• The KIPS Transactions:PartB
• /
• v.15B no.2
• /
• pp.137-146
• /
• 2008

Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.3
• /
• pp.515-521
• /
• 2013

Healthcare approach in Western medicine and Korean Traditional Medicine (KTM) varies from its nature of human understanding and cultural differences. This fundamental difference in their approach of the human pathology has dualised and hindered common medical communication between the two fields of medicines. Within this current difficulty, the integrative medical service is said to become a novel method to provide the patients with the best medical care as their intent is to adapt and combine the advantages stated from the two different fields. This research paper shows the integrative approach of treating jaundice, where the symptoms of dampness and heat on Korean traditional standards are analyzed using statistical methods based on monitoring the blood test results. Therefore, we can explore an approach to diagnose and treat with comprehensive and integrative medicine algorithm.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.6
• /
• pp.1249-1258
• /
• 2015

DGAT1(diacylglycerol O-acyltransferase1) gene is well known as a major gene of milk production in dairy cattle. This study was conducted to investigate how the DGAT1 gene effect on milk yield was appeared from the genome wide association (GWA) using high density whole genome SNP chip. The data set used in this study consisted of 353 Korean Holstein sires with 50k SNP genotypes and deregressed estimated breeding values of milk yield. After quality control 41,051 SNPs were selected and locations on chromosome were mapped using UMD 3.1. Bayesian regression of BayesB method (pi=0.99) was used to estimate the SNP effects and genomic breeding values. Percentages of variance explained by 1 Mb non-overlapping windows were calculated to detect the QTL region. As the result of this study, top 1 and 3 of 2,516 windows were seen around DGAT1 gene region and 0.51% and 0.48% of genetic variance were explained by these two windows. Although SNPs on the DGAT1 gene region are excluded in commercial 50k SNP chip, the effect of DGAT1 gene seem to be reflected on GWA by the SNPs which are in linkage disequilibrium with DGAT1 gene.

• Journal of Chest Surgery
• /
• v.22 no.3
• /
• pp.425-435
• /
• 1989

The solitary pulmonary nodule is considered as a round or ovoid lesion with sharp, circumscribed borders, surrounded by normal appearing lung parenchyme on all sides, and found on a simple chest X-ray without any particular symptoms or signs. There is a wide spectrum of pathologic conditions in the solitary pulmonary nodules prove to be malignant tumors, either primary or metastatic. Most Benign granulomas and other benign conditions can also be seen as solitary nodules. The resection of solitary malignant nodules results in a surprisingly high 5-year survival rate. On the contrary, most benign nodules do not need to be resected and a period of prolonged observation and nonsurgical management is usually indicated. Therefore, the best approach to the controversial management of solitary pulmonary nodules depends on finding factors affecting the probability of malignancy. In this article, clinical records and chest roentgenographies of 60 patients operated on over the past 8 years at the Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital were reviewed. There were 15 malignant nodules and 45 benign nodules and the prevalence of malignancy was 25%. The most common pathologic entity was tuberculoma [21 cases]. The mean age was 55.5*9.6 years in the malignant group, 45.8>12.5 years in the benign group and there was a significant statistical difference between the two groups [P < 0.05]. The malignant ratio in each age group increased with advancing age. The average smoking amount was 35.6*12.9 cigarettes per day in malignant smokers, 20.9* 12.0 cigarettes per day in benign smokers, and there was a significant statistical difference between the two groups [p< 0.05]. The malignant ratio also increased with the increasing smoking amount. Comparing the appearance of the nodule on chest films, 6 calcifications and 7 cavitations were found only in benign nodules, not in malignant nodules. Therefore, calcification and cavitation can be considered as preferential findings for benignity. Previous cancer history was also a significant factor deciding the prognosis of the nodule [p< 0.05]. The average diameter on chest X-ray was 3.07*0.82 cm in malignant nodules, 3.25*1.04 cm in benign nodules and there was no significant statistical difference between the two groups [p< 0.05]. The author used Bayes theorem to develop a simple method for combining individual clinical or radiological factors of patients with solitary nodules into an overall estimate of the probability that the nodule is malignant. In conclusion, patient age, smoking amount, appearance of nodule on chest film such as calcification and cavitation, and previous cancer history were found to be strongly associated with malignancy, but size of nodule was not associated with malignancy. Since these prognostic factors have been found retrospectively, prospective controlled studies are needed to determine whether these factors have really prognostic significance.

• Journal of Intelligence and Information Systems
• /
• v.22 no.4
• /
• pp.177-192
• /
• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.