• Korean Journal of Agricultural Science
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• v.2 no.2
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• pp.357-373
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• 1975

This study was conducted to evaluate the winter active polycross progenies of 10 genotypes selected at the hot and dry climate of the Southern Oregon in their performance in the progeny test comparing with a high yielding variety, 'Fawn', and a winter active variety, 'TFM', as the control varieties at Daejon, Korea. Various plant and leaf characteristics, especially which related to photosynthesis, and forage production during the first summer after their establishment, were examined. The important conclusions of this study are summarized as follows: 1. The winter active genotypes and variety had less leaf fresh weight and dry weight per leaf than variety 'Fawn'. Variations among polycross progenies of genotypes for these characteristics were great. 2. The winter active genotypes and variety had less leaf area per leaf than variety 'Fawn'. Leaf area among polycross progenies of genotypes deviated greatly and poly cross progenies of 'genotype-16' had the same average leaf area as 'Fawn'. 3. Differences of specific leaf weight (S. L. W.) in the winter active genotypes and variety were not significant. Probably the genetic diversity for S. L. W were not big and were narrowed down already in this genetic population. It was suggested that the photosynthate production within the population might not be different and there might be differences in the photosynthate production-translocation balance. Further study for the diurnal change in S. L. W. within the population might be useful. 4. The winter active variety and genotypes had less leaf width than 'Fawn' does. Leaf width among polycross progenies of genotypes deviated significantly. 5. Differences among controls and polycross progeny group in the initial plant height were significant and variety 'Fawn' was taller than the winter active genotypes and variety. But the differences were not significant in the regrowth of plant height after the first forage harvest. On the contrary. the differences among polycross progenies of genotypes were not significant in the initial plant but the differences in their polycross progeny performance became obvious and great in the regrowth ability which is an improtent agronomic characteristics for forage crops produced in the pasture and for hay and silage. 6. Plant width of the winter active genotypes and variety was lesser than 'Fawn' variety. 7. Differences of tiller number became evident and variety 'Fawn' had higher tiller number than the winter active genotypes and variety after the first forage cutting. There, deviations among polycross progenies of genotypes were great for this characteristic. It was obvious that the genetic differences became more evident in the second measurement after the first cutting of forage probably because this characteristic were stimulated by defoliation in the cartain genotypes and variety. 8. The winter active genotypes and variety on the initial growth. the regrowth ability andtotal yield had lesser forage yield than variety 'Fawn'. Deviation of forage yield among polycross progenies of genotypes were great and gave basis for selection according to their polycross progeny performance improving the forage yield of these winter active tall fescue population during summer. 9. It was concluded that the winter active variety and genotypes in this study was poorer than variety 'Fawn' for the most of leaf and plant characteristics including forage yield. For these measurements, the variations among polycross progenies of genotypes were great. and plant breeding might able to improve further this winter active tall fescue through the polycross progeny testing method for the higher forage production during summer in Korea. 10. The result of the associations among various characteristics under study were quite agreeable with the results of the analysis of variance and woul be useful in the selection of desirable genotypes for the development of a new variety.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Chest Surgery
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• v.36 no.7
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• pp.463-471
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• 2003

The long-term results of combined mitral valve repair and aortic valve replacement (AVR) have not been well evaluated. This study was performed to investigate the early and long-term results of mitral valve repair with AVR. Material and Method: We retrospectively reviewed 45 patients who underwent mitral valve repair and AVR between September 1990 and April 2002. The average age was 47 years: 28 were men and 17 women. Twelve patients had atrial fibrillation and three had a previous cardiac operation. The mitral valve disease consisted of pure insufficiency (MR) in 34 patients, mitral stenosis (MS) in 3, and mixed lesion in 8. Mitral valve disease was due to rheumatic origin in 24 patients, degenerative in 11, annular dilatation in 8, and ischemia or endocarditis in 2. The functional anatomy of mitral valve was annular dilatation in 31 patients, chordal elongation in 19, leaflet thickening in 19, commissural fusion in 13, chordal fusion in 10, chordal rupture in 6, and so on. Aortic prostheses used included mechanical valve in 32 patients, tissue valve in 12, and pulmonary autograft in one. The techniques of mitral valve repair included annuloplasty in 32 patients and various valvuloplasty of 54 techniques in 29 patients. Total cardiopulmonary bypass and aortic cross clamp time were 204$\pm$62 minute and 153$\pm$57 minutes, respectively. Result: Early death was in one patient due to low output syndrome (2.2%). After follow up of 57$\pm$37 months, late death was in one patient and the actuarial survival at 10 years was 96$\pm$4%. Recurrent MR developed grade II or III in 11 patients and moderate MS in 3. Three patients required reoperation for valve-related complications. The actuarial freedom from recurrent MR, MS, and reoperation were 64$\pm$11%, 86$\pm$8%, and 89$\pm$7% respectively. Conclusion: Combined mitral valve repair with AVR offers good early and long-term survival, and adequate techniques and selection of indication of mitral valve repair, especially in rheumatic disease, are prerequisites for better long-term results.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.49-58
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• 2010

Corporate social responsibility has become a very important issue for researchers (Greenfield, 2004; Maignan & Ralston, 2002; McWilliams et al., 2006; Pearce & Doh 2005), and many consider it necessary for businesses to define their role in society and apply social and ethical standards to their businesses (Lichtenstein et al., 2004). As a result, a significant number of retailers have adopted CSR as a strategic tool to promote their businesses. To this end, this study sought to discover U.S. consumers' attitudes and behavior in ethical purchasing and consumption based on their subjective perception and evaluation of a retailer. The objectives of this study include: 1) determine the participants awareness of retailers corporate social responsibility; 2) assess how participants evaluate retailers corporate social responsibility; 3) examine whether participants evaluation process of retailers CSR influence their attitude toward the retailer; and 4) assess if participants attitude toward the retailers CSR influence their purchase behavior. This study does not focus on actual retailers' CSR performance because a consumer's decision making process is based on an individual assessment not an actual fact. This study examines US college students' awareness and evaluations of retailers' corporate social responsibility (CSR). Fifty six college students at a major Southeastern university participated in the study. The age of the participants ranged from 18 to 26 years old. Content analysis was conducted with open coding and focused coding. Over 100 single-spaced pages of written responses were collected and analyzed. Two steps of coding (i.e., open coding and focused coding) were conducted (Esterberg, 2002). Coding results and analytic memos were used to understand participants' awareness of CSR and their ethical purchasing behavior supported through the selection and inclusion of direct quotes that were extracted from the written responses. Names used here are pseudonyms to protect confidentiality of participants. Participants were asked to write about retailers, their aware-ness of CSR issues, and to evaluate a retailer's CSR performance. A majority (n = 28) of respondents indicated their awareness of CSR but have not felt the need to act on this issue. Few (n=8) indicated that they are aware of this issue but not greatly concerned. Findings suggest that when college students evaluate retailers' CSR performance, they use three dimensions of CSR: employee support, community support, and environmental support. Employee treatment and support were found as an important criterion in evaluation of retailers' CSR. Respondents indicated that their good experience with a retailer as an employee made them have a positive perception and attitude toward the retailer. Regarding employee support four themes emerged: employee rewards and incentives based on performance, working environment, employee education and training program, and employee and family discounts. Well organized rewards and incentives were mentioned as an important attribute. The factors related to the working environment included: how well retailers follow the rules related to working hours, lunch time and breaks was also one of the most mentioned attributes. Regarding community support, three themes emerged: contributing a percentage of sales to the local community, financial contribution to charity organizations, and events for community support. Regarding environments, two themes emerged: recycling and selling organic or green products. It was mentioned in the responses that retailers are trying to do what they can to be environmentally friendly. One respondent mentioned that the company is creating stores that have an environmentally friendly design. Information about what the company does to help the environment can easily be found on the company’s website as well. Respondents have also noticed that the stores are starting to offer products that are organic and environmentally friendly. A retailer was also mentioned by a respondent in this category in reference to how the company uses eco-friendly cups and how they are helping to rebuild homes in New Orleans. The respondents noticed that a retailer offers reusable bags for their consumers to purchase. One respondent stated that a retailer uses its products to help the environment, through offering organic cotton. After thorough analysis of responses, we found that a participant's evaluation of a retailers' CSR influenced their attitudes towards retailers. However, there was a significant gap between attitudes and purchasing behavior. Although the participants had positive attitudes toward retailers CSR, the lack of funds and time influenced their purchase behavior. Overall, half (n=28) of the respondents mentioned that CSR performance affects their purchasing decisions making when shopping. Findings from this study provide support for retailers to consider their corporate social responsibility when developing their image with the consumer. This study implied that consumers evaluate retailers based on employee, community and environmental support. The evaluation, attitude and purchase behavior of consumers seem to be intertwined. That is, evaluation is based on the knowledge the consumer has of the retailers CSR. That knowledge may influence their attitude toward the retailer and thus influence their purchase behavior. Participants also indicated that having CSR makes them think highly of the retailer, but it does not influence their purchase behavior. Price and convenience seem to surpass the importance of CSR among the participants. Implications, recommendations for future research, and limitations of the study are also discussed.

• Journal of Korean Society of Forest Science
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• v.38 no.1
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• pp.33-45
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• 1978

The purpose of present study is to analyze the genetic variation of natural stand of Pinus densiflora. In 1975 following after the selection of 1974, twenty trees from each of three natural populations of the species were selected and their open-pollinated seeds were collected, and the locations and conditions of the populations ate presented in table 1, 2 and figure 1. Some morphological traits of the populations were already detailed in our second report of this series, in which Myong-Ju and Ul-Jin populations were regarded to be superior phenotypically to suweon population. The morphological traits of cone, seed and seed-wing, and also the growth performances and needle characters of the seedling were observed in the present study according to the previous methods. The results obtained are summarized as follows; 1. The meteorological data obtained by averaging the records of 30 year period (1931~1960) measured from the nearest meteorological stations to each population are shown in fig.2, 3, 4. The distributional patterns of investigated climate factors are generally considered to be similar among the locations. However, the precipitation density during growing season and the air temperature during dormant season on Suweon area, population 6, were quite different from those of the other areas. 2. The measurements of fresh cone weight, length, diameter and cone index, i.e., length to diameter ratio are presented in table 7. As shown in table 7, all these traits except for cone diameter seem to be highly significant in population differences and family differences within population. 3. The morphological traits of seed and seed-wing are detailed in table 8, 9, and highly significant differences are recognized among the populations and the families within population in seed-wing length, seed-wing index, seed weight, seed-length and seed index but not among the populations in the other observed traits. The values of correlation coefficient between the characters of cone and seed are given in table 10 and the positive significant correlations can be observed in the most parts of the compared traits. 4. Significant statistical differences among populations and families within population are observed in the growth performances of 1-0 and 1-1 seedling height of these progenies. But the differences in root collar diameter are shown only among families within population. As shown in table 13, the most parts of correlations are not significant statistically between the growth performances of seedling and the seed characters. 5. The number of stomata row on both sides of needle and the serration density were measured in the seedlings from each of the families of the three populations. As shown in table 15, statistical differences are considered to be significant among the populations and among the families within population in serration density but not among the populations in stomata row on both sides of the needle. The results differ from those of the third report of this series. Even if one of the reason seems to be the diversity of selected populations, it could not be confirmed definitely. The correlations between progenies and parents are not generally observed in the investigated traits of needle as shown in table 16.

• Journal of Korean Academy of Nursing Administration
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• v.6 no.1
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• pp.135-146
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• 2000

The purpose of this study was to develope, based on the Nursing Intervention Classification (NIC) system. a set of standardized nursing interventions which had been validated. and their associated activities. for use with nursing diagnoses related to home health care for women who have had a caesarian delivery and for their newborn babies. This descriptive study for instrument development had three phases: first. selection of nursing diagnoses. second, validation of the preliminary home health care interventions. and third, application of the home care interventions. In the first phases, diagnoses from 30 nursing records of clients of the home health care agency at P. medical center who were seen between April 21 and July 30. 1998. and from 5 textbooks were examined. Ten nursing diagnoses were selected through a comparison with the NANDA (North American Nursing Diagnosis Association) classification In the second phase. using the selected diagnoses. the nursing interventions were defined from the diagnoses-intervention linkage lists along with associated activities for each intervention list in NIC. To develope the preliminary interventions five-rounds of expertise tests were done. During the first four rounds. 5 experts in clinical nursing participated. and for the final content validity test of the preliminary interventions. 13 experts participated using the Fehring's Delphi technique. The expert group evaluated and defined the set of preliminary nursing interventions. In the third phases, clinical tests were held at in a home health care setting with two home health care nurses using the preliminary intervention list as a questionnaire. Thirty clients referred to the home health care agency at P. medical center between October 1998 and March 1999 were the subjects for this phase. Each of the activities were tested using dichotomous question method. The results of the study are as follows: 1. For the ten nursing diagnoses. 63 appropriate interventions were selected from 369 diagnoses interventions links in NlC., and from 1.465 associated nursing activities. From the 63 interventions. the nurses expert group developed 18 interventions and 258 activities as the preliminary intervention list through a five-round validity test 2. For the fifth content validity test using Fehring's model for determining lCV (Intervention Content Validity), a five point Likert scale was used with values converted to weights as follows: 1=0.0. 2=0.25. 3=0.50. 4=0.75. 5=1.0. Activities of less than O.50 were to be deleted. The range of ICV scores for the nursing diagnoses was 0.95-0.66. for the nursing interventions. 0.98-0.77 and for the nursing activities, 0.95-0.85. By Fehring's method. all of these were included in the preliminary intervention list. 3. Using a questionnaire format for the preliminary intervention list. clinical application tests were done. To define nursing diagnoses. home health care nurses applied each nursing diagnoses to every client. and it was found that 13 were most frequently used of 400 times diagnoses were used. Therefore. 13 nursing diagnoses were defined as validated nursing diagnoses. Ten were the same as from the nursing records and textbooks and three were new from the clinical application. The final list included 'Anxiety', 'Aspiration. risk for'. 'Infant behavior, potential for enhanced, organized'. 'Infant feeding pattern. ineffective'. 'Infection'. 'Knowledge deficit'. 'Nutrition, less than body requirements. altered', 'Pain'. 'Parenting'. 'Skin integrity. risk for. impared' and 'Risk for activity intolerance'. 'Self-esteem disturbance', 'Sleep pattern disturbance' 4. In all. there were 19 interventions. 18 preliminary nursing interventions and one more intervention added from the clinical setting. 'Body image enhancement'. For 265 associated nursing activities. clinical application tests were also done. The intervention rate of 19 interventions was from 81.6% to 100%, so all 19 interventions were in c1uded in the validated intervention set. From the 265 nursing activities. 261(98.5%) were accepted and four activities were deleted. those with an implimentation rate of less than 50%. 5. In conclusion. 13 diagnoses. 19 interventions and 261 activities were validated for the final validated nursing intervention set.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Horticultural Science & Technology
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• v.32 no.1
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• pp.105-114
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• 2014

We established a high throughput screening system of African yam tuber lines which contain high contents of total carotenoids, flavonoids, and phenolic compounds using ultraviolet-visible (UV-VIS) spectroscopy and Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. The total carotenoids contents from 62 African yam tubers varied from 0.01 to $0.91{\mu}g{\cdot}g^{-1}$ dry weight (wt). The total flavonoids and phenolic compounds also varied from 12.9 to $229{\mu}g{\cdot}g^{-1}$ and from 0.29 to $5.2mg{\cdot}g^{-1}$dry wt. FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and $1,100-950cm^{-1}$, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins ($1,700-1,500cm^{-1}$), phosphodiester groups from nucleic acid and phospholipid ($1,500-1,300cm^{-1}$) and carbohydrate compounds ($1,100-950cm^{-1}$). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate the 62 African yam tuber lines into three separate clusters corresponding to their taxonomic relationship. The quantitative prediction modeling of total carotenoids, flavonoids, and phenolic compounds from African yam tuber lines were established using partial least square regression algorithm from FT-IR spectra. The regression coefficients ($R^2$) between predicted values and estimated values of total carotenoids, flavonoids and phenolic compounds were 0.83, 0.86, and 0.72, respectively. These results showed that quantitative predictions of total carotenoids, flavonoids, and phenolic compounds were possible from FT-IR spectra of African yam tuber lines with higher accuracy. Therefore we suggested that quantitative prediction system established in this study could be applied as a rapid selection tool for high yielding African yam lines.

• Journal of dental hygiene science
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• v.12 no.4
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• pp.413-421
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• 2012

The main purpose of this study is to present practical data for the development of the health enhancing programs for the dental hygiene students. This data is based on the investigation of the psychosomatic self-reported symptoms of the dental hygiene students. Out of the random selection of the current dental hygiene students in Gyeonggi-do and Gangwon-do districts. We conducted a survey and analyzed the collected data from 432 respondents. The results are as follows: 1. The mental appeals (19.37) were higher then the physical appeals (17.53) and of the items in psychosomatic symptoms, the point of mental instability the highest (21.85); whereas, that of mouth and anal scored the lowest (14.59). 2. In terms of the religion, statistical significance was shown among physical appeals (p<.01), mental appeals (p<.05), multiple subjective symptom (p<.01), digestive organs (p<.01), aggressiveness (p<.01), nervousness (p<.01), and eye and skin (p<.05), mental instability (p<.05). 3. Concerning the living conditions, Statistical significance was found on the items such as physical appeals (p<.05), mental appeals (p<.01), depression (p<.001), irregular and life (p<.001), multiple subjective symptom (p<.01), lie scale (p<.01) and mouth and anal (p<.05), digestive organs (p<.05). 4. As for regular health check-ups, statistical significance was shown in the following items such as mental appeals (p<.05), depression (p<.01), multiple subjective symptom (p<.05), mental instability (p<.05).

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.