real-time basis, information about products and customers is obtained massively in a very short time. A strategy for successful CRM promotion should be improving the organizations in charge of contacting customers, re-planning the customer management processes and establishing the integrated system with the marketing strategy to keep good relation with the customers according to a long-term plan and a proper method suitable to the market conditions and run a company-wide program. In addition, a CRM program should be continuously improved and complemented to meet the company's characteristics. Especially, a strategy for successful CRM for the medium and small sized distributors should be as follows. First, they should change their existing recognition in CRM and keep in-depth care for the customers. Second, they should benchmark the techniques of CRM from the leading companies and find out success points to use. Third, they should seek some methods best suited for their particular conditions by achieving the ideas combining their own strong points with marketing. Fourth, a CRM model should be developed that will promote relationship with individual customers just like the precedents of small sized businesses in Switzerland through small but noticeable events.

• Journal of dental hygiene science
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• v.16 no.3
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• pp.242-248
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• 2016

The purposes of this study were to investigate the factors affection the Oral Impacts on Daily Performances for Children (C-OIDP) in elementary and middle school students, and identify the association between oral health-related behaviors, oral health condition and C-OIDP. A cross-sectional study was conducted in three schools in Incheon, Asan, Korea. A total of 175 selected children were interviewed by a trained examiner using a questionnaire. Oral Health Related Quality of Life was assessed by the Korean version of C-OIDP. Socio-economic characteristics, oral health-related behaviors, oral health condition and C-OIDP were verified using the questionnaire. ANOVA analysis was performed to determine the oral health and C-OIDP, and multiple regression analysis was performed to determine the factors affecting the C-OIDP. The activities with the greatest effect were eating (28.0%), cleaning teeth (22.9%), and smiling (18.9%). In the logistic regression model, the high item score of C-OIDP was associated with experiencing dental caries and gum pain in the past month. The more the C-OIDP prevalence item, the more the fillng deciduous tooth surface (fs) (p=0.024), caries experienced deciduous tooth surface (dfs) (p=0.049), total caries tooth surface (ds+DS) (p=0.021), and total caries experienced tooth surface (dfs+DMFS) (p=0.047). It can be concluded that the factors affecting C-OIDP are fs, dfs, dfs+DMFS, and gingival pain. Based on these results, we can improve C-OIDP to advance preventive practice.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.8
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• pp.1177-1191
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• 2016

Estimation of food consumption details, such as portion size and frequency of consumption, is needed for exposure assessment step in microbiological risk assessment. This study investigated the amounts and frequencies of 50 kinds of consumed livestock products. A quantitative survey was performed by trained interviewers in face-to-face interviews with 1,500 adults aged over 19, who were randomly selected from seven major provinces in Korea. Respondents received a picture of one serving size for each of the 50 livestock products, including meats, processed meat products, milk and dairy products, and eggs and processed egg products. A t-test and general linear model were carried out using SPSS statistics. The most important factor affecting consumption of livestock products was residence area. The most frequently consumed food was milk (2.6 times/week), followed by pork (1.4 times/week), liquid yogurt (1.3 times/week), rolled omelet (1.2 times/week), semisolid yogurt (1.0 times/week), steamed egg (1.0 times/week), ice cream (0.9 times/week), chicken (0.8 times/week), low fat milk (0.7 times/week), and beef (0.6 times/week). In the case of consumption amount, people living in a city consumed meat (beef, pork, chicken, and duck) 1.5 times more than those living in a village, whereas milk and dairy products and eggs and processed egg products were consumed more frequently by people living in a town. When people eat meat, they consume twice the amount of one serving size. Students consumed livestock and processed livestock products more frequently with greater portions all at once. People living in Seoul, Incheon/Gyeonggi, and Busan/Ulsan/Gyeongnam consumed livestock products more frequently in large amounts. Data from this study can be used for risk assessment of livestock and processed livestock products as well as education for safe consumption of livestock products.