• Journal of Cadastre & Land InformatiX
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• v.54 no.1
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• pp.19-32
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• 2024

This study examined the feasibility of image-based surveys by detecting objects in facilities and agricultural land using the YOLO algorithm based on drone images and comparing them with the land category by law. As a result of detecting objects through the YOLO algorithm, buildings showed a performance of detecting objects corresponding to 96.3% of the buildings provided in the existing digital map. In addition, the YOLO algorithm developed in this study detected 136 additional buildings that were not located in the digital map. Plastic greenhouses detected a total of 297 objects, but the detection rate was low for some plastic greenhouses for fruit trees. Also, agricultural land had the lowest detection rate. This result is because agricultural land has a larger area and irregular shape than buildings, so the accuracy is lower than buildings due to the inconsistency of training data. Therefore, segmentation detection, rather than box-shaped detection, is likely to be more effective for agricultural fields. Comparing the detected objects with the land category by law, it was analyzed that some buildings exist in agricultural and forest areas where it is difficult to locate buildings. It seems that it is necessary to link with administrative information to understand that these buildings are used illegally. Therefore, at the current level, it is possible to objectively determine the existence of buildings in fields where it is difficult to locate buildings.

• Information Systems Review
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• v.25 no.3
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• pp.99-120
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• 2023

Courier services users' experience of violating privacy affects psychology and behavior of protecting personal privacy. Depending on what privacy infringement experience (PIE) of courier services users, learning about perceived privacy infringement incidents is made, recognition is formed, affection is formed, and behavior is appeared. This paradigm of changing in privacy psychologies of courier services users has an important impact on predicting responses of privacy protective action (PPA). In this study, a theoretical research framework are developed to explain the privacy protective action (PPA) of courier services users by applying attitude theory. Based on this framework, the relationships among past privacy infringement experience (PIE), perceived privacy risk (PPR), privacy concerns (i.e., concerns in unlicensed secondary use (CIUSU), concerns in information error (CIE), concerns in improper access (CIA), and concern in information collection (CIC), and privacy protective action (PPA) are analyzed. In this study, the proposed research model was surveyed by people with experience in using courier services and was analyzed for finding relationships among research variables using structured an equation modeling software, SMART-PLS. The empirical results show the causal relationships among PIE, PPR, privacy concerns (CIUSU, CIE, CIA, and CIC), and PPA. The results of this study provide useful theoretical implications for privacy management research in courier services, and practical implications for the development of courier services business model.

• Journal of The Korean Association For Science Education
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• v.44 no.5
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• pp.511-518
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• 2024

This study investigated the perceptions of chemistry teachers regarding the use of AI in education, focusing on their stages of concern, expected effects, and factors impeding implementation. Data were collected through a survey of 79 chemistry teachers who participated in first-grade teacher qualification training in 2024. The stages of concern were analyzed both overall and individually, and differences in stages of concern based on background variables were examined using the Kruskal-Wallis H test. The expected effects were measured across seven aspects, with differences were analyzed using repeated measures ANOVA and the Bonferroni method. Factors impeding implementation were analyzed through keyword analysis, focusing on internal and external factors. The results showed that overall concern was relatively low, with informational concern (Stage 1) and unconcerned (Stage 0) being high at 35.4% and 34.2%, respectively. Among active teachers, significant differences in stages of concern were observed depending on whether they had training experience (p<.05). The expected effects of AI in education showed significant statistical differences across the seven aspects (p<.05). Teachers rated 'providing diverse learning experiences' as the highest effect, while 'enhancing understanding of scientific concepts', 'improving scientific inquiry skills', and 'cultivating scientific literacy' were rated relatively low (p<.05). Internal factors were found to impede implementation more than external factors, with key internal factors including 'resistance to change', 'lack of capability', and 'teachers' negative perceptions of AI in education'. Based on these findings, recommendations were made to enhance the implementation of AI in educational settings.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• 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.20 no.4
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• pp.155-169
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• 2014

With the rapid advances in technologies, organizations are more likely to depend on information systems in their decision-making processes. Business Intelligence (BI) systems, in particular, have become a mainstay in dealing with complex problems in an organization, partly because a variety of advanced computational methods from statistics, machine learning, and artificial intelligence can be applied to solve business problems such as demand forecasting. In addition to the ability to analyze past and present trends, these predictive analytics capabilities provide huge value to an organization's ability to respond to change in markets, business risks, and customer trends. While the performance effects of BI system use in organization settings have been studied, it has been little discussed on the use of predictive analytics technologies embedded in BI systems for forecasting tasks. Thus, this study aims to find important factors that can help to take advantage of the benefits of advanced technologies of a BI system. More generally, a BI system can be viewed as an advisor, defined as the one that formulates judgments or recommends alternatives and communicates these to the person in the role of the judge, and the information generated by the BI system as advice that a decision maker (judge) can follow. Thus, we refer to the findings from the advice-giving and advice-taking literature, focusing on the role of explanations of the system in users' advice taking. It has been shown that advice discounting could occur when an advisor's reasoning or evidence justifying the advisor's decision is not available. However, the majority of current BI systems merely provide a number, which may influence decision makers in accepting the advice and inferring the quality of advice. We in this study explore the following key factors that can influence users' advice taking within the setting of a BI system: explanations on how the box-office grosses are predicted, types of advisor, i.e., system (data mining technique) or human-based business advice mechanisms such as prediction markets (aggregated human advice) and human advisors (individual human expert advice), users' evaluations of the provided advice, and individual differences in decision-makers. Each subject performs the following four tasks, by going through a series of display screens on the computer. First, given the information of the given movie such as director and genre, the subjects are asked to predict the opening weekend box office of the movie. Second, in light of the information generated by an advisor, the subjects are asked to adjust their original predictions, if they desire to do so. Third, they are asked to evaluate the value of the given information (e.g., perceived usefulness, trust, satisfaction). Lastly, a short survey is conducted to identify individual differences that may affect advice-taking. The results from the experiment show that subjects are more likely to follow system-generated advice than human advice when the advice is provided with an explanation. When the subjects as system users think the information provided by the system is useful, they are also more likely to take the advice. In addition, individual differences affect advice-taking. The subjects with more expertise on advisors or that tend to agree with others adjust their predictions, following the advice. On the other hand, the subjects with more knowledge on movies are less affected by the advice and their final decisions are close to their original predictions. The advances in predictive analytics of a BI system demonstrate a great potential to support increasingly complex business decisions. This study shows how the designs of a BI system can play a role in influencing users' acceptance of the system-generated advice, and the findings provide valuable insights on how to leverage the advanced predictive analytics of the BI system in an organization's forecasting practices.

• Research in Community and Public Health Nursing
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• v.13 no.2
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• pp.205-215
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• 2002

Objectives: This study was designed to develop and evaluate a stage matched exercise intervention program to effectively increase exercise behaviors in urban elders. Methods: The study included three phases: preliminary descriptive data collection, program development, and program evaluation. The data for the preliminary descriptive phase were collected between May and June 2001. The study participants were 89 urban elders who responded a questionnaire that included general characteristics, exercise related experiences, stage, and process of change in exercise behaviors. Data were analyzed using descriptive statistics, $x^2$-test, and content analysis. Development of the program was based on the preliminary data. and a literature review, and was guided by the tran theoretical model. It consisted of strategies to facilitate the process of changes used in each stage. Evaluation of the program was achieved from October to December 2001, using a case study method, in which eight urban female elders participated. Interviews were conducted on a weekly basis in the form of either an individual interview, or group discussion. Each elder subject received education in accordance with the program strategies and education materials. In the case that a subject's stage of change moved into another one, the scores for the process of change were re-measured. The data were analyzed using the content analysis technique. Results: The results were as follows: 1. Elders who participated in the preliminary data collection phase were over 75 years of age, and the majority of them were women. They had a higher educational level, and fewer number of illnesses than the subjects in other studies. Their stage of change was divided into pre-contemplation and maintenance. The social liberation scores were the highest across all stages of change. There was no difference between men and women on scores for processes of change in each stage. 2. The stage matched exercise intervention program that was developed in this study consisted of one counseling type program and three distinguished educational booklet materials. 3. The results of the case studies are as follows: 1) The study participants were 8 women between 75 and 87 years of age. At the first interviews, all of them were in the pre-contemplation stage. All of them reached the action stage before the 7th week. The scores for processes of change that were the focus in each stage increased more than the scores for other processes of change. During the early stages of change, experimental processes increased more than behavioral processes. However. this pattern was reversed during later stages of change. 2) Characteristics of the subjects in each stage were identical as presented at the tran theoretical model. The intervention strategies were effective in the transition occurred in any stage. 3) Barriers for exercise included unwillingness to exercise, fatigue, shortness of breath, and pain. Ways to overcome these barriers were 'learning an alternative exercise method that can be done at home', 'self-promising/ exercise-promising', and 'use of cues to exercise'. 4) The factors that affected the application of the program were consideration of age and personal preference in selecting an exercise pattern, individualized intervention, and use of education materials appropriate to elders. Women over 80 years of age preferred muscle strengthening and stretching exercise, because they can be easily done at home. They also preferred individualized interventions, materials that were easy to read, and education contents appropriate for elders. Conclusion: In conclusion, the stage matched exercise intervention program that considered the characteristics of the elders was effective to facilitate exercise behaviors of the elders.

• Journal of Gifted/Talented Education
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• v.23 no.2
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• pp.161-175
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• 2013

The purpose of this study is to investigate the differences of perception on giftedness between homeroom teachers and teachers of the gifted. The data was collected from 13 homeroom teachers and 8 teachers of gifted students. It was analyzed using recommendation letters, and shorthand notes about giftedness. The results of the study were as follows: most homeroom teachers used awards, learning attitude, presentation of self and school achievements for defined giftedness and preferred an exemplary student with task commitment but often overlooked motivation. The teachers of the gifted preferred motivation and self-satisfaction but not other social and affective characteristics. Also homeroom teachers thought that education condition is important, while the teachers of the gifted believed it was not an all important element. These differences will hurt the credibility in the selection or gifted students because homeroom teachers and teachers of the gifted use different words and expressions in their assessments of the same students. Therefore, I believe more needs to be done to encourage homeroom teachers to better understand gifted children through training programs.

• Journal of Elementary Mathematics Education in Korea
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• v.21 no.2
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• pp.309-341
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• 2017

This study aims to investigate an approach to teach percentages in elementary mathematics class by analyzing calculating strategies with percentage the students use to solve the percentage tasks and their percentages of correct answers, as well as types of errors with percentages the students make. For this research 182 sixth graders were examined. The instrument test consists of various task types in reference to the previous study; the percentages tasks are divided into algebraic-geometric, part whole-comparison-change and find part-find whole-find percentage tasks. According to the analysis of this study, percentages of correct answers of students with percentage tasks were lower than we expected, approximately 50%. Comparing the percentages of correct answers according to the task types, the part-whole tasks are higher than the comparison and change tasks, the geometric tasks are approximately equal to the algebraic tasks, and the find percentage tasks are higher than the find whole and find part tasks. As to the strategies that students employed, the percentage of using the formal strategy is not much higher than that of using the informal strategy, even after learning the formal strategy. As an insightful approach for teaching percentages, based on the study results, it is suggested to reinforce the meaning of percentage, include various types of the comparison and change tasks, emphasize the informal strategy explicitly using models prior to the formal strategy, and understand the relations among part, whole and percentage throughly in various percentage situations before calculating.