• Journal of Intelligence and Information Systems
• /
• v.18 no.1
• /
• pp.125-141
• /
• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Intelligence and Information Systems
• /
• v.16 no.3
• /
• pp.147-161
• /
• 2010

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.

• Journal of Intelligence and Information Systems
• /
• v.16 no.3
• /
• pp.181-199
• /
• 2010

As rapid progress of population aging and strong interest in health, the demand for new healthcare service is increasing. Until now healthcare service has provided post treatment by face-to-face manner. But according to related researches, proactive treatment is resulted to be more effective for preventing diseases. Particularly, the existing healthcare services have limitations in preventing and managing metabolic syndrome such a lifestyle disease, because the cause of metabolic syndrome is related to life habit. As the advent of ubiquitous technology, patients with the metabolic syndrome can improve life habit such as poor eating habits and physical inactivity without the constraints of time and space through u-healthcare service. Therefore, lots of researches for u-healthcare service focus on providing the personalized healthcare service for preventing and managing metabolic syndrome. For example, Kim et al.(2010) have proposed a healthcare model for providing the customized calories and rates of nutrition factors by analyzing the user's preference in foods. Lee et al.(2010) have suggested the customized diet recommendation service considering the basic information, vital signs, family history of diseases and food preferences to prevent and manage coronary heart disease. And, Kim and Han(2004) have demonstrated that the web-based nutrition counseling has effects on food intake and lipids of patients with hyperlipidemia. However, the existing researches for u-healthcare service focus on providing the predefined one-way u-healthcare service. Thus, users have a tendency to easily lose interest in improving life habit. To solve such a problem of u-healthcare service, this research suggests a u-healthcare recommender system which is based on collaborative filtering principle and social network. This research follows the principle of collaborative filtering, but preserves local networks (consisting of small group of similar neighbors) for target users to recommend context aware healthcare services. Our research is consisted of the following five steps. In the first step, user profile is created using the usage history data for improvement in life habit. And then, a set of users known as neighbors is formed by the degree of similarity between the users, which is calculated by Pearson correlation coefficient. In the second step, the target user obtains service information from his/her neighbors. In the third step, recommendation list of top-N service is generated for the target user. Making the list, we use the multi-filtering based on user's psychological context information and body mass index (BMI) information for the detailed recommendation. In the fourth step, the personal information, which is the history of the usage service, is updated when the target user uses the recommended service. In the final step, a social network is reformed to continually provide qualified recommendation. For example, the neighbors may be excluded from the social network if the target user doesn't like the recommendation list received from them. That is, this step updates each user's neighbors locally, so maintains the updated local neighbors always to give context aware recommendation in real time. The characteristics of our research as follows. First, we develop the u-healthcare recommender system for improving life habit such as poor eating habits and physical inactivity. Second, the proposed recommender system uses autonomous collaboration, which enables users to prevent dropping and not to lose user's interest in improving life habit. Third, the reformation of the social network is automated to maintain the quality of recommendation. Finally, this research has implemented a mobile prototype system using JAVA and Microsoft Access2007 to recommend the prescribed foods and exercises for chronic disease prevention, which are provided by A university medical center. This research intends to prevent diseases such as chronic illnesses and to improve user's lifestyle through providing context aware and personalized food and exercise services with the help of similar users'experience and knowledge. We expect that the user of this system can improve their life habit with the help of handheld mobile smart phone, because it uses autonomous collaboration to arouse interest in healthcare.

• Journal of agriculture & life science
• /
• v.44 no.2
• /
• pp.7-15
• /
• 2010

This study was conducted to investigate the bacteriological and physiological quality of seawater and surface sediments in Sacheon Bay of Korea from January to September in 2009. During the study period, the means of temperature was range from 5.3 to $24.9^{\circ}C$ (mean $17.7{\pm}0.4^{\circ}C$), transparency range from 1.4 to 2.5 m (mean $1.8{\pm}0.5m$), suspended solid ranged from 16.2 to 35.8 mg/L (mean $24.2{\pm}2.2mg/L$), chemical oxygen demand ranged from 1.42 to $3.29mgO_2/L$ (mean $2.06{\pm}0.55mgO_2/L$), dissolved oxygen ranged from 6.7 to 9.5mg/L (mean $7.9{\pm}0.6mg/L$), respectively. Seafood, if eaten raw, carries the risk of food poisoning. Seafood poisoning is often cause by pathogenic microorganism originating from fecal contamination, such as Salmonella sp., Shigella sp. and norovirus. Fecal coliforms are an important indicator of fecal contamination. Therefor, data on fecal coliform are very important for evaluating the safety of fisheries in coastal areas. So, we investigated the sanitary indicate bacteria. In this study, 56 sea water samples were collected from the Sacheon Bay, and total and fecal coliforms were compared and analyzed. The coliform group and fecal coliform MPN's of sea water in Sacehon Bay were ranged from <1.8~7,900 MPN/100mL (GM 214.7 MPN/100mL) and <1.8~330 MPN/100mL (GM 9.7 MPN/ 100mL), respectively. Total coliforms were detected in 75.0% of the samples and 76.2% of the total coliforms were fecal coliforms. During the study period, the means of water content, ignition loss, COD, and acid volatile sulfates in sediments in Sacheon Bay were $53.28{\pm}2.58%$, $9.38{\pm}0.42%$, $14.23{\pm}3.36mgO_2/g$, $0.09{\pm}0.07mgS/g$, respectively.

• Geophysics and Geophysical Exploration
• /
• v.8 no.1
• /
• pp.50-58
• /
• 2005

Recently, the imaging of geological structures beneath water-covered areas has been in great demand because of numerous tunnel and bridge construction projects on river or lake sites. An electrical resistivity survey can be effective in such a situation because it provides a subsurface image of faults or weak zones beneath the water layer. Even though conventional resistivity surveys in water-covered areas, in which electrodes are installed on the water bottom, do give high-resolution subsurface images, much time and effort is required to install electrodes. Therefore, an easier and more convenient method is sought to find the strike direction of the main zones of weakness, especially for reconnaissance surveys. In this paper, we investigate the applicability of the streamer resistivity survey method, which uses electrodes in a streamer cable towed by ship or boat, for delineating a fault zone. We do this through numerical experiments with models of water-covered areas. We demonstrate that the fault zone can be imaged, not only by installing electrodes on the water bottom, but also by using floating electrodes, when the depth of water is less than twice the electrode spacing. In addition, we compare the signal-to-noise ratio and resolving power of four kinds of electrode arrays that can be adapted to the streamer resistivity method. Following this numerical study, we carried out both conventional and streamer resistivity surveys for the planned tunnel construction site located at the Han River in Seoul, Korea. To obtain high-resolution resistivity images we used the conventional method, and installed electrodes on the water bottom along the planned route of the tunnel beneath the river. Applying a two-dimensional inversion scheme to the measured data, we found three distinctive low-resistivity anomalies, which we interpreted as associated with fault zones. To determine the strike direction of these three fault zones, we used the quick and convenient streamer resistivity.

• Korean Journal of Psychosomatic Medicine
• /
• v.24 no.1
• /
• pp.66-73
• /
• 2016

Objectives : The purpose of this study was to investigate the characteristics of psychosocial factors related to functional dyspepsia(FD) and their effects on quality of life(QOL) in firefighters. Methods : This study examined data collected from 1,217 firefighters. We measured psychological symptoms by Patient Health Questionnaire-9(PHQ-9), Generalized Anxiety Disorder questionnaire(GAD-7), Korean Occupational Stress Scale(KOSS), Ways of Coping checklist(WCCL), Rosenberg's Self-Esteem Scale(RSES) and World Health Organization Quality of Life Scale abbreviated version(WHOQOL-BREF). Chi-square test, independent t-test, Pearson's correlation test, logistic regression analysis, and hierarchical regression analysis were used as statistical analysis methods. Results : For the group with FD, the male participants showed significantly higher frequency(p=0.006) compared to the female participants. The group with FD had higher scores for depressive symptoms(p<.001), anxiety (p<.001), and occupational stress(p<.001), and did lower scores for self-esteem(p=.008), quality of life(p<.001) than those without FD. The FD risk was higher in the following KOSS subcategories: job demand(OR 1.94, 95% CI : 1.29-2.93), lack of reward(OR 2.47, 95% CI : 1.61-3.81), and occupational climate(OR 1.51, 95% CI : 1.01-2.24). In the hierarchical regression analysis, QOL was best predicted by depressive symptoms, self-esteem, and occupational stress. Three predictive variables above accounts for 42.0% variance explained of total variance. Conclusions : The psychosocial factors showed significant effects on FD, and predictive variables for QOL were identified based on regression analysis. The results suggest that the psychiatric approach should be accompanied with medical approach in future FD assessment.

• The Journal of KAIRB
• /
• v.3 no.2
• /
• pp.19-27
• /
• 2021

Purpose: This study aimed to analyze the knowledge levels, attitudes, and factors affecting the choices on the education of the participants who completed their education of persons conducting clinical trial workers, and to assess the problems of the current education system for clinical trial workers, leading to improvements. Methods: Clinical trial workers (including principal investigators/subinvestigators, members of the Institutional Review Board [IRB], clinical research coordinators) who were affiliated to one of the 4 university hospitals running their own clinical trial center and IRB in Daegu and completed their education of persons conducting clinical trial workers were the subjects of this study. One hundred seven online questionnaires were answered from 2021-04-02 to 2021-04-17. Descriptive statistics and Pearson correlation analysis were used to analyze the acquired data. Independent t-test and 1-way analysis of variance were used to analyze the differences in the knowledge levels and attitudes following the characteristics of the education participants. Results: The baseline characteristics of the 107 participants were as follows: the majority of the participants were female (72.0%), were in their 30s (36.4%), had a nursing major (29.0%), were clinical research coordinators (63.6%), had never experienced a principal investigator (79.4%), had participated 3 or more educations (58.9%), had completed their maintenance course (55.1%), had 5 or more years of clinical trial experiences (34.6%). The fields on which participants had low levels of objective knowledge were "types and preparations on audits of clinical trials," "regulations on clinical trials (Pharmaceutical Affairs Act, Korea Good Clinical Practice)." The difficulties that the participants faced were on "annual educations" and "lack of information regarding the educations." Factors that showed significant differences in objective knowledge were sex (p=0.02), number of educations (p=0.004), the curriculum of 2020 (p=0.001). Age (p=0.004), having experienced a principal investigator (p=0.006), number of educations (p<0.001), the curriculum of 2020 (p<0.001), clinical trial career (p=0.001) were factors that significantly affected subjective knowledge. Attitudes toward the education were positively correlated with objective knowledge (r=0.20, p=0.04) and subjective knowledge (r=0.32, p=0.001). Major sources through which information on educations was acquired were "institutional notices," and major factors affecting the choices on the education were "when the education took place" and "where the education took place." "Within the affiliated institution," "Online classes (recorded)" and "IRB and review processes" were each the most preferred place, mode, and content of the education. Conclusion: Knowledge levels varied largely among participants who completed their education of persons conducting clinical trial workers, depending on their characteristics such as the number of educations. Participants also complained about their lack of information on educations. The quality of education may be improved if clinical trial organizations are designated as education facilities. Education programs must be developed considering the knowledge level and demand of the participants. Furthermore, as offline classes may be impossible due to pandemics such as the coronavirus disease 2019, the development of diverse and sophisticated online classes is looked forward to.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.5
• /
• pp.456-467
• /
• 2019

The rapid aging of South Korea is causing difficulties in meeting the man-power demands for the increasing number of elderly nursing hospitals. To cope with this gap in supply and demand, more foreign workers are now being hired to provide elderly care services. The purpose of this study is to examine the influence of the quality of the care services provided by foreign caregivers in nursing hospitals for the elderly. For this purpose, the researcher surveyed 249 senior citizens who are receiving care services from Chinese-Korean caregivers in six elderly nursing hospitals in Seoul and Gyeonggi region. The data collected from the survey were analyzed through SPSS and AMOS. The result of the analyses showed that, first, the quality factors of the care services of the caregivers at elderly nursing hospitals, such as reliability, responsibility, empathy, formality, and expertise, all turned out to have a positive correlation with the trust in, satisfaction with, and intent to revisit the institute. Second, thefluencing factors for the qualities of the care services by Chinese-Korean care givers in elderly nursing hospitals included responsiveness, materiality, and expertise, while the factors that influenced satisfaction with significance included trust, materiality, and expertise. Also, the trust in and satisfaction with the institution both influenced the intent of revisit in a positive manner. Third, the indirect effect of trust in the relationship between the quality of the care services by the Chinese-Korean care givers and satisfaction, appeared in all independent variable except for responsiveness, which was a factor of the quality of the care services. And, it was also shown that the satisfaction level had an indirect effect in the relationship between trust and revisiting intent. The result of this study implicates that, in order to cause the quality of the care service by the Chinese-Korean care givers in elderly nursing hospitals to increase the revisit rate, it would be necessary to provide a strategy to increase the levels of trust and satisfaction through a higher quality level of care services.

• Korean Journal of Environment and Ecology
• /
• v.33 no.4
• /
• pp.481-492
• /
• 2019

The Korean Ministry of Culture, Sports and Tourism has held "Travel Week" since 2014 to encourage the people to take a vacation and disperse the seasonal tourism demand that is concentrated in summer in Korea. As part of the program, the Korea National Park Service has also operated the participatory lowland exploration program that offers nature-themed attractions and enjoyment in national parks across the country during the "Travel Week" since 2018. The purpose of this study was to investigate the satisfaction with the program and intention to participate again of participants in the "National Park Spring Week Program" which is held in national parks during the "Travel Week." We conducted a self-report survey of 1,281 participants in the "2019 National Park Spring Week Program" held in 18 national parks across the country. The analysis of responses on the difference in the participants' satisfaction and intention to participate again according to the awareness in advance of the "2019 National Park Spring Week Program" showed that the average satisfaction and intentional to participate again of those who were aware of the program before visiting national parks were statistically significantly higher than those who were not. As for the type of national parks, those who participated in "maritime and coastal national parks" and "historical national parks" showed the statistically significantly higher satisfaction and intention to participate again than those who participated in "urban national parks." As for the type of the programs, those who participated in "cultural performance" and "exploration experience" showed the statistically significantly higher satisfaction than those who participated in "exhibition," "PR booth," and "campaign." Those who participated in "cultural performance" and "exploration experience" showed the statistically significantly higher intention to participate again than those who participated in "exhibition" and "PR booth." This study is expected to provide basic data for establishing a policy to improve exploration services in response to the increasing number of visitors to national parks in spring and fall as well as the peak season of summer.

• Management & Information Systems Review
• /
• v.38 no.1
• /
• pp.23-41
• /
• 2019

Smart Factory is different from existing factory automation in that it aims to produce personalized products with minimum time and cost through ICT. However, previous researches, not from consumers but from product suppliers, have focused on technology trends and technology application methods. In order for Smart Factory to be successful, it must go beyond supplier-focus to meet the needs of consumers. In this study, we surveyed the purchase intention of the personalized product manufactured by smart factory. Influencing factors of purchase intention were drawn as consumers' need for uniqueness, innovativeness, need for touch, and privacy concern, based on previous research. As results of data analysis, it was confirmed that respondents were willing to purchase personalized products, and that consumers' need for uniqueness, innovativeness, and need for touch had a significant impact on purchase intention of personalized products. Our findings can be summarized as follows. First, Consumers' need for uniqueness was found to have positive effects(${\beta}=0.168$) on purchase intention of personalized products. The desire to differentiate themselves from others will be reflected in their personalized products. Therefore, consumers with a higher desire for uniqueness tend to be more willing to purchase personalized products. Second, consumer innovativeness was found to have positive effects(${\beta}=0.233$) on purchase intention of personalized products. Personalized shoes suggested in this study is a new type of personalized product that is manufactured by the latest information and communication technologies such as multi-function robots and 3D printing. Therefore, consumers seeking innovative new experiences are more willing to purchase personalized products. Third, need for touch was found to have positive effects(${\beta}=0.299$) on purchase intention of personalized products. In a smart factory environment, prosuming participation is given to consumers. If consumers participate in the product development process and reflect their requirements on the product, they are expected to increase their purchase intention by virtually satisfying the need for touch. Fourth, privacy concern was found to have no significantly related to purchase intention of personalized products. This is interpreted as a willingness to tolerate the risk of exposing personal information such as home address, telephone number, body size, and preference for consumers who feel highly useful in personalized products.