• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.586-594
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• 2004

Because web documents become creation and disappearance rapidly, users require the recommend system that offers users to browse the web document conveniently and correctly. One largely untapped source of knowledge about large data collections is contained in the cumulative experiences of individuals finding useful information in the collection. Recommendation systems attempt to extract such useful information by capturing and mining one or more measures of the usefulness of the data. The existing Information Filtering system has the shortcoming that it must have user's profile. And Collaborative Filtering system has the shortcoming that users have to rate each web document first and in high-quantity, low-quality environments, users may cover only a tiny percentage of documents available. And dynamic recommendation system using the user browsing pattern also provides users with unrelated web documents. This paper classifies these web documents using the similarity between the web documents under the web document type and extracts the user browsing sequential pattern DB using the users' session information based on the web server log file. When user approaches the web document, the proposed Dynamic recommendation system recommends Top N-associated web documents set that has high similarity between current web document and other web documents and recommends set that has sequential specificity using the extracted informations and users' session information.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.9-17
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• 2021

In this study, we propose a part of the CDSS(Clinical Decision Support System) study, a system that can classify chemotherapy, one of the treatment methods for colorectal cancer patients. In the treatment of colorectal cancer, the selection of chemotherapy according to the patient's condition is very important because it is directly related to the patient's survival period. Therefore, in this study, chemotherapy was classified using a machine learning algorithm by creating a baseline model, a pathological model, and a combined model using both characteristics of the patient using the individual and pathological characteristics of colorectal cancer patients. As a result of comparing the prediction accuracy with Top-n Accuracy, ROC curve, and AUC, it was found that the combined model showed the best prediction accuracy, and that the LGBM algorithm had the best performance. In this study, a chemotherapy classification model suitable for the patient's condition was constructed by classifying the model by patient characteristics using a machine learning algorithm. Based on the results of this study in future studies, it will be helpful for CDSS research by creating a better performing chemotherapy classification model.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.139-157
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• 2002

The rapid growth of e-commerce has made both companies and customers face a new situation. Whereas companies have become to be harder to survive due to more and more competitions, the opportunity for customers to choose among more and more products has increased. So, the recommender systems that recommend suitable products to the customer have an important position in E-commerce. This research introduces collaborative filtering based recommender system which helps customers find the products they would like to purchase by producing a list of top-N recommended products. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is used to select target customers, who have high possibility of purchasing recommended products. We applied the recommender system to a Korean department store. The methodology is evaluated with the analysis of a real department store case and is compared with other methodologies.

• Journal of Korean Public Health Nursing
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• v.4 no.1
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• pp.25-35
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• 1990

This study examined the evaluation of the information effects of the teaching on knowledge and daily activities of open heart surgery patients between 2 and 6 weeks after discharge. The subject was 29 patients being taught with teaching materials at discharge as experimental group, 20 patients who received no education as control group among the patients who had undergone open heart surgery in S.N.U.H. And research method was non-equivalent control group non-synchronized quasi-experimental design. As the tool of this study, 30 items of knowledge measurement scale which was extracted among the content of teaching materials to evaluate the effect of education and 28 items of which were designed to measure the daily activities of patients with myocardial infarction for the estimation of the degree of observance in daily activities were used. For data analysis, frequency, t-test, Pearson's correlation coefficient and Cronbach's $\alpha$ were used. The result were as follows; 1. Informations given through teaching materials were effective for increasing the knowledge of the patient with open heart surgery. The knowledge of patients increased to the top level (p<0.05) in 2 weeks after discharge. In control group, the knowledge level of patients did not increase after discharge. 2. The knowledge level daily activity of the experimental group was somewat higher than that of the control group, but there was no significant difference. The score .of the experimental group was 69.66 in 6 weeks after discharge much less than the top level score 112. 3. The correlation between knowledge and daily activities was not significant, suggesting the fact that the increase of knowledge did not influence the daily activities significantly. Recommendation was suggested that; 1) Further studies might be .needed with the increasing numbers of the subjects. 2) Daily activities of the patients with open heart surgery should be investigated for long term period until they recovered normal activities.

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

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.361-361
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• 2017

The NPK are known as macro elements that affect crop growth and yield. In 1989, Cambodia Agricultural Research and Development Institute (CARDI) gave a recommendation rate of fertilizer on rice production based on soil types. This recommended rate of NPK seems however relatively low as compared to farmers' practices nowadays and the amount in the neighboring countries. The CARDI recommended rate for Prateah Lang soil type is 50kg N, $25kg\;P_2O_5$, $25kg\;K_2O\;ha^{-1}$ while recent farmers' practice rates are 55 - 64kg N, 24 - 46kg $P_2O_5$, $30kg\;K_2O\;ha^{-1}$. However, the overuse of chemical fertilizer will lead to un-preferable plant growth, insect pest, disease and economic yield. Thus, we examined the effect of different NPK application rates on the growth and yield components in Prateah Lang soil type in Takeo province to investigate appropriate rates for improving rice productivity with economic efficiency. This study was conducted from July to November during wet season in 2013. A multi-locational trial with 6 treatments (T0 - T5) of NPK rates in 5 locations (trial 1 - 5) with 3 replications was conducted. The different combinations of NPK application were employed from 0, 50, 60, 80, 100, $120kg\;N\;ha^{-1}$, 0, 25, 30 45, $60kg\;P_2O_5\;ha^{-1}$ and 0, 15, 25, 30, $45kg\;K_2O\;ha^{-1}$. Urea, DAP and KCl were used for fertilization. Split application was employed [basal: 20% of N, 100% of P and K, top dressing-1st: 40% of N (30DAT), 2nd: 40% of N (PI stage)]. Three-week-old seedlings of var. Phka Rumdoul were transplanted with 2 - 3 seedlings $hill^{-1}$ with $20cm{\times}20cm$ spacing. Plant length, tiller number at the maximum tillering stage and yield components were measured. The different rates of NPK application affected some yield components. The panicle number per hill was the most important key component followed by the spikelet number per panicle. However, the other parameters such as the filled grain percentage and 1000 grains weight had small effect or weak relation with the yield. Although the panicle number per hill had a significantly positive correlation with the stem number per hill, it was not correlated with the percentage of productive culms. The variation in the grain yield among the 5 trials was small and the difference was not significant. Although the yield tended to be higher at higher N and P application, there was no significant difference above 60kg N and $30kg\;P_2O_5$. The yield was the highest at 15, 30 and $45kg\;K_2O$ followed by $25kg\;K_2O$. The relationships between N, P and the stem number per hill were significantly linear positive, though it was not linear between K and the stem number. From these results, to increase rice productivity in the target area, farmers' effort to increase N and P input rather than CARDI recommendation up to 60kg N and $30kg\;P_2O_5$ will be sufficient considering economic efficiency. Besides, the amount of K application should be reconsidered.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.363-363
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• 2017

The NPK are known as macro elements that affect crop growth and yield. In 1989, Cambodia Agricultural Research and Development Institute (CARDI) gave a recommendation rate of fertilizer on rice production based on soil types. This recommended rate of NPK seems however relatively low as compared to farmers' practices nowadays and the amount in the neighboring countries. The CARDI recommended rate for Prateah Lang soil type is 50kg N, 25kg P2O5, 25kg K2O ha-1 while recent farmers' practice rates are 55 - 64kg N, 24 - 46kg P2O5, 30kg K2O ha-1. However, the overuse of chemical fertilizer will lead to un-preferable plant growth, insect pest, disease and economic yield. Thus, we examined the effect of different NPK application rates on the growth and yield components in Prateah Lang soil type in Takeo province to investigate appropriate rates for improving rice productivity with economic efficiency. This study was conducted from July to November during wet season in 2013. A multi-locational trial with 6 treatments (T0 - T5) of NPK rates in 5 locations (trial 1 - 5) with 3 replications was conducted. The different combinations of NPK application were employed from 0, 50, 60, 80, 100, 120kg N ha-1, 0, 25, 30 45, 60kg P2O5 ha-1 and 0, 15, 25, 30, 45kg K2O ha-1. Urea, DAP and KCl were used for fertilization. Split application was employed [basal: 20% of N, 100% of P and K, top dressing-1st: 40% of N (30DAT), 2nd: 40% of N (PI stage)]. Three-week-old seedlings of var. Phka Rumdoul were transplanted with 2 - 3 seedlings hill-1 with $20cm{\times}20cm$ spacing. Plant length, tiller number at the maximum tillering stage and yield components were measured. The different rates of NPK application affected some yield components. The panicle number per hill was the most important key component followed by the spikelet number per panicle. However, the other parameters such as the filled grain percentage and 1000 grains weight had small effect or weak relation with the yield. Although the panicle number per hill had a significantly positive correlation with the stem number per hill, it was not correlated with the percentage of productive culms. The variation in the grain yield among the 5 trials was small and the difference was not significant. Although the yield tended to be higher at higher N and P application, there was no significant difference above 60kg N and 30kg P2O5. The yield was the highest at 15, 30 and 45kg K2O followed by 25kg K2O. The relationships between N, P and the stem number per hill were significantly linear positive, though it was not linear between K and the stem number. From these results, to increase rice productivity in the target area, farmers' effort to increase N and P input rather than CARDI recommendation up to 60kg N and 30kg P2O5 will be sufficient considering economic efficiency. Besides, the amount of K application should be reconsidered.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.312-318
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• 2010

This study was carried out to develop and test a prototype program that recommends the nitrogen topdressing rate using the color digital camera image taken from rice field at panicle initiation stage (PIS). This program comprises four models to estimate shoot N content (PNup) by color digital image analysis, shoot N accumulation from PIS to maturity (PHNup), yield, and protein content of rice. The models were formulated using data set from N rate experiments in 2008. PNup was found to be estimated by non-linear regression model using canopy cover and normalized green values calculated from color digital image analysis as predictor variables. PHNup could be predicted by quadratic regression model from PNup and N fertilization rate at panicle initiation stage with $R^2$ of 0.923. Yield and protein content of rice could also be predicted by quadratic regression models using PNup and PHNup as predictor variables with $R^2$ of 0.859 and 0.804, respectively. The performance of the program integrating the above models to recommend N topdressing rate at PIS was field-tested in 2009. N topdressing rate prescribed for the target protein content of 6.0% by the program were lower by about 30% compared to the fixed rate of 30% that is recommended conventionally as the split application rate of N fertilizer at PIS, while rice yield in the plots top-dressed with the prescribed N rate were not different from those of the plots top-dressed with the fixed N rates of 30% and showed a little lower or similar protein content of rice as well. And coefficients of variation in rice yield and quality parameters were reduced substantially by the prescribed N topdressing. These results indicate that the N rate recommendation using the analysis of color digital camera image is promising to be applied for precise management of N fertilization. However, for the universal and practical application the component models of the program are needed to be improved so as to be applicable to the diverse edaphic and climatic condition.

• Journal of KIISE
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• v.41 no.12
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• pp.1058-1065
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• 2014

In order to assist user's who are in the process of executing a search, a query expansion method suggests keywords that are related to an input query. Recently, several studies have suggested keywords that are identified by finding domains using a clustering method over the documents that are retrieved. However, the clustering method is not relevant when presenting various domains because the number of clusters should be fixed. This paper proposes a method that suggests keywords by finding various domains related to the input queries by using a community detection algorithm. The proposed method extracts words from the top-30 documents of those that are retrieved and builds communities according to the word graph. Then, keywords representing each community are derived, and the represented keywords are used for the query expansion method. In order to evaluate the proposed method, we compared our results to those of two baseline searches performed by the Google search engine and keyword recommendation using TF-IDF in the search results. The results of the evaluation indicate that the proposed method outperforms the baseline with respect to diversity.