• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• The Korean Journal of Health Service Management
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• v.2 no.1
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• pp.105-124
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• 2008

The purpose of this research is to provide the data base for the activation of Quality Improvement operation through investigating the status of Quality Improvement operation, and finding out factors influencing on the goal-orientation of QI performers in the medical centers of more than one hundred beds where are practicing Quality Improvement operation. In order to reach the purpose, document study was carried out grounded on the proceeding researches and formulated statistical data in relation with the status of Quality Improvement performers, and proof study was carried out through questionnaire survey. The subjects of the survey were the Quality Improvement performers working in seventy three medical centers in Pusan-Gyeongnam, Daegu-Gyeongbuk, and Ulsan. Among eighty three Quality Improvement performers, fifty, five were questionnaire surveyed, on the result of which Reliability Analysis, Factor Analysis, and Multiple Regression Analysis were made, using statistical program. The the results of the proof analysis on this research are as follows. First, in the factors influencing the devoting to goal pursuit of QI performers, organization-goal contribution(0.44) had significant positive effects, while organization conflict(-0.25) had significant negative effects. In other words, the higher the organization-goal contribution was, the higher the devoting to goal pursuit was, while the less the organization conflict was, the higher the devoting to goal pursuit was, which was statistically significant.(p<0.05). Second, in the aspect of goal performance types of QI performers, the process-centered type showed high level of the devoting to goal pursuit, which was statistically significant.(p<0.05). Third, in the aspect of QI performance degree, the higher the devoting to goal pursuit was, the higher the QI performance degree was, which was statistically significant.(p<0.05). In addition, the performers who perceived their workplaces organic structure showed much higher QI performance degree, which statistically significant.(p<0.05). Generalizing the results of this research, it is possible to offer a few suggestions as follows. First, as the competition among the medical centers is more severe recently owing to medical center evaluation system, medical centers are practicing various Quality Improvement operation in all of medical services such as clinical performance and management performance, to reach the purpose of both cost-cutting and medical quality improvement. Thus in order to practice Quality Improvement operation more efficiently in medical centers, it is essential to nuke use of problem-solving methods and statistical members. This as the willingness of chief executives and positive attitude and recognition of organization members. This requires the installation of divisions in charge and disposition of persons in charge, not to speak of persistent training of Quality Improvement. Second, the divisions in charge of QI carry out Quality Improvement operation at the medical center level, and take the role of generalizing and adjusting QI performances of various departments. Owing to this role, the division in charge of QI is considered indispensable organization in the QI operation of medical centers along with medical QI committee, while it contributes to the government's goal of reducing quality level gaps among medical centers. Therefore it is necessary for government and QI organizations to give institutional support and resources for the sake of QI operation of medical centers, besides to supply systematic trainning and informations to the divisions and persons in charge of QI. Third, it is certain that disposition of persons in charge should be determined in view of the scale and the scope of QI operation in medical centers.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.161-173
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• 2020

As ICT convergence technology has spread and applied to various industrial fields and society in general, interest in rural entrepreneurship using smart farm as a means for solving many pending problems in agriculture is increasing. In this context, this study is to look at the influential factors in terms of perceived usefulness associated with the rural entrepreneurial intention using smart farm and suggest a proposal for spreading smart farms. The subjects were 296 general adults over 20 years old who were selected by simple random sampling method. The research method was exploratory factor analysis and multiple regression analysis using IBM SPSS 22.0. The perceived usefulness of smart farm, which are availability, reliability and economic efficiency were selected as independent variables to analyze the influential factors on rural entrepreneurial intention using smart farm and the moderating effect of personal innovation was observed. As a result, reliability and economic efficiency have a positive(+) influence on rural entrepreneurial intention using smart farm. And personal innovation moderates the relationship between the availability, reliability of smart farm and rural entrepreneurial intention using smart farm. The results of this study have significance in that we devised and empirically revealed factors affecting rural entrepreneurship intentions from the perspective of perceived usefulness of smart farms, away from studies of general entrepreneurship intention factors such as internal personal characteristics and external environmental factors. The implications of the study are expected to be utilized at the seeking direction of policy for potential entrepreneur using smart farm, the training and consulting in actual field of smart farm.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.253-266
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• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.549-556
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• 2017

This study investigated the importance of social cognitive intervention and the cognitive rehabilitation intervention by comparing the difference and examining the relationship between neurological cognitive function and social cognitive function of stroke patients in the acute phase and chronic stroke before returning to the community. LOTCA, cartoon intention inference task, and social behavior sequence task were performed on 30 acute stroke inpatients and 30 chronic stroke patients from May 2015 to June 2016. A two sample t test was conducted to examine the differences between the groups. The Pearson's correlations test was performed to examine the correlation among the variables in each group. As a result, there were statistically significant differences between the neurological cognitive function and social cognitive function of acute stroke patients and chronic stroke patients who were undergoing rehabilitation training before returning to the community (p<0.05). A linear relationship was found between the thinking operation and social behavior sequence task in the acute stroke group (r=0.539, p<0.05). In the chronic stroke group, visual perception (r=0.530, p<0.05), visual motor organization (r=0.655, p<0.05) and thinking operation (r=0.534, p<0.05) were correlated with the cartoon intention inference task. In addition, the social behavior sequence task were correlated with visual organization (r=0.534, p<0.05) and thinking operation (r=0.764, p<0.05). As a result of multiple regression analysis, the neurological cognitive functions influencing the social cognitive function in the cartoon task was found to be the thinking operation (B = 0.431) in acute stroke patients and the thinking operation (B=0.272) and visuomotor organization (B = 0.218) in the case of chronic stroke. In addition, the results of the social behavior sequence task revealed the thinking operation (B=0.417) in the acute stroke patients, and thinking operation (B=0.267), visual motor organization(B=0.274) and visual perception(B=151) in chronic stroke patients to be significant. According to this result, there is a difference in the neurological and social cognitive levels between the two groups. Therefore, the social cognition is strongly related to the high level cognitive function as thinking operation of the neurological cognitive function. Therefore, in further research, it would be necessary to determine if there is a change in higher cognitive function in neurological cognitive function after applying a social cognition intervention program for stroke.

• Journal of Korean Home Economics Education Association
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• v.17 no.2
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• pp.197-219
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• 2005

This study attempted, in accordance with the National Educational Curriculum, to develop achievement assessment standards for a course within the field of home economics which has been widely adopted by Korean vocational high schools, namely, the food and nutrition subject. Focus was also placed on strengthening the management of the curriculum for this food and nutrition course, as well as on establishing proper assessment standards by developing model assessment tools which can be used to assess the subject. The results of this study can be summarized as follows : First, based on an analysis of the related literature and materials. the desired notion of the achievement and assessment standards was established, and their significance ascertained the achievement and assessment standards for the food and nutrition course were set and the type of model assessment tool which should be developed, as well as the method in which it should be applied. was established Second. by analyzing the curriculums and the contents of the textbooks used in the food and nutrition subject, the researcher was able to compile the 70 factors which could to be used to develop the achievement and assessments standards, and then classify these into 6 main categories and 32 sub-categories. Based on the characteristics of these factors and learners' academic performance levels the number of factors was expanded to 89 in order to establish the achievement standards. In turn, these achievement standards were used, in accordance with the learners' achievement and teaming activity levels, to develop three different levels of assessment standards. namely, upper, middle, and lower ones. Third. a model assessment tool was developed which could be used by individual school units as a reference in terms of achievement and assessment standards, and that could be modified to meet each school's circumstances. In order to create the model assessment tool a 100-question questionnaire was formulated that contained various types of questions, such as essay, report, theoretical and practical, portfolio, as well as multiple choice-type questions. Lastly, the researcher introduced measures to effectively use the achievement and assessment standards developed for the food and nutrition course, as well as the model assessment tool in school units.

• Asia pacific journal of information systems
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• v.19 no.1
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• pp.33-62
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• 2009

Internet computing is a disruptive IT innovation. Semantic Web can be considered as an IT innovation because the Semantic Web technology possesses the potential to reduce information overload and enable semantic integration, using capabilities such as semantics and machine-processability. How should organizations adopt the Semantic Web? What factors affect the adoption and diffusion of Semantic Web innovation? Most studies on adoption and diffusion of innovation use empirical analysis as a quantitative research methodology in the post-implementation stage. There is criticism that the positivist requiring theoretical rigor can sacrifice relevance to practice. Rapid advances in technology require studies relevant to practice. In particular, it is realistically impossible to conduct quantitative approach for factors affecting adoption of the Semantic Web because the Semantic Web is in its infancy. However, in an early stage of introduction of the Semantic Web, it is necessary to give a model and some guidelines and for adoption and diffusion of the technology innovation to practitioners and researchers. Thus, the purpose of this study is to present a model of adoption and diffusion of the Semantic Web and to offer propositions as guidelines for successful adoption through a qualitative research method including multiple case studies and in-depth interviews. The researcher conducted interviews with 15 people based on face-to face and 2 interviews by telephone and e-mail to collect data to saturate the categories. Nine interviews including 2 telephone interviews were from nine user organizations adopting the technology innovation and the others were from three supply organizations. Semi-structured interviews were used to collect data. The interviews were recorded on digital voice recorder memory and subsequently transcribed verbatim. 196 pages of transcripts were obtained from about 12 hours interviews. Triangulation of evidence was achieved by examining each organization website and various documents, such as brochures and white papers. The researcher read the transcripts several times and underlined core words, phrases, or sentences. Then, data analysis used the procedure of open coding, in which the researcher forms initial categories of information about the phenomenon being studied by segmenting information. QSR NVivo version 8.0 was used to categorize sentences including similar concepts. 47 categories derived from interview data were grouped into 21 categories from which six factors were named. Five factors affecting adoption of the Semantic Web were identified. The first factor is demand pull including requirements for improving search and integration services of the existing systems and for creating new services. Second, environmental conduciveness, reference models, uncertainty, technology maturity, potential business value, government sponsorship programs, promising prospects for technology demand, complexity and trialability affect the adoption of the Semantic Web from the perspective of technology push. Third, absorptive capacity is an important role of the adoption. Fourth, suppler's competence includes communication with and training for users, and absorptive capacity of supply organization. Fifth, over-expectance which results in the gap between user's expectation level and perceived benefits has a negative impact on the adoption of the Semantic Web. Finally, the factor including critical mass of ontology, budget. visible effects is identified as a determinant affecting routinization and infusion. The researcher suggested a model of adoption and diffusion of the Semantic Web, representing relationships between six factors and adoption/diffusion as dependent variables. Six propositions are derived from the adoption/diffusion model to offer some guidelines to practitioners and a research model to further studies. Proposition 1 : Demand pull has an influence on the adoption of the Semantic Web. Proposition 1-1 : The stronger the degree of requirements for improving existing services, the more successfully the Semantic Web is adopted. Proposition 1-2 : The stronger the degree of requirements for new services, the more successfully the Semantic Web is adopted. Proposition 2 : Technology push has an influence on the adoption of the Semantic Web. Proposition 2-1 : From the perceptive of user organizations, the technology push forces such as environmental conduciveness, reference models, potential business value, and government sponsorship programs have a positive impact on the adoption of the Semantic Web while uncertainty and lower technology maturity have a negative impact on its adoption. Proposition 2-2 : From the perceptive of suppliers, the technology push forces such as environmental conduciveness, reference models, potential business value, government sponsorship programs, and promising prospects for technology demand have a positive impact on the adoption of the Semantic Web while uncertainty, lower technology maturity, complexity and lower trialability have a negative impact on its adoption. Proposition 3 : The absorptive capacities such as organizational formal support systems, officer's or manager's competency analyzing technology characteristics, their passion or willingness, and top management support are positively associated with successful adoption of the Semantic Web innovation from the perceptive of user organizations. Proposition 4 : Supplier's competence has a positive impact on the absorptive capacities of user organizations and technology push forces. Proposition 5 : The greater the gap of expectation between users and suppliers, the later the Semantic Web is adopted. Proposition 6 : The post-adoption activities such as budget allocation, reaching critical mass, and sharing ontology to offer sustainable services are positively associated with successful routinization and infusion of the Semantic Web innovation from the perceptive of user organizations.

• Journal of agricultural medicine and community health
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• v.15 no.2
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• pp.97-106
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• 1990

1. Background Women's health and their involvement in health care are essential to health for everyone. If they are ignorant, malnourished or over-worked, the health &-their families as well as their own health will suffer. Women's health depends on broad considerations beyond medicine. Among other things, it depends upon their work in farming. their subordination to their families, their accepted roles, and poor hygiene with poorly equipped housing and environmental sanitation. 2. Objectives and Contents a. The health status of rural women : physical and mental complaints, experience of pesticides intoxication, Farmer's syndrome, experiences of reproductive health problems. b. participation in and attitudes towards housework and farming c. accessibility of medical care d. status of maternal health : fertility, family planning practice. induced abortion, and maternal care 3. Research method A nationwide field survey, based on stratified random sampling, was conducted during July, 1986. Revised Cornell Medical index(68 out of 195 items). Kawagai's Farmers Syndrome Scale, and self-developed structured questionnaires were used to rural farming wives(n=2.028). aged between 26-55. 4. Characteristics of the respondents mean age : 40.2 marital status : 90.8% married mean no. of household : 4.9 average years of education : 4.7 yrs. average income of household : \235,000 average years of residence in rural area : 36.4 yrs average Working hours(household and farming) : 11 hrs. 23 min 5. Health Status of rural women a. The average number of physical and mental symptoms were 12.4, 4.7, and the rate of complaints were 22.1%, 38.8% each. revealing complaints of mental symptomes higher than physical ones. b. 65.4% of rural women complained of more than 4 symptoms out of 9, indicating farmer's syndrome. 11.9 % experienced pesticide overdue syndrome c. 57.6% of respondents experienced women-specific health problems. d. Age and education of respondents were the variables which affect on the level of their health 6. Utilization of medical services a. The number of symptoms and complaints of respondents were dependent on the distance to where the health-care service is given b. Drug store was the most commonly utilized due to low price and the distance to reach. while nurse practitioners were well utilized when there were nurse practitioner's office in their villages. c. Rural women were internalized their subordination to husbands and children, revealing they are positive(93%) in health-care demand for-them but negative(30%) for themselves d. 33.0% of respondents were habitual drug users, 4.5% were smokers and 32.3% were alcohol drinkers. and 86.3% experienced induced-abortion. But most of them(77.6%) knew that those had negative effects on health. 7. Maternal Health Care a. Practice rate of contraception was 48.1% : female users were 90.9% in permanent and 89.6% in temporary contraception b. Induced abortions were taken mostly at hospital(86.3%), while health centers(4.7%), midwiferies(4.3%). and others(4.5%) including drug stores were listed a few. The repeated numbers of induced abortion seemed affected on the increasing numbers of symptoms and complaints. c. The first pre-natal check-up during first trimester was 41.8%, safe delivery rate was 15.6%, post-natal check-up during two months after delivery. Rural women had no enough rest after delivery revealing average days of rest from home work and farming 8.3 and 17.2. d. 86.6% practised breast feeding, showing younger and more educated mothers depending on artificial milk 8. Recommendations a. To lessen the multiple role over burden housing and sanitary conditions should be improved, and are needed farming machiner es for women and training on the use of them b. Health education should begin at primary school including health behavior and living environment. c. Women should be encouraged to become policy-makers as well as administrators in the field of women specific health affairs. d. Women's health indicators should be developed and women's health surveillance system too.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.4
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• pp.299-308
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• 2007

Purpose: The aim of this study was to develop a bioinformatics software and to test it in serum samples of papillary thyroid cancer using mass spectrometry (SELDI-TOF-MS). Materials and Methods: Development of 'Protein analysis' software performing decision tree analysis was done by customizing C4.5. Sixty-one serum samples from 27 papillary thyroid cancer, 17 autoimmune thyroiditis, 17 controls were applied to 2 types of protein chips, CM10 (weak cation exchange) and IMAC3 (metal binding - Cu). Mass spectrometry was performed to reveal the protein expression profiles. Decision trees were generated using 'Protein analysis' software, and automatically detected biomarker candidates. Validation analysis was performed for CM10 chip by random sampling. Results: Decision tree software, which can perform training and validation from profiling data, was developed. For CM10 and IMAC3 chips, 23 of 113 and 8 of 41 protein peaks were significantly different among 3 groups (p<0.05), respectively. Decision tree correctly classified 3 groups with an error rate of 3.3% for CM10 and 2.0% for IMAC3, and 4 and 7 biomarker candidates were detected respectively. In 2 group comparisons, all cancer samples were correctly discriminated from non-cancer samples (error rate = 0%) for CM10 by single node and for IMAC3 by multiple nodes. Validation results from 5 test sets revealed SELDI-TOF-MS and decision tree correctly differentiated cancers from non-cancers (54/55, 98%), while predictability was moderate in 3 group classification (36/55, 65%). Conclusion: Our in-house software was able to successfully build decision trees and detect biomarker candidates, therefore it could be useful for biomarker discovery and clinical follow up of papillary thyroid cancer.