• Pediatric Infection and Vaccine
• /
• v.4 no.1
• /
• pp.97-105
• /
• 1997

Purpose : Aseptic meningitis mainly caused by enterovirus is common in pediatric population especially during summer & fall. Most of pediatric patients restore their health without any complications with proper management. Between May to August of 1996, Masan and surrounding areas of the Kyoungsangnamdo were epidemic areas for the aseptic meningitis. The purpose of this study was to determine causative virus and describe correlation between disease and clinical symptoms in aseptic meningitis patients and those with fever and characteristic rashes without apparent meningitis symptoms. Methods : Between May to August, 1996, 57 patients with high fever and characteristic feature of rashes were reviewed. From 22 cerebrospinal fluid & 57 stool obtained specimens, viral culture and detection of enterovirus RNA were conducted. Collected specimens were kept in $-30^{\circ}C$ environment until sending of specimens to labortory. The virus identified through indirect immunofluorescence. RT-PCR method was used to identify enterovirus RNA in cerebralspinal fluid. Results : 1) One hundred fifty five pediatric patients with viral infection required hospitalization. Disease occurred higher rate in male than female with ratio of 1.94:1. Examined patients' age ranged from 15days old to 15years old. But most of patients(74.8%) were under age of 5years old. The time of occurrence was between May to August of 1996. 2) All patients had high fever and physical symptoms in those patients include headache, vomiting, abdominal pain, diarrhea, and rashes. The rashes observed mainly in patients under age of 4 years and were predominantly commom patients under age of 18 months olds)<0.001). 3) Between sampled patients and non-sampled patients, clinical course was similar. Echovirus type 9 was cultivated in 41 out of 57 cases of collected stool specimens. RT-PCR that used on CSF showed positive results in 10 out of 22 cases. Three cases of positive cultivated of positive results in RT-PCR were echovirus type 9. Conclusions : Echovirus type 9 was thought to be the causative agent of aseptic meningitis that was prevalent throughout mid areas of Kyoungsangnamdo from May to August, 1996. Additionally causative agent that responsible for high fever with rashes without meningitis symptoms also thought to be the same echovirus type 9.

• Journal of Intelligence and Information Systems
• /
• v.16 no.3
• /
• pp.77-97
• /
• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.