• Journal of Forest and Environmental Science
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• v.21 no.1
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• pp.34-58
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• 2005

This study was executed to find out how to improve the planting and use of landscaping plants in Weonju and Hoengseong. 1. The number of street trees were 22,068 and the species number were 10 species in Weonju in 2004. The major species of street trees were Ginkgo biloba(58%), Prunus sargentii(15%), Zelkova serrata(9%), Prunus armeniaca var. ansu(8%), and Acer palmatum(6%). The ratio of native species versus exotic were 50:50. In Hoengseong, the number of street trees was 13,500 and the species number were 15 species. The major species of street trees were Prunus sargentii(42%), Ginkgo biloba(23%), Acer triflorum(12%), Prunus armeniaca var. ansu(6%), and Prunus mume(4%). The ratio of native species versus exotic were 67:33. The species of which planting frequency within two areas was very high were Ginkgo biloba and Prunus sargentii. 2. It is necessary to select tree species suitable for the characteristics of the locality and to raise distinctive street trees that contribute to the tourist industry. For the purpose, the appropriate street trees in two areas are Cornus controversa, Quercus aliena, Zelkova serrata, Prunus padus, Sorbus alnifolia, Sorbus comixta, Albizzia julibrissin, Acer triflorum, Styrax japonica, Chionanthus retusus, Celtis sinensis, Prunus yedoensis, Malus sieboldii, Crataegus Pinnatifida, Prunus armeniaca var. ansu and Pyrus pyrifolia etc.. 3. Appropriate pruning adds to the aesthetic and prolongs the useful life, it also requires less managing of insects and diseases to maintain good healthy of street trees. Street trees were not properly pruned due to electric lines and shortage of pruning information. The pruning was controlled by Korea Electric Power Co, which has no pruning information. Pruning must be maintained by a professional landscape company to maintain good shape such as with Bonsai. The shrubs planting zone between street trees and other trees, and preservation plates were established for healthy of street trees. They have to be repaired and maintained well to keep better environmental conditions. The proper fertilization, the control of pests and diseases, the installation of drainpipe and the use of soil brought from another place were needed to improve the planting, use and maintenance of landscape plants. 4. The species number of school trees and flowers of 102 schools in Weonju and Hoengseong were 17species, 16species respectively. The major species of school trees in Weonju were Juniperus chinensis(24%), Ginkgo biloba(17%), Pinus densiflora(14%), Zelkova serrata(14%), and Pinus koraiensis(9%), and those of school trees in Hoengseong were Pinus koraiensis(44%), Abies holophylla(25%), Juniperus chinensis(8%), and Ginkgo biloba(8%). The major species of school flowers in Weonju were Rosa centifolia(47%), Forsythia koreana(24%), Magnolia kobus(12%), and Rhododendron schlippenbachii(6%), and those of school flowers in Hoengseong were Forsythia koreana(36%), Rhododendron schlippenbachii(33%), Magnolia kobus(6%) and Dicentra spectabilis(6%). 5. The species number of the protection trees designated by Woenju and Hoengseong were 15 species. The major species of protection trees were Zelkova serrata(100 trees), Ginkgo biloba(18) Pinus densiflora(7), Quercus spp. (5), Juniperus chinensis(4) and Alnus japonica(4). 6. The landscape plants planted around 2004 in weonju were Prunus yedoensis(2,563 trees), Betula platyphylla var. japonica(2,000), Abies holophylla(1,785), Diospyros kaki(1,100), Prunus sargentii(880) and Prunus armeniaca var. ansu(708) etc.. The shrubs planted were Rhododendron obutusum(21,559 plants), Rosa centifolia (7,150), Rhododendron yedoense var. poukhanense(5,950), Forsythia koreana(3,000) and Ligustrum obtusi[olium(2,500) etc.. The landscape plants planted in Hoengseong Acer triflorum(928trees), Prunus yedoensis(455), Zelkova serrata(327), Thuja orientalis(261), Prunus sargentii(257), Pinus koraiensis(200), Prunus persica for. rubro-plena(200) and Pyrus pyrifolia (200) etc.. The shrubs planted were Rhododendron yedoense var. poukhanense(15,936), Syringa dilatata(10,090), Forsythia koreana(9,660), Cercis chinensis(3,200), Buxus microphylla var. koreana(2,600) and Rosa centifolia(1,868) etc.. 7. The species numbers of the herbaceous plants planted in 2004 in Weonju were 24 species and the ratio of native species versus exotic were 7:17. The major species of perennial plants were Aster koraiensis(30,656 plants), Coreopsis drummondii(7,656), Rudbeckia bicolor(6,000), Chrysanthemum morifolium(4,850) and Chrysanthemum zawadskii var. latilobum(4,312). The major species of annuals and biennials were Cosmos bipinnatus(672,000 plants), Zinnia elegans(35,600), Petunia hybrida(26,920), Viola tricolor(23,000), Helianthus annuus(17,000), and Geranium cinereum var. pubcaulescens(5,200). In Hoengseong, the numbers of herbaceous plants were 906,310 plants and the species numbers were 15 species. The major species of perennials plants were Aster koraiensis(70,480 plants), Hemerocallis fulva(20,070), and Phlox drummondii(18,000). The major species of annuals and biennials were Phlox hybrida(174,000 plants), Cosmos bipinnatus(125,000), Zinnia elegans(109,000), Tagetes patula(96,700), Vinca rosea(89,000) and Calendula officinalis(70,000). 8. Through these result, it was thought that the diversification of planting species, the selection of plants suitable to each space and the generalization of use of native species were needed.

• Tuberculosis and Respiratory Diseases
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• v.50 no.1
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• pp.52-66
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• 2001

Background : NF-${\kappa}B$ is the most important transcriptional factor in IL-8 gene expression. Triptolide is a new compound that recently has been shown to inhibit NF-${\kappa}B$ activation. The purpose of this study is to investigate how triptolide inhibits NF-${\kappa}B$-dependent IL-8 gene transcription in lung epithelial cells and to pilot the potential for the clinical application of triptolide in inflammatory lung diseases. Methods : A549 cells were used and triptolide was provided from Pharmagenesis Company (Palo Alto, CA). In order to examine NF-${\kappa}B$-dependent IL-8 transcriptional activity, we established stable A549 IL-8-NF-${\kappa}B$-luc. cells and performed luciferase assays. IL-8 gene expression was measured by RT-PCR and ELISA. A Western blot was done for the study of $I{\kappa}B{\alpha}$ degradation and an electromobility shift assay was done to analyze NF-${\kappa}B$ DNA binding. p65 specific transactivation was analyzed by a cotransfection study using a Gal4-p65 fusion protein expression system. To investigate the involvement of transcriptional coactivators, we perfomed a transfection study with CBP and SRC-1 expression vectors. Results : We observed that triptolide significantly suppresses NF-${\kappa}B$-dependent IL-8 transcriptional activity induced by IL-$1{\beta}$ and PMA. RT-PCR showed that triptolide represses both IL-$1{\beta}$ and PMA-induced IL-8 mRNA expression and ELISA confirmed this triptolide-mediated IL-8 suppression at the protein level. However, triptolide did not affect $I{\kappa}B{\alpha}$ degradation and NF-$_{\kappa}B$ DNA binding. In a p65-specific transactivation study, triptolide significantly suppressed Gal4-p65T Al and Gal4-p65T A2 activity suggesting that triptolide inhibits NF-${\kappa}B$ activation by inhibiting p65 transactivation. However, this triptolide-mediated inhibition of p65 transactivation was not rescued by the overexpression of CBP or SRC-1, thereby excluding the role of transcriptional coactivators. Conclusions : Triptolide is a new compound that inhibits NF-${\kappa}B$-dependent IL-8 transcriptional activation by inhibiting p65 transactivation, but not by an $I{\kappa}B{\alpha}$-dependent mechanism. This suggests that triptolide may have a therapeutic potential for inflammatory lung diseases.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Horticultural Science & Technology
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• v.31 no.5
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• pp.640-647
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• 2013

This study was conducted to provide exportation strategy by surveying on preference of Japanese consumers on cut chrysanthemum exported. The survey was conducted two times by a local survey company in Japan, and the surveys were conducted largely on chrysanthemums for casual flowers and the altar. After departmentalizing Japanese consumers per groups the result were analyzed through conjoint and cluster methods, flower colors and shape were used relatively higher rate for selection criteria of flowers in every group in the case of casual flowers. Group 1 comprised of 60 year-old housewives who reside in a small city with high school diploma and annual income less than 300 million yen, and group 2 of 40 year-old housewives who are small city residents with high school diplomas and annual income of 300 million yen show higher rate of use in flower shape than colors. Another group 3 whose members are 50 year-old housewives, small city residents with high school diplomas and annual income of 600 million yen showed higher rate of use colors than the shape for selection criteria of flowers. The consumption characteristics according to the ages of the consumers showed a pronounced tendency. The 40-50 year-old housewives preferred single flowers packed with other flowers, and the 60 year-old housewives double flowers packed with only chrysanthemums. In flower color, the 50-60 year-old housewives preferred white and yellow flowers, and the 40 year-old housewives pink and yellow flowers. Therefore, there are needs for development strategy of new products considering the consumption characteristics of flower shape and color according to the ages of consumer. After analyzing the chrysanthemums for altar by departmentalization of Japanese consumers, every group showed relative higher rate of use for flower shape for selection criteria of flowers. According to the analysis on the consumption characteristics, group 1 which is comprised of 30-40 year-old housewives who reside in small city with high school diplomas and income less than 300 million yen, and the group 2 of 20 year-old housewives who reside in small city with college diplomas and annual income less than 300 million yen. They are very sensitive to the price of the products while the group 3 of 50 year-old housewives who reside in small city with high school diplomas and annual income less than 300 million yen are insensitive to the price. The 30-50 year-old housewives preferred white and pink flowers, and the 20 year-old housewives yellow and pink flowers. In flower shape, the 50 year-old housewives preferred anemone shape, the 30-40 year-old housewives double shape, and the 20 year-old housewives pompon shapes. Therefore, the white, double flowers for the 30-40 year-old housewives and the yellow, pompon flowers for the 20 year-old housewives are needed to be created at the lowest cost, while the white, anemone flowers are needed to created at higher cost with high quality. In light of these results, it is considered that we should understand the types of purchasing products through consumption characteristics of Japanese consumers. Also we should plan, create market-oriented and consumer-oriented products, and should export them in order to expand more exportation.

• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.9
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• pp.1172-1181
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• 2007

This research involved 226 Jeonbuk Province school dietitians as subjects to investigate intake and perceptions of the healthy functional foods. Sixty nine percent of the school dietitians didn't even know about the law enforcement concerning the health functional foods. Although 68.1% of the respondents said that they slightly knew about health functional foods, only 25% knew exactly what it was. As shown in the survey, most didn't have the cognitive understanding did not understand which should be obtained by education. Sixty two percent of the answerers said they had experience of taking health various functional food products of various kinds such as supplements (57.9%), red ginseng products (52.9%), and chlorella products (30.0%). The motive of intake was in the order of fatigue restoration (25.7%), sickness prevention (22.9%), and nutrient replenishment (22.9%). A fascinating fact from this study was that the reason for healthy functional product intake was different between groups that was primarily interested in the products and those that was not. For those who had interest, the reason for intake was for sickness prevention. On the other hand, for those who didn't have any interest, the reasons was primarily for fatigue restoration and they were mostly persuaded by close friends and relatives. Main concerns were in the order of side effects (4.72), efficacy after intake (4.59), cleanliness (4.51), reliability of the company (4.29), and price (4.23). In view of the study, it is clear that a lot of people are showing interest in healthy functional food products. However, dietitians who are experts in food and nutrition lacked knowledge and information on healthy functional food.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• (The) Research of the performance art and culture
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• no.25
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• pp.253-289
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• 2012

Dasiraegi is a traditional funeral rite performance of Jindo located in the South Jeolla Province of South Korea. With its unique stylistic structure including various dances, songs and witty dialogues, and a storyline depicting the birth of a new life in the wake of death, embodying the Buddhism belief that life and death is interconnected; it attracted great interest from performance organizers and performers who were desperately seeking new contents that can be put on stage as a performance. It is needless to say previous research on Dasiraegi had been most valuable in its recreation as it analyzed the performance from a wide range of perspectives. Despite its contributions, the previous researches were mainly academic focusing on: the symbolic meanings of the performance, basic introduction to the components of the performance such as script, lyrics, witty dialogue, appearance (costume and make-up), stage properties, rhythm, dance and etc., lacking accurate representation of the most crucial element of the performance which is sori (song). For this reason, the study analyzes the music of Dasiraegi and presents its musical characteristics along with its scores to provide practical support for performers who are active in the field. Out of all the numbers in Dasiraegi, this study analyzed all of Geosa-nori and Sadang-nori, the funeral dirge (mourning chant) sung as the performers come on stage and Gasangjae-nori, because among the five proceedings of the funeral rite they were the most commonly performed. There are a plethora of performance recordings to choose from, however, this study chose Jindo Dasiraegi, an album released by E&E Media. The album offers high quality recordings of performances, but more importantly, it is easy to obtain and utilize for performers who want to learn the Dasiraegi based on the script provided in this study. The musical analysis discovered a number of interesting findings. Firstly, most of the songs in Dasiraegi use a typical Yukjabaegi-tori which applies the Mi scale frequently containing cut-off (breaking) sounds. Although, Southern Kyoung-tori which applies the Sol scale was used, it was only in limited parts and was musically incomplete. Secondly, there was no musical affinity between Ssitgim-gut and Dasiraegi albeit both are for funeral rites. The fundamental difference in character and function of Ssitgim-gut and Dasiraegi may be the reason behind this lack of affinity, as Ssitgim-gut is sung to guide the deceased to heaven by comforting him/her, whereas, Dasiaregi is sung to reinvigorate the lives of the living. Lastly, traces of musical grammar found in Pansori are present in the earlier part of Dasiraegi. This may be attributed to the master artist (Designee of Important Intangible Cultural Heritage), who was instrumental in the restoration and hand-down of Dasiaregi, and his experience in a Changgeuk company. The performer's experience with Changgeuk may have induced the alterations in Dasiraegi, causing it to deviate from its original form. On the other hand, it expanded the performative bais by enhancing the performance aspect of Dasiraegi allowing it to be utilized as contents for Performing Arts. It would be meaningful to see this study utilized to benefit future performance artists, taking Dasiraegi as their inspiration, which overcomes the loss of death and invigorates the vibrancy of life.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• The Korean Journal of Nuclear Medicine Technology
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• v.26 no.2
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• pp.20-31
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• 2022

Purpose Broad Quantification Calibration(B.Q.C) is the procedure for Quantitative Analysis to measure Standard Uptake Value(SUV) in SPECT/CT scanner. B.Q.C was performed with Tc-99m, I-123, I-131, Lu-177 respectively and then we acquired the phantom images whether the SUVs were measured accurately. Because there is no standard for SUV test in SPECT, we used ACR Esser PET phantom alternatively. The purpose of this study was to lay the groundwork for Quantitative Analysis with various isotopes in SPECT/CT scanner. Materials and Methods Siemens SPECT/CT Symbia Intevo 16 and Intevo Bold were used for this study. The procedure of B.Q.C has two steps; first is point source Sensitivity Cal. and second is Volume Sensitivity Cal. to calculate Volume Sensitivity Factor(VSF) using cylinder phantom. To verify SUV, we acquired the images with ACR Esser PET phantom and then we measured SUV_mean on background and SUV_max on hot vials(25, 16, 12, 8 mm). SPSS was used to analyze the difference in the SUV between Intevo 16 and Intevo Bold by Mann-Whitney test. Results The results of Sensitivity(CPS/MBq) and VSF were in Detector 1, 2 of four isotopes (Intevo 16 D1 sensitivity/D2 sensitivity/VSF and Intevo Bold) 87.7/88.6/1.08, 91.9/91.2/1.07 on Tc-99m, 79.9/81.9/0.98, 89.4/89.4/0.98 on I-123, 124.8/128.9/0.69, 130.9, 126.8/0.71, on I-131, 8.7/8.9/1.02, 9.1/8.9/1.00 on Lu-177 respectively. The results of SUV test with ACR Esser PET phantom were (Intevo 16 BKG SUV_mean/25mm SUV_max/16mm/12mm/8mm and Intevo Bold) 1.03/2.95/2.41/1.96/1.84, 1.03/2.91/2.38/1.87/1.82 on Tc-99m, 0.97/2.91/2.33/1.68/1.45, 1.00/2.80/2.23/1.57/1.32 on I-123, 0.96/1.61/1.13/1.02/0.69, 0.94/1.54/1.08/0.98/ 0.66 on I-131, 1.00/6.34/4.67/2.96/2.28, 1.01/6.21/4.49/2.86/2.21 on Lu-177. And there was no statistically significant difference of SUV between Intevo 16 and Intevo Bold(p>0.05). Conclusion Only Qualitative Analysis was possible with gamma camera in the past. On the other hand, it's possible to acquire not only anatomic localization, 3D tomography but also Quantitative Analysis with SUV measurements in SPECT/CT scanner. We could lay the groundwork for Quantitative Analysis with various isotopes; Tc-99m, I-123, I-131, Lu-177 by carrying out B.Q.C and could verify the SUV measurement with ACR phantom. It needs periodic calibration to maintain for precision of Quantitative evaluation. As a result, we can provide Quantitative Analysis on follow up scan with the SPECT/CT exams and evaluate the therapeutic response in theranosis.