• Journal of Preventive Medicine and Public Health
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• v.26 no.3 s.43
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• pp.359-370
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• 1993

To identify the smoking status and its related factors in middle and high school boys in Kwanju a study was performed from 15th to loth of June 1992. Population were selected by two-stage stratified random sampling method and total 3,959 students replied to the self-administered questionnaire survey (1,574 in middle school, 1,664 in academic high school, 712 in business high school). The results were as follows ; 1. The proportion of current smokers was 1.5% in middle schools and 20.1% in high schools. And the smoking rates increased with school grade years (p<0.01). 2. For the motivation of smoking, curiosity was the most frequent factor and the next was temptation by friends. 3. The most common situation on the first experience of smoking was that middle school boys smoked a cigarette which was found in a house, through curiosity, with friends, at home. High school boys smoked a cigarette taken from friends, through curiosity, with friend, on the road or at home. 4. The proportion of smokers who smoke a cigarette regularly was 34.8% among smokers in middle school and 70.2% among smokers in high school. The most proportion of duration of smoking was less than 1 month among middle school boys (20.8%) and more than 2 years among high school boys (43.9%). The first smoking experience was in elementary school among middle school boys and the third grade of middle school in high school students. Most current smokers (73.9% in middle school boys, 65.3% in high school boys) wanted to quit smoking. 5. Smokers had significant association with intimate friend's smoking, mother's and brother's smoking, inharmonious friendships, dissatisfied with home and school life, lower school grades, generous attitude to other smokers, lack of knowledge to passive smoking and no contact to mass media (TV) (p<0.01).

• Korean Journal of Microbiology
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• v.40 no.4
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• pp.342-347
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• 2004

A bacterial strain YC4963 with antifungal activity against Colletotrichum orbiculare, a causal organism of cucumber anthracnose was isolated from the rhizosphere soil of Siegesbeckia pubescens Makino in Korea. Based on physiological and biochemical characteristics and 16S ribosomal DNA sequence analysis, the bac­terial strain was identified as Pseudomonas aurantiaca. The bacteria also inhibited mycelial growth of several plant fungal pathogens such as Botrytis cinerea, Fusarium oxysporum and Rhizoctonia solani on PDA and 0.1 TSA media. The antifungal activity was found from the culture filtrate of this isolate and the active compound was quantitatively bound to XAD adsorption resin. The antibiotic compound was purified and identified as phenazine-l-carboxylic acid on the basis of combined spectral and chemical analyses data. This is the first report on the production of phenazine-l-carboxylic acid by Pseudomonas aurantiaca.

• Applied Microscopy
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• v.38 no.4
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• pp.279-284
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• 2008

The phase change materials have been extensively used as an optical rewritable data storage media utilizing their phase change properties. Recently, the phase change materials have been spotlighted for the application of non-volatile memory device, such as the phase change random access memory. In this work, we have investigated the crystallization behavior and microstructure analysis of In-Sb-Te (IST) thin films deposited by RF magnetron sputtering. Transmission electron microscopy measurement was carried out after the annealing at $300^{\circ}C$, $350^{\circ}C$, $400^{\circ}C$ and $450^{\circ}C$ for 5 min. It was observed that InSb phases change into $In_3SbTe_2$ phases and InTe phases as the temperature increases. It was found that the thickness of thin films was decreased and the grain size was increased by the bright field transmission electron microscopy (BF TEM) images and the selected area electron diffraction (SAED) patterns. In a high resolution transmission electron microscopy (HRTEM) study, it shows that $350^{\circ}C$-annealed InSb phases have {111} facet because the surface energy of a {111} close-packed plane is the lowest in FCC crystals. When the film was heated up to $400^{\circ}C$, $In_3SbTe_2$ grains have coherent micro-twins with {111} mirror plane, and they are healed annealing at $450^{\circ}C$. From the HRTEM, InTe phase separation was occurred in this stage. It can be found that $In_3SbTe_2$ forms in the crystallization process as composition of the film near stoichiometric composition, while InTe phase separation may take place as the composition deviates from $In_3SbTe_2$.

• Korean Journal of Microbiology
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• v.42 no.2
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• pp.89-94
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• 2006

The purpose of this study is to develop the strain-specific PCR primers for the identification of prevotella inter-media ATCC 49046 which is frequently used in the pathogenesis studies of periodontitis. The Hind III-digested genomic DNA of P. intermedia ATCC 49046 were cloned by random cloning method. The specificity of cloned DNA fragments were determined by Southern blot analysis. The nucleotide sequence of cloned DNA probes was determined by chain termination method. The PCR primers were designed based on the nucleotide sequence of cloned DNA fragment. The data showed that Pig6 DNA probe were hybridized with the genomic DNA from P. intermedia strains (ATCC $25611^T$ and 49046) isolated from the Westerns, not the strains isolated from Koreans. The Pig6 DNA probe were consisted of 813 bp. Pig6-F3 and Pig6-R3 primers, designed base on the nucleotide Sequences Of Pig6 DNA Probe, were 3150 specific to the only both P. intermedia ATCC $25611^T$ and P. intermedia ATCC 49046. In the other hand, Pig6-60F and Pig6-770R primers were specific to the only P. intermedia ATCC 49046. The two PCR primer sets could detect as little as 4 pg of chromosomal DNA of P. intermedia. These results indicate that Pig6-60F and Pig6-770R primers have proven useful for the identification of P. intermedia ATCC 49046, especially with regard to the maintenance of the strain.

• 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.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.