• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1672-1683
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• 2004

Recommender system is a kind of web intelligence techniques to make a daily information filtering for people. Researchers have developed collaborative recommenders (social recommenders), content-based recommenders, and some hybrid systems. In this paper, we introduce a new hybrid recommender method - ICHM where clustering techniques have been applied to the item-based collaborative filtering framework. It provides a way to integrate the content information into the collaborative filtering, which contributes to not only reducing the sparsity of data set but also solving the cold start problem. Extensive experiments have been conducted on MovieLense data to analyze the characteristics of our technique. The results show that our approach contributes to the improvement of prediction quality of the item-based collaborative filtering, especially for the cold start problem.

• Journal of the Korean Institute of Educational Facilities
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• v.21 no.4
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• pp.21-30
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• 2014

In general, teachers' perceptions on the physical environment of elementary school are related on the quality of education and school life. But, we don't have study what teachers think of their enviroment of school. This study analyzed elementary school teachers' satisfaction with overall physical environment and their assessment of spaces with specific functions in order to understand teachers' perception of the physical environment of elementary schools. A survey questionnaire was administered to 982 homeroom teachers working at 67 elementary schools located in Seoul and Busan. Teachers' satisfaction with physical environment of school and their assessment were analyzed comparatively according to the teachers' personal attributes and school conditions. 1) Most of the surveyed teachers were satisfied with overall physical environment of the school, and assessed high the substantiality of functional spaces indoor and outdoor. By sub-factor, however, their satisfaction with work environment was lower than that with education and living environment. 2) The teachers perceived that the optimal number of classes was 30~36, and this is probably because they were relatively accustomed to large scale schools. 3) As might be expected, negative assessments were more frequent among teachers working at a large-scale school, an old school, or a school with small outdoor spaces. The results of this study show that, though partially different according to region, gender, teaching experience and school condition, teachers' satisfaction with school environment was relatively high. This suggests that elementary school teachers perceive their current school environment positively. Because they are accustomed to traditional education methods that have been maintained.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.297-304
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• 2003

Nowdays, with the explosive growth of the web information, web users Increase requests of systems which collect and analyze web pages that are relevant. The systems which were develop to solve the request were used clustering methods to improve the duality of information. Clustering is defining inter relationship of unordered data and grouping data systematically. The systems using clustering provide the grouped information to the users. So, they understand the information efficiently. We proposed a hybrid clustering method to cluster a large quantity of data efficiently. By that method, We generate initial clusters using COBWEB Algorithm and refine them using Ezioni Algorithm. This paper adds two ideas in prior hybrid clustering method to increment accuracy and efficiency of clusters. Firstly, we propose the clustering method considering weight of attributes of data. Second, we redefine evaluation functions which generate initial clusters to increase efficiency in clustering. Clustering method proposed in this paper processes a large quantity of data and diminish of dependancy on sequence of input of data. So the clusters are useful to make user profiles in high quality. Ultimately, we will show that the proposed clustering method outperforms the pervious clustering method in the aspect of precision and execution speed.

• Korean Journal of Food Science and Technology
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• v.23 no.3
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• pp.296-300
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• 1991

Seven different cultivars of Korean red pepper, Capsicum annuum L., were collected from different growing areas and analyzed for pungent principles, redness, size and weight. Length of the pod varied in the range of $7.0{\sim}9.4\;cm$ total weight, $1.2{\sim}3.1\;g$, capsaicin content, $11.5{\sim}45.0\;mg%$, dihydrocapsaicin content, $8.5{\sim}35.1mg%$, and redness (absorbance at 460 nm), $6,270{\sim}7,810$ conventional color unit. Contents of capsaicins and redness were significantly different according to the cultivars but the two components were not correlated each other. According to the growing areas, contents of capsaicins and redness were significantly different. Capsaicins content was not significantly different but redness was significantly different between sun-drying and hot air-drying of the pods.

• Journal of the Korean Society of Food Culture
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• v.10 no.4
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• pp.327-337
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• 1995

The purposes of this study were to: (a) identify college students' patronage behaviors, (b) develop an instrument measuring the attitudes of University Students towards university foodservices management practices, (c) determine university students' attitude towards the four types of university foodservices, and (d) provide recommendations on marketing strategies for university foodservice. Questionnaires were hand delivered to 600 Yonsei University students by designated coordinators. A total of 549 questionnaires were usable; resulting in an 93.3% response rate. The survey was conducted between November 28 to December 4, 1995. Statistical data analysis was completed using the SAS Programs for descriptive analysis, T-test, ${\chi}^2$ test, ANOVA, Factor Analysis and Stepwise Multiple Regression. Most (88.3%) of students were patronizing university foodservices for lunch. Underground student foodservice (40.1%) and Restaurants outside the campus (33.7%) were primarily used for lunch and dinner respectively. Eighty six percent of university students had 1 to 2 meals per day at university foodservices. The reasons given by students for patronizing university foodservices were as follows: location, time, price, menu, taste. Most of the respondents were least satisfied with hygiene, taste, menu and atmosphere. Data indicated strong support for eight priori dimensions in terms of food, menu, atmosphere, hygiene, employee attitude, facilities and convenience. After the factor analysis, price, fast service and foodservice location attributes were rearranged, combined and created a new dimension called as 'access'. Three dimensions in terms of menu, hygiene, convenience were important to students although performance was perceived as poor through importance-performance analysis. Most of students were not satisfied with all four types of university foodservices. In terms of food quality and price which university foodservices offer, most of respondents were moderately satisfied. According to multiple regression analysis, 93.31% of the variance respondents' satisfaction score could be explained by food, menu, price, atmosphere, hygiene, employee attitude, facilities, and convenience dimensions.

• Food Science of Animal Resources
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• v.33 no.1
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• pp.89-95
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• 2013

This research was conducted to compare the meat characteristics of Korean native duck and imported commercial duck. The Korean native ducks and imported commercial ducks (broiler duck: Grimaud) were raised under identical rearing and feeding conditions for 8 wk and 6 wk, respectively. At the end of the rearing period, ten ducks from each group were slaughtered, and breast and leg meat were obtained 24 h after slaughtering for analyses of meat characteristics. The results showed that the breast of Korean native ducks contained lower moisture and fat, and higher protein and water holding capacity (WHC) than those of imported commercial ducks (p<0.05). The breast of Korean native ducks also had higher CIE $a^*$ and lower CIE $L^*$ (p<0.05). After cooking, the breast meat of Korean native ducks had higher shear-force, sensory attributes of texture, taste and overall likeness (p<0.05). Also, the breast meat of Korean native ducks contained a higher percentage of palmitic acid ($C_{16:0}$) and arachidonic acid ($C_{20:4}$) (p<0.05) than those of imported commercial ducks. Furthermore, the leg meat of Korean native ducks contained higher percentages of total unsaturated fatty acid and lower percentages of total saturated fatty acid (p<0.05). It is concluded that the meat from Korean native ducks, especially breast meat, had better quality parameters and contained higher amounts of unsaturated fatty acids.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.915-934
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• 2020

Software refactoring is a process to restructure an existing software code while keeping its external behavior the same. Currently, various refactoring techniques are being used to develop more readable and less complex codes by improving the non-functional attributes of software. Refactoring can further improve code maintainability by applying various techniques to the source code, which in turn preserves the behavior of code. Refactoring facilitates bug removal and extends the capabilities of the program. In this paper, an exhaustive review is conducted regarding bad smells present in source code, applications of specific refactoring methods to remove that bad smell and its effect on software quality. A total of 68 studies belonging to 32 journals, 31 conferences, and 5 other sources that were published between the years 2001 and 2019 were shortlisted. The studies were analyzed based on of bad smells identified, refactoring techniques used, and their effects on software metrics. We found that "long method", "feature envy", and "data class" bad smells were identified or corrected in the majority of studies. "Feature envy" smell was detected in 36.66% of the total shortlisted studies. Extract class refactoring approach was used in 38.77% of the total studies, followed by the move method and extract method techniques that were used in 34.69% and 30.61% of the total studies, respectively. The effects of refactoring on complexity and coupling metrics of software were also analyzed in the majority of studies, i.e., 29 studies each. Interestingly, the majority of selected studies (41%) used large open source datasets written in Java language instead of proprietary software. At the end, this study provides future guidelines for conducting research in the field of code refactoring.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.543-555
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• 2019

The purpose of this study was to define and clarify the concept of Shared decision-making (SDM) in patients with chronic disease. Walker and Avant's concept analysis process was used to analyze interdisciplinary convergence in SDM. SDM in patients with chronic disease can be defined by the following attributes: acknowledgment patients as 'self-care experts', the rights of self-determination, reversible negotiation, and patient-centered care. The antecedents of SMD consisted of situations where there is a need to make a decision from several treatment options of similar efficacy, decisional conflict, patient, family, and health provider's willingness to participate in the decision-making process, enough time and opportunity for SDM. The consequences occurring as a result of SMD were decrease decisional conflict, improvement health outcome, satisfaction, quality of life, enhancement self-management and self-efficacy with long-term, and living acceptably with the illness. Based on these results, a scale measuring SDM in patients with chronic disease is needed.

• Journal of Animal Science and Technology
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• v.63 no.3
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• pp.614-625
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• 2021

This study investigates how visual appearance of pork cuts affects consumer preference. Images of pork belly, Boston butt, and loin were chosen on the basis of visible fattiness and used to analyze consumers' perception of the appearance of each pork cut. Meat color and visible fat proportion of images of pork cuts were analyzed by the researchers before conducting the survey. A total of 211 pork eaters evaluated the pork cuts based on appearance (lightness of color, redness, visible fat proportion, and fat distribution), preferability, and overall acceptability. Also, muscle pieces from different pork cuts were taken and the relative area composition of muscle fibers was measured. Based on survey results, correlation between visual traits and preferences of each pork cut was analyzed. The survey results showed that preferred pork appearance varied as per each individual's favorite pork cut. Also, the respondents evaluated visual characteristics and preference for each pork cut differently possibly due to the different visual characteristics of each cut. Correlation analysis between visual traits and preference indicated that overall acceptability of pork cuts was mainly influenced by fat preference, followed by color preference. Fat and color preferences for each pork cut were affected by various visual attributes including redness, lightness of color, visible fat proportion, and fat distribution, but their effects were considerably varied among different pork cuts. Thus, Korean consumers perceived and assessed pork appearance using various quality cues but the evaluation depended on which cut was being observed.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.