• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Korean journal of food and cookery science
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• v.15 no.3
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• pp.264-271
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• 1999

This study was conducted to know the storage stability of Yackwa, a traditional Korean fried cookie, prepared by two different cooking methods; deep fat frying at 160$^{\circ}C$ for 8 min (DFF), and preheating Yackwa dough and then deep fat frying at 180$^{\circ}C$ for 2 min (MW/DFF). Soybean oil (SBO), hydrogenated soybean oil (HSBO) or hydrogenated palm oil (HPO) were used for frying Yackwa. Compared to Yackwa prepared by DFF, all MW/DFF Yackwa samples had low fat content and high moisture content. MW/DFF saved frying time 6 min compared with DFF. Non-hydrogenated soybean oil for frying fats was replaced with hydrogenated types of soybean oil and palm oil to improve the storage stability of Yackwa. To investigate the oxidation stability of Yackwa during the accelerated storage for 15 days at 60$^{\circ}C$, acid value, anisidine value, peroxide value and oxidation value of Yackwas were measured. Acid values of Yackwa made by MW/DFF were higher than those made by DFF through the whole storage periods, regardless of frying fats. Peroxide and anisidine values of Yackwa coated with syrup were much lower than those without syrup. MW/DFF cooking method, using hydrogenated soybean oil or hydrogenated palm oil for frying, showed lowering effects on peroxide value as well as anisidine value, resulting in improved oxidation stability of Yackwa during the storage. In sensory evaluation, the acceptability of MW/DFF Yackwa was higher than those of the commerical products. Yackwa prepared by MW/DFF cooking method using hydrogenated palm oil, showed the highest acceptability in color, taste and texture among the samples.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.455-464
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• 2018

This study investigated whether the whitening effects of fullerene serum occurred through improvement of melanin production and skin tone and evaluated its potential for use as a cosmetic material for adult women aged 30-55 with dull skin color and pigmentation. Subjects were divided into a control group with fullerene-less serum (B) and an experimental group with serum containing 0.1% fullerene (A) and evaluated over 8 weeks. Visual assessment and measurement of skin conditions (melanin index, skin brightness) were conducted before use of and 4 and 8 weeks after use of the product, and a questionnaire survey and safety assessment were conducted at the end of the experiment. The results revealed that the highest decrease was 0.37% in the experimental group (A) after eight weeks of use of the product, but that this difference was not statistically significant. Moreover, no significant differences were observed upon inter-group comparison. Evaluation of changes in the melanin index and inter-group comparison revealed a significant decline in the experimental group (A) at all time points. Additionally, the $L^*$ value (skin brightness) of the experimental group (A) showed a significant increase (improvement) after eight weeks, while in inter-group comparison revealed a significant increase (improvement) at all time points (4 and 8 weeks later) (p<0.05). Based on a questionnaire survey of efficacy, positive response rates such as improvement of the target parts on week 8 were high in terms of 'improvement of skin color.' Moreover, the safety assessment revealed no adverse reactions. In conclusion, serum containing 0.1% fullerene revealed improved melanin production and skin tone (brightness) and confirmed its potential for use as a cosmetic material with brightening efficacy. Taken together, the results of this study indicate systematic investigation of the effective application and cosmetics formulations of this product are warranted.

• Journal of Animal Science and Technology
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• v.46 no.4
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• pp.613-624
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• 2004

This study was aimed at investigating the effect of fat supplementation with divalent ions such as MgO and $CaCl_2$ on 1) in vitro ruminal fermentation characteristics and insoluble fatty acid formation, and on 2) animal performance in finishing Hanwoo bulls. In in vitro trial, five different types of diets based on supplementation sources of fat and divalent ions, i.e. T=basal diet+4% tallow, T-Ca=T+0.5% $CaCl_2$, T-Mg=TA+0.5% MgO, T-MgCa = T +0.5% $CaCl_2$+0.5% MgO, T-caS =4% Ca salt tallow, were tested. Higher pH values were observed at 6 hr incubation(P<0.01) while higher amount of VFA were produced in diets 4 and 5 at 12 hr incubation(P<0.05). Nutrients(DM, OM, Crude protein and NDF) degradation tended to increase in divalent ions or Ca-salts treated tallow treatments compared with tallow treatment after 12 h. The amount of insoluble fatty acid increased by adding MgO or $CaCl_2$ to tallow or Ca soap tallow during incubation(P<0.05). In in vivo trial, thirty finishing Hanwoo(average BW 460kg) were divided into three groups based on fat sources and divalent ions, i.e. Control(EE 2.40), T-MgCa = control + tallow + $CaCl_2$ + MgO, T-CaS = control + Ca soap tallow (EE 5.30%). After feeding each diet for 80 days, average daily weight gain showed 0.89, 1.02, 1.17kg in diets 1, 2 and 3, respectively. The highest feed efficiency was observed(0.12) in diet 2 group, followed by diet 3 (0.10) and 1 groups(0.08; P < 0.05). In conclusion, the present results could be sununarized that the performance of Hanwoo bulls was improved by tallow with divalent ions without any negative effect on rumen fermentation.

• Journal of Chest Surgery
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• v.40 no.6 s.275
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• pp.414-419
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• 2007

Background: Replacing the ascending aorta is a standard surgical option for treating acute type A aortic dissection. But replacing the aortic arch has recently been reported as an acceptable procedure for this disease. We compared the effects of aortic arch replacement for treating acute type A aortic dissection with the effects of ascending aortic replacement. Material and Method: From 2002 to 2006, 25 patients undewent surgical treatment for acute type A aortic dissection, 12 patients undewent ascending aortic replacement and 13 patients underwent aortic arch replacement. Among the aortic arch group, an additional distal stent-graft was inserted during the operation in 5 patients. 19 patients (11 arch replaced patients and 8 ascending aortic replaced patients) were followed up at the out patient clinic for an average of $756{\pm}373$ days. All the patients undewent CT scanning and we analyzed their distal aortic segments. Result: 4 patients who underwent ascending aortic replacement died, so the overall mortality rate was 16%. Among the 11 long term followed-up arch replacement patients, 2 patients (18.1 %) developed distal aortic dilatation and one of them underwent thoracoabdominal aortic replacement later on. However, among the 8 the ascending aortic replaced patients, 5 patients (62.5%) developed distal aortic dilatation. Conclusion: Aortic arch replacement is one of the safe options for treating acute type A aortic dissection. Aortic arch replacement for treating acute type A aortic dissection could contribute to a reduced distal aortic dilatation rate and fewer secondary aortic procedures.

• Journal of Korean Neurosurgical Society
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• v.30 no.sup2
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• pp.189-196
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• 2001

Objective : We tested the hypothesis that photodynamic therapy(PDT) with Photofrin inhibits tumor invasion of U87 human glioma cells using several in vitro assay to measure tumor invasiveness. The effects of PDT on cell growth, directional migration and cell invasion were investigated. Material and Method : Tumor cells were treated with Photofrin at various doses and at a fixed optical(632nm) dose of $100mJ/cm^2$. Cytotoxicity was tested using the MTT method. Invasion assays including the matrigelartificial basement membrane barrier migration and spheroid confrontation with confocal microscopic analysis were used to study the relationship between PDT and invasiveness. Result : U87 cells showed a dose dependent cytotoxic response to increasing Photofrin dose. Data from the matrigel artificial basement membrane assay indicate that PDT inhibits the U87 cell migration dose dependently. Low doses of subcytotoxic PDT treatment, such as 2.5ug/ml Photofrin dose, also appeared to significantly inhibit migration of U87 cells(p<0.05). In co-cultures between U87 cell spheroids and brain aggregates, progressive invasion with destruction of the brain aggregate occurs. The extent of tumor cell infiltration and proportion or intact brain aggregate remaining after 24h differs in Photofrin PDT treated versus Photofrin only control, with changes suggestive of a dose-response effect. Conclusion : our data indicate that PDT with Photofrin significantly inhibits the invasiveness of U87 cells, and this inhibition is dose dependent.

• Clean Technology
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• v.25 no.2
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• pp.107-113
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• 2019

Numerical model that considered the shrinking core model and elutriation and degradation of particles was developed to predict selective chlorination of ilmenite and carbo-chlorination of $TiO_2$ in a two stage fluidized bed chlorination furnace. It is possible to analyze the fluidized bed chlorination reaction to be able to reflect particle distribution for mass balances and the chlorination reaction. The numerical model showed an accuracy with error less than 6% compared with fluidized bed experiments. The chlorination degree with particle size change was greater with a smaller particle size, and there was a 100 min difference to obtain a chlorination degree of 1 between $75{\mu}m$ and $275{\mu}m$. This was not shown to such a great extent with variation of temperature ($800{\sim}1000^{\circ}C$), and there was only a 10 min difference to obtain a chlorination degree of 0.9. In the first selective chlorination process, the mass reduction rate approached to the theoretical value of 0.4735 after 180 min, and chlorination changed the Fe component into $FeCl_2$ or $FeCl_3$ and showed nearly 1. In the second carbo-chlorination process, the chlorination degree of $TiO_2$ approached 0.98 and the mass fraction reached 0.02 with conversion into $TiCl_4$. In the first selective chlorination process, 98% of $TiO_2$ was produced at 180 min, and this was changed into 99% of $TiCl_4$ after an additional 90 min. Also the mass reduction rate of $TiO_2$ was reduced to 99% in the second continuous carbo-chlorination process.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of Preventive Medicine and Public Health
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• v.3 no.1
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• pp.1-16
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• 1970

The present status of control measures for public health important helminthic infections in Korea was surveyed in 1969 and the following results were obtained. The activities of parasitic examination and Ascaris treatment for the positives which were done during 1966 to 1969 were brought in poor result and could not decrease the infection rate. It is needed to improve or strengthen the activities. The mass treatment activities for paragonimiasis and clonorchiasis in the areas which were designated by the Ministry of Health were carried out during 1965 to 1968 with no good results in decrease of estimated number of the patients. There were too many pharmaceutical companies where many kinds of anthelmintics were produced. It may be better to reduce the number of anthelmintics produced and control the quality. The human feces, the most important source of helminthic infections, was generally not treated in sanitary ways because of the poor sewerage system and no sewage treatment plant in urban areas and insanitary latrines in rural areas. The field soils of 170 specimens were collected from 34 areas out of 55 urban and tourist areas where night soil has been prohibited by a regulation to be used as a fertilizer, and examined for parasites contamination with the result of Ascaris egg detection in 44%. Some kinds of vegetables of 64 specimens each from the supply agents of parasite free vegetables and general markets were collected and examined for parasites contamination with the results of Ascaris egg detection in 25% and 36% respectively. The parasite control activities and the ability of parasitological examination techniques in the health centers of the country were not satisfactory. The budget of the Ministry of Health for the parasite control was very poor. The actual expenditure needed for cellophane thick smear technique was 8 Won per a specimen. As a principle the control of helminthic infections might be led toward breaking the chain of events in the life cycle of the prasites and eliminating environmental and host factors concerned with the infections, and the following methods nay be pointed out. 1) Mass treatment might be done to eliminate human reservoirs of an infection. 2) Animal reservoirs which are related with human infections night be eliminated. 3) The excretes of reservoirs, particularly human feces, should be treated in sanitary ways by the means of sanitary sewerage system and sewage treatment plant in urban areas and sanitary latrines such as waterborne latrine, aqua privy and pit latrine in rural areas. The increase of national economical development and prohibition of the habit of using night soils as a fertilizer might be very important factors to achieve the purpose. 4) The control of vehicles and intermediate hosts might be done by the means of prohibition of soil contamination with parasites, food sanitation, insect control and snail control. 5) The improvement of insanitary attitudes and bad habits which are related with parasitic infections night be done by the means of prohibition of habit of using night soils as a fertilizer, and improving eating habits and personal hygiene. 6) Chemoprophylactic measure and vaccination may be effective to prevent the infections or the development of a parasite to adult in the bodies when the bodies were invaded by parasites. Further studies and development of this kind of measures are needed.