• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.293-300
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• 2024

On October 31, 2023, the revision of the Medical Technologist Act made it mandatory to complete field training courses in order to obtain a license as a radiologic technologist. Therefore, we would like to survey the actual situation of field training in medical institutions to inform the revised Medical Technologist Act and propose improvement measures to increase the effectiveness of field training. A survey was conducted from March to April, 2023, among radiologic technologists working in medical institutions. The questionnaire was sent through a form on a domestic portal site, Company N, and 120 respondents completed it. Eighty-two respondents, or 68.3 percent, had experience in educating on-the-job training students. 58% of the respondents were aware of the fact that the amendment to the Act on Medical Technologist etc. made field training mandatory to obtain a radiologic technologist license. In accordance with Article 9 of the Medical Technologist Act, which prohibits unlicensed persons from practicing, 50% of the respondents were aware that those who are in training to complete an education course equivalent to the license they are seeking to obtain at a university or other institution are allowed to practice as medical Technologists. When asked what is currently taught during fieldwork, 6% of respondents said that they are required to perform radiation-generating activities in addition to observing, guiding patients, and positioning and moving patients. When asked about the future direction of education as fieldwork becomes mandatory for licensure, 77% of respondents said that they will teach more than they currently do. When asked about the appropriate total length of fieldwork, 35% said 12 weeks and 480 hours, 33% said 8 weeks and 320 hours, and 27% said 16 weeks and 640 hours. It can be seen that the current on-the-job training is inadequate according to various regulations, and students' satisfaction is low. However, with the revision of the Act on Medical Technologists, field training has become mandatory to obtain a license as a radiologist, and it is necessary to improve the educational conditions of field training. Therefore, it is necessary to comply with the Nuclear Safety Act and the Rules on the Safety Management of Diagnostic Radiation Generating Devices, introduce standardized training objectives and evaluation systems, designate training hospitals and radiologists in charge of training, and introduce extended training periods and simulation exercises to internalize field training.

• Journal of Food Hygiene and Safety
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• v.39 no.1
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• pp.54-60
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• 2024

This study investigated the effectiveness of using pathogens and aqueous acids to reduce the Aspergillus ochraceus and Rhodotorula mucilaginosa contamination in livestock production environments. For this study, 1 mL of each bacterial suspension (10⁷-10⁸ spores/mL) was inoculated on a knife surface, dried at 37℃, and used under each treatment condition. First, to investigate the effect of organic acids, acetic, lactic, and citric acids were used. Subsequently, to select the appropriate concentration, they were prepared at concentrations of 0.5, 1, 2, 3, 4, and 5%, respectively. Accordingly, to further maximize the effect of organic acid treatment, we combined the treatment with ultraviolet light. The two strains showed a significant difference (P<0.05) compared to the initial strain, with a greater than 90% decrease in the concentrations of all organic acids. Consequently, acetic and lactic acids decreased by approximately 5 and 2 log colony forming unit (CFU)/cm², respectively, when treated with ultraviolet light (360 mJ/cm²); however, citric acid decreased by less than 1 log CFU/cm². However, when manufactured with 4% acetic acid, a severe malodor was emitted, making it difficult for workers to use it in a production environment. Accordingly, the optimal treatment conditions for organic acid and ultraviolet light for application were selected as follows: immersion in a 4% lactic acid solution for 1 minute and then, sterilization with ultraviolet light at 360 mJ/cm². Finally, when a pork meat sample was cut with a knife that was finally washed with lactic acid and treated with ultraviolet light, the low level of inoculum transferred from the cleaned knife to the surface of the sample was not detected. In conclusion, using this established method can prevent cross-contamination of the surface of the meat during processing.

• Research in Plant Disease
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• v.30 no.1
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• pp.66-77
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• 2024

Black wood ear mushroom (Auricularia auricula-judae) is one of the most economically important mushrooms in China, Japan, and Korea. The cultivation of wood ear mushrooms on artificial substrates is more efficient in terms of time and cost compared with their natural growth on trees. However, if the substrate cultivation is infected by fast-growing fungi, the relatively slow-growing ear mushroom will be outcompeted, leading to economic losses. In this study, we investigated the competitive fungal isolates from substrates infected with fast-growing fungi for the cultivation of ear mushrooms in Jangheung and Sunchon, Korea. We collected 54 isolates and identified them by sequencing their internal transcribed spacer region with morphological identification. Among the isolates, the dominant isolates were Trichoderma spp. (92.6%), Penicillium spp. (5.6%), and Talaromyces sp. (1.8%). To find an appropriate eco-friendly biocontrol agent, we used five Streptomyces spp. and Benomyl, as controls against Trichoderma spp. and Penicillium spp. Among the six Streptomyces spp., Streptomyces sp. JC203-3 effectively controlled the fungi Trichoderma spp. and Penicillium spp., which pose a significant problem for the substrates of black wood ear mushrooms. This result indicated that this Streptomyces sp. JC203-3 can be used as biocontrol agents to protect against Trichoderma and Penicillium spp.

• Journal of the Korean Society of Radiology
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• v.85 no.1
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• pp.138-146
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• 2024

Purpose To evaluate whether the image quality of chest radiographs obtained using a camera-type portable X-ray device is appropriate for clinical practice by comparing them with traditional mobile digital X-ray devices. Materials and Methods Eighty-six patients who visited our emergency department and underwent endotracheal intubation, central venous catheterization, or nasogastric tube insertion were included in the study. Two radiologists scored images captured with traditional mobile devices before insertion and those captured with camera-type devices after insertion. Identification of the inserted instruments was evaluated on a 5-point scale, and the overall image quality was evaluated on a total of 20 points scale. Results The identification score of the instruments was 4.67 ± 0.71. The overall image quality score was 19.70 ± 0.72 and 15.02 ± 3.31 (p < 0.001) for the mobile and camera-type devices, respectively. The scores of the camera-type device were significantly lower than those of the mobile device in terms of the detailed items of respiratory motion artifacts, trachea and bronchus, pulmonary vessels, posterior cardiac blood vessels, thoracic intervertebral disc space, subdiaphragmatic vessels, and diaphragm (p = 0.013 for the item of diaphragm, p < 0.001 for the other detailed items). Conclusion Although caution is required for general diagnostic purposes as image quality degrades, a camera-type device can be used to evaluate the inserted instruments in chest radiographs.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.2
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• pp.41-46
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• 2011

Purpose: The result of exam using an imaging device is very closely related with the image quality. Moreover, this image quality can be changed according to the condition of image acquisition and evaluation method. In this study, we evaluated the image quality according to the difference of pre-set method in PET/CT image. Materials & Methods: PET/CT Discovery STe16 (GE Healthcare, Milwaukee, USA), Chest PET phantom (Experiment 1) and 94 NEMA phantom (Experiment 2) were used. Phantom were filled with $^{18}F$-FDG maintaining hot sphere and background ratio to 4:1. In the case of experiment 1, we set the radio activity concentration on 3.5, 6.0, 8.6 kBq/mL. In the case of experiment 2, we set the radio activity concentration on 3.3, 5.5, 7.7, 9.9, 12.1, 16.5 kBq/mL. All experiments were performed with the time-set method for 2 minutes 30 seconds per frame and the count-set method with one hundred million counts in 3D mode after CT transmission scan. For the evaluation of the image quality, we compared each results by using the NECR and SNR. Results: In the experiment 1, both the NECR and SNR were increased as radioactivity concentration getting increased. The NECR was shown as 53.7, 66.9, 91.4. and SNR was shown as 7.9, 10.0, 11.7. Both the NECR and SNR were increased in time-set method. But the count-set method's pattern was not similar with the time-set method. The NECR was shown as 53.8, 69.1, 97.8, and SNR was shown as 14.1, 14.7 14.4. The SNR was not increased in count-set method. In experiment 2, results of both the NECR and SNR were shown as 45.1, 70.6, 95.3, 115.6, 134.6, 162.2 and 7.1, 8.8, 10.6, 11.5, 12.7, 14.0. These results were shown similar patten with the experiment 1. Moreover, when the count-set method was applied, the NECR was shown as 42.1, 67.3, 92.1, 112.2, 130.7, 158.7, and SNR was shown as 15.2, 15.9, 15.6, 15.4, 15.5, 14.9. The NECR was increased but SNR was not shown same pattern. Conclusion: Increment of administered radioactivity improves the quality of image unconcerned with the pre-set method. However, NECR was not influenced by increment of total acquisition counts through simple increasing scan duration without increment of administered activity. In case of count-set method, the SNR was shown similar value despite of increment of radioactivity. So, the administered activity is more important than the scan duration. And we have to consider that evaluation of image quality using only SNR may not be appropriate.

• Horticultural Science & Technology
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• v.32 no.1
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• pp.53-59
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• 2014

This study aimed to determine an appropriate cooling timing in the root zone for lowering substrate temperature and its effect on physiological response of sweet pepper (Capsicum annum L. 'Orange glory') grown on coir substrate in summer, from the July 16 to October 15, 2012. Daily temperature of substrate, root activity, leaf water potential, first flowering date, and the number of fruits were measured by circulating cool water through a XL pipe in the root zone during either all day (all-day) or only night time (5 p.m. to 3 a.m.; night) from the July 23 to September 23, 2012. For comparison, no cooling (control) was also applied. Between the $23^{rd}$ of July and $31^{st}$ of August (hot temperature period), daily average temperatures in substrates were $25.6^{\circ}C$, $26.1^{\circ}C$, and $29.1^{\circ}C$ for the all-day and night treatment, and control respectively. About 1.8 to $5^{\circ}C$ lower substrate temperature was observed in both treatments compared to that of control. In sunny day ($600-700 W{\cdot}m^{-2}{\cdot}s^{-1}$), the highest temperature of substrate was measured between 4 p.m. and 5 p.m. under both the all-day and night treatments, whereas it was measured between 7 p.m. and 8 p.m. under the control. Substrate temperatures during the day (6 a.m. to 8 p.m.) and night (8 p.m. to 6 a.m.) differed depending on the treatments. During the day and night, averaged substrate temperature was lower about $3.3^{\circ}C$ and $4.0^{\circ}C$ for the all-day, and $2.1^{\circ}C$ and $3.4^{\circ}C$ for the night treatment, compared to that of control. In the all-day and night treatment, the TD [TD = temperature of (control)] was greater in bottom than that of other regions of the substrate. Between the day and night, no different TD values were observed under the all-day treatment, whereas under the night treatment there was difference with the greatest degree in the bottom of the substrate. During the hot temperature period, total numbers of days when substrate temperature was over $25^{\circ}C$ were 40, 23 and 27 days for the control, all-day, and night treatment, respectively, and the effect of lowering substrate temperature was therefore 42.5% and 32.5% for the all-day and night treatment, respectively, compared to that for the control. Root activity and leaf water potential of plants grown under the all-day treatment were significantly higher than those under the night treatment. The first flowering date in the all-day treatment was similar to that in the night treatment, but 4-5 day faster than in the control. Also, the number of fruits in both treatments was significantly higher than that in the control. However, there was no effect of root zone cooling on eliminating delay in fruiting caused by excessively higher air temperature (> $30^{\circ}C$), although the substrate temperature was reduced $18^{\circ}C$ to $5^{\circ}C$. These results suggest that the method of cooling root zone temperature need to be incorporated into the lowering growing temperature for growth and fruit set of health paprika.

• Journal of Global Scholars of Marketing Science
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• v.19 no.4
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• pp.1-10
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• 2009

The purpose of this study was to categorize the gap between coffee shop 'importance' (as perceived by customers before patronizing the coffee shop) and 'satisfaction' (perception of customers after patronizing the coffee shop) as positive or negative and to analyze the effect of these gaps on purchasing behavior. To do this, I used the gap between importance and satisfaction regarding the choice of a coffee shop as the explanatory variable and performed an empirical analysis of the direction and size of the effect of the gap on purchasing behavior (overall satisfaction, willingness-to-revisit) by applying the Ordered Probit Model (OPM). A previous study that used IPA to evaluate the effects of gaps estimated the direction and size of a quadrant but failed to analyze the effect of gaps on customers. In this study, I evaluated the effects of positive and negative gaps on customer satisfaction and willingness-to-revisit. Using OPM, I quantified the effect of positive and negative gaps on overall customer satisfaction and willingness-to-revisit. Per-head expenditure, frequency of visits, and coffee-purchasing place had the most positive effects on overall customer satisfaction. Frequency of visits, followed by per-head expenditure and then coffee-purchasing place, had the most positive impact on willingness-to-visit. Thus per-head expenditure and frequency of visits had the greatest positive effects on overall satisfaction and willingness-to-revisit. This finding implies that the higher the actual satisfaction (gap) of customers who spend KRW5,000 or more once or more per week at coffee shops is, the higher their overall satisfaction and willingness-to-revisit are. Despite the fact that economical efficiency had a significant effect on overall satisfaction and willingness-to-revisit, college and university students still use coffee shops and are willing to spend KRW5,000 because they do not only purchase coffee as a product itself, but use the coffee shop for other activities, such as working, meeting friends, or relaxing. College and university students also access the Internet in coffee shops via personal laptops, watch movies, and study; thus, coffee shops should provide their customers with the appropriate facilities and services. The fact that a positive gap for coffee shop brand had a positive effect on willingness-to-revisit implies that the higher the level of customer satisfaction, the greater the willingness-to-revisit. A negative gap for this factor, on the other hand, implies that the lower the level of customer satisfaction, the lower the willingness-to-revisit. Thus, the brand factor has a comparatively greater effect on satisfaction than the other factors evaluated in this study. Given that the domestic coffee culture is becoming more upscale and college/university students are sensitive to this trend, students are attentive to brands. In most upscale coffee shops in Korea, the outer wall is built out of glass that can be opened, the interiors are exotic with an open kitchen. These upscale coffee shops function as landmarks and match the taste of college/university students. Coffee shops in Korea have become a cultural brand. To make customers feel that coffee shops are upscale, good quality establishments and measures to provide better services in terms of brand factor should be instituted. The intensified competition among coffee shop brands in Korea as a result of the booming industry indicates that provision of additional services is needed to differentiate competitors. These customers can also use a scanner free of charge. Another strategy that can be used to boost brands could be to provide and operate a seminar room for seminars and group study. If coffee shops adopt these types of strategies, college/university students would be more likely to consider the expenses they incur worthwhile and, subsequently, they would be more likely to be satisfied with the brands of these coffee shops, with an associated increase in their willingness-to-revisit. Gender and study year had the most negative effects on overall satisfaction and willingness-to-revisit. Female students were more likely to be satisfied and be willing to return than male students, and third and fourth-year students were more likely to be satisfied and willing-to-return than first or second-year students. Students who drink coffee, read books, and use laptops alone at coffee shops are easily noticeable. High-grade students tend to visit coffee shops alone in order to use their time efficiently for self-development and to find jobs. The economical efficiency factor had the greatest effect on overall satisfaction and willingness-to-revisit in terms of a positive gap. The higher the actual satisfaction (gap) of students with the price of the coffee, the greater their overall satisfaction and willingness-to-revisit. Economical efficiency with a negative gap had a negative effect on willingness-to-revisit, which implies that a less negative gap will result in a greater willingness-to-revisit. Amid worsening market conditions, coffee shops located around colleges/universities are using strategies, such as a point or membership card, strategic alliances with credit-card companies, development of a set menu or seasonal menu, and free coffee-shot services to increase their competitive edge. Product power also had a negative effect in terms of a negative gap, which indicates that a higher negative gap will result in a lower willingness-to-revisit. Because there are many more customers that enjoy coffee in this decade, as compared to previous decades, the new generation of customers, namely college/university students, want various menu items in addition to coffee, and coffee shops should, therefore, add side menu items, such as waffles, rice cakes, cakes, sandwiches, and salads. For example, Starbucks Korea is making efforts to enhance product power by selling rice cakes flavored in strawberry, wormwood, and pumpkin, and providing coffee or cream free of charge. In summary, coffee shops should focus on increasing their economical efficiency, brand, and product power to enhance the satisfaction of college/university students. Because shops adjacent to colleges or universities enjoy a locational advantage, providing differentiated services in terms of economical efficiency, brand, and product power, is likely to increase customer satisfaction and return visits. Coffee shop brands should, therefore, be innovative and embrace change to meet their customers' desires. Because this study only targeted college/university students in Seoul, comparative studies targeting diverse regions and age groups are required to generalize the findings and recommendations of this study.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.143-163
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• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.