• 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.

• Korean Journal of Biological Psychiatry
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• v.9 no.1
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• pp.50-61
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• 2002

Objectives:High nailfold plexus visibility can reflect central nervous system defects as an etiologic factor of schizophrenia indirectly. Previous studies suggest that this visibility is particularly related to the negative symptoms of schizophrenia and frontal lobe deficiency. In this study, we examined the relationships between nailfold plexus visibility, and various clinical variables and neuropsychological functions in schizo-phrenic patients. Methods:Forty patients(21males, 19 females) satisfying the DSM-IV criteria for schizophrenia and thirty eight normal controls(20 males, 18 females) were measured for Plexus Visualization Score(PVS) by using the capillary microscopic examination. For the assessment of psychopathology, process-reactivity, premorbid adjustment, and neuropsychological functions, we used Positive and Negative Syndrome Scale(PANSS), Ullmann-Giovannoni Process-Reactive Questionnaire(PRQ), Phillips Premorbid Adjustment Scale(PAS), Korean Wechsler Adult Intelligence Scale(KWIS), Continuous Performance Test(CPT), Wisconsin Card Sort Test (WCST), and Word Fluency Test. We also collected data about clinical variables. Results:PVS was correlated with PANSS positive symptom score and composite score negatively. There were no correlations between PVS and PRQ score, PAS score and neuropsychological variables respectively. Conclusions:This study showed that nailfold plexus visibility was a characteristic feature in some schizophrenic patients, and that higher plexus visibility was associated with the negative symptoms of schizophrenia. There was no association between plexus visibility and neuropsychological functions.

• Clinical and Experimental Pediatrics
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• v.51 no.3
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• pp.315-322
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• 2008

Purpose : Prader-Willi syndrome (PWS) is a complex genetic disorder, caused by the deletion of the paternally derived 15q11-13 region or the maternal uniparental disomy of chromosome 15 (mUPD(15)). In this study, we compared phenotypic differences between those patients whose disease was caused by microdeletion and those caused by mUPD(15). In addition, a comparison of the efficacy of growth hormone (GH) therapy between these two PWS genotypes was analyzed. Methods : Fifty-three patients were diagnosed as having PWS based on molecular and cytogenetic analyses and clinical features. Data that included maternal age, birth weight, a feeding problem in the neonatal period, cryptorchidism, developmental delay or mental retardation, short stature, hypopigmentation, changes in height, weight, and body mass indexes (BMI) before and after GH treatment were obtained by a retrospective review of medical records. The data from the patients with microdeletion were compared with those from the patients with mUPD(15). Results : Of the 53 patients with genetically confirmed PWS, 39 cases had microdeletion and 14 mUPD(15). Maternal ages were significantly higher in the mUPD(15) group, and hypopigmentation and a feeding problem in the neonatal period were more frequent in the microdeletion group. Growth hormone was administered to 20 patients [14 with microdeletion, 6 with mUPD(15)]. There were no differences between the two groups in height velocity, weight and height SDS, and BMI after GH therapy. Conclusion : Phenotype and genotype correlations were observed in Korean PWS patients, such as more advanced maternal ages in the mUPD(15) group and more feeding problems and hypopigmentations in the microdeletion group. Further long-term prospective studies are needed to correlate other aspects of the phenotypes.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.