• Journal of Korean Academy of Nursing
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• v.11 no.1
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• pp.65-87
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• 1981

The study is aimed at exploring concept held by clinical nurses of nursing. The data were collected from 225 nurses conviniently selected from the population of nurses working in Kang Won province. Findings include. 1) Nurse's Qualification. The respondents view that specialized knowledge is more important qualification of the nurse. Than warm personality. Specifically, 92.9% of the respondents indicated specialized knowledge as the most important qualification while only 43.1％ indicated warm personality. 2) On Nursing Profession. The respondents view that nursing profession as health service oriented rather than independent profession specifically. This suggests that nursing profession is not consistentic present health care delivery system nor support nurses working independently. 3) On Clients of Nursing Care The respondents include patients, family and the community residents in the category of nursing care. Specifically, 92.0％ of the respondents view that patient is the client, while only 67.1％ of nursing student and 74.7％ of herself. This indicates the lack of the nurse's recognition toward their clients. 4) On the Priority of Nursing care. Most of the respondents view the clients physical psychological respects as important component of nursing care but not the spiritual ones. Specially, 96.0% of the respondents indicated the physical respects, 93% psychological ones, while 64.1% indicated the spiritual ones. This means the lack of comprehensive conception on nursing aimension. 5) On Nursing Care. 91.6% of the respondents indicated that nursing care is the activity decreasing pain or helping to recover illness, while only 66.2% indicated earring out the physicians medical orders. 6) On Purpose of Nursing Care. 89.8% of the respondents indicated preventing illness and than 76.6% of them decreasing 1;ai of clients. On the other hand, maintaining health has the lowest selection at the degree of 13.8%. This means the lack of nurses' recognition for maintaining health as the most important point. 7) On Knowledge Needed in Nursing Care. Most of the respondents view that the knowledge faced with the spot of nursing care is needed. Specially, 81.3% of the respondents indicated simple curing method and 75.1%, 73.3%, 71.6% each indicated child nursing, maternal nursing and controlling for the communicable disease. On the other hand, knowledge w hick has been neglected in the specialized courses of nursing education, that is, thinking line among com-w unity members, overcoming style against between stress and personal relation in each home, and administration, management have a low selection at the depree of 48.9%,41.875 and 41.3%. 8) On Nursing Idea. The highest degree of selection is that they know themselves rightly, (The mean score measuring distribution was 4.205/5) In the lowest degree,3.016/5 is that devotion is the essential element of nursing, 2.860/5 the religious problems that human beings can not settle, such as a fatal ones, 2,810/5 the nursing profession is worth trying in one's life. This means that the peculiarly essential ideas on the professional sense of value. 9) On Nursing Services. The mean score measuring distribution for the nursing services showed that the inserting of machine air way is 2.132/5, the technique and knowledge for surviving heart-lung resuscitating is 2.892/s, and the preventing air pollution 3.021/5. Specially, 41.1% of the respondents indicated the lack of the replied ratio. 10) On Nurses' Qualifications. The respondents were selected five items as the most important qualifications. Specially, 17.4% of the respondents indicated specialized knowledge, 15.3% the nurses' health, 10.6％ satisfaction for nursing profession, 9.8% the experience need, 9.2% comprehension and cooperation, while warm personality as nursing qualifications have a tendency of being lighted. 11) On the Priority of Nursing Care The respondents were selected three items as the most important component. Most of the respondents view the client's physical, spiritual: economic points as important components of nursing care. They showed each 36.8%, 27.6%, 13.8% while educational ones showed 1.8%. 12) On Purpose of Nursing Care. The respondents were selected four items as the most important purpose. Specially,29.3% of the respondents indicated curing illness for clients, 21.3% preventing illness for client 17.4% decreasing pain, 15.3% surviving. 13) On the Analysis of Important Nursing Care Ranging from 5 point to 25 point, the nurses' qualification are concentrated at the degree of 95.1%. Ranging from 3 point to 25, the priorities of nursing care are concentrated at the degree of 96.4%. Ranging from 4 point to 16, the purpose of nursing care is concentrated at the degree of 84.0%. 14) The Analysis, of General Characteristics and Facts of Nursing Concept. The correlation between the educational high level and nursing care showed significance. (P < 0.0262). The correction between the educational low level and purpose of nursing care showed significance. (P < 0.002) The correlation between nurses' working yeras and the degree of importance for the purpose of nursing care showed significance (P < 0.0155) Specially, the most affirmative answers were showed from two years to four ones. 15) On Nunes' qualification and its Degree of Importance The correlation between nurses' qualification and its degree of importance showed significance. (r = 0.2172, p< 0.001) 0.005) B. General characteristics of the subjects The mean age of the subject was 39 ; with 38.6% with in the age range of 20-29 ; 52.6% were male; 57.9％ were Schizophrenia; 35.1% were graduated from high school or high school dropouts; 56.l％ were not have any religion; 52.6% were unmarried； 47.4% were first admission; 91.2% were involuntary admission patients. C. Measurement of anxiety variables. 1. Measurement tools of affective anxiety in this study demonstrated high reliability (.854). 2. Measurement tools of somatic anxiety in this study demonstrated high reliability (.920). D. Relationship between the anxiety variables and the general characteristics. 1. Relationship between affective anxiety and general characteristics. 1) The level of female patients were higher than that of the male patient (t = 5.41, p < 0.05). 2) Frequencies of admission were related to affective anxiety, so in the first admission the anxiety level was the highest. (F = 5.50, p < 0.005). 2, Relationship between somatic anxiety and general characteristics. 1) The age range of 30-39 was found to have the highest level of the somatic anxiety. (F = 3.95, p < 0.005). 2) Frequencies of admission were related to the somatic anxiety, so .in first admission the anxiety level was the highest. (F = 9.12, p ＜ 0.005) 0. Analysis of significant anxiety symptoms for nursing intervention. 1. Seven items such as dizziness, mental integration, sweating, restlessness, anxiousness, urinary frequency and insomnia, init. accounted for 96％ of the variation within the first 24 hours after admission. 2. Seven items such as fear, paresthesias, restlessness, sweating insomnia, init., tremors and body aches and pains accounted for 84% of the variation on the 10th day after admission.

• Journal of agricultural medicine and community health
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• v.35 no.4
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• pp.417-427
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• 2010

Objectives: This study was conducted to develop a Korean version of the Participatory Action Oriented Training (PAOT) program and training materials for improvement of agricultural working environments. Methods: The PAOT manual and checklist were developed on the basis of the original English version of the training materials, a questionnaire survey of agricultural health and safety professionals, and a brainstorming conference. Good example pictures were collected through visits to agricultural workers' houses, and an easily understandable picture was drawn by an animation specialist. A PAOT action manual and five types of action checklists were developed for use in different agricultural environments. Each action checklist contained six categories and 38 items of agricultural health and safety principles; the six categories were material storage and handling, work stations and tools, machine safety, physical environment, working schedule and organization, and basic health and safety management. Incorporating these training materials, a one-day Korean PAOT program was developed. Results: Among 307 candidate agricultural workers from four rural Korean villages, 94 workers (59 males, 35 females) participated in a PAOT from July 2007 to Oct 2008. The PAOT program was successful and the mean self-estimated satisfaction score of the participants was greater than 90%. Conclusions: A Korean PAOT program and training materials were successfully developed and applied to Korean agricultural workers. Although more studies are needed, it is expected that PAOT will greatly contribute to the improvement of agricultural working conditions and health and safety through the use of agricultural workers' self initiatives.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.