Clinical characteristics to predict the development of coronary artery abnormalities (CAA) in Kawasaki disease (KD) were assessed by reviewing medical records of patients diagnosed with KD at Korea University Medical Center from March 2001 to February 2005. Of the 285 patients diagnosed with KD, 19 developed CAA (6.7%). Compared with the CAA(-) group, the CAA(+) group had a longer duration of fever after intravenous gamma-globulin (IVGG) injection (2.4+/-2.9 vs. 1.5+/-1.2 days, p=0.008) and higher C-reactive protein (CRP)(12.3+/-7.8 vs. 8.7+/-7.1 mg/dL, p=0.038). In particular, the CAA(+) group tended to have more than 7 days of fever before IVGG and more than 3 days of fever after IVGG (26.3 vs. 5.3%, p<0.001; 26.3 vs. 6.4%, p=0.002). When the IVGG responsiveness was defined by the presence of defervescence within 3 days after IVGG, IVGG-non-responders showed a higher incidence of CAA (22.7 vs. 5.3%, p=0.002). Non-responders had a longer duration of fever after IVGG (5.5+/-1.9 vs. 1.2+/-0.6 days, p<0.001) and a significantly increased CRP, AST, ALT and total bilirubin. Multivariate regression analysis for CAA showed that the only factor significantly associated with the development of CAA was total fever that lasted for longer than 8 days (OR=4.052, 95% CI=1.151-14.263, p=0.0293). Conclusively, the most important predictor of CAA in KD is total duration of fever longer than 8 days. Early identification of IVGG non-responders and active therapeutic intervention for fever in KD cases might decrease the incidence of CAA.

The performance evaluation results showed that the optimal EEG electrode locations were F7, F8, FC5, and FC6, whereas the accuracy of learning and test data of user-intention recognition was found to be 94.2% and 92.3%, respectively, which suggests that the proposed system can be used to recognize user intention for specific behavior. The system proposed in the present study can allow continued rehabilitation exercise in everyday living according to user intentions, which is expected to help improve the user?s willingness to participate in rehabilitation and his or her quality of life.

Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.

To process continuous sensor data in Internet of Things (IoT) environments, this study optimizes queries using multiple MJoin operators. To achieve efficient storage management, it classifies and reduces data using a support vector machine (SVM) classification algorithm. A global shared query execution technique was used to optimize multiple MJoin queries. By comparing each kernel function of the SVM classification algorithm, the system's performance was evaluated through experiments according to the selected optimal kernel function and changes in sliding window size. Furthermore, to implement a smart home system that can actively respond to users, classified and reduced sensor data were utilized to enable the intelligent control of devices inside the home. The sensor data (e.g., temperature, humidity, gas) used to recognize the current conditions of an IoT-based smart home system and corresponding date data were classified into decision trees, and the system was designed using five sensors to intelligently control priorities such as ventilation, temperature, and fire and intrusion detection. The experiments demonstrated that the multiple MJoin technique yields high improvements in performance with relatively few searches. In this study, the sigmoid was selected as the optimal kernel function for the SVM classification algorithm. According to the SVM classification algorithm results, based on changes in the sliding window size, the average error rate was 2.42%, the reduction result was 17.58%, and the classification accuracy was 85.94%. According to the comparison of the classification performance of SVM and other algorithms, the SVM classification algorithm exhibited a minimum 9% better classification performance. Thus, compared to existing home systems, this algorithm is expected to increase system efficiency and convenience by enabling the configuration of a more intelligent environment according to the user's characteristics or requirements.INDEX TERMS Application, Internet of Things (IoT), sensor data, smart home system, SVM algorithm.

All persons in self-driving vehicle would like to receive each service. To do it, the system has to know the person’s state from emotion or stress, and to know the person’s state, it has to catch by analyzing the person’s bio-information. In this paper, we propose a system for inferring emotion using EEG, pulse, blood pressure (systolic and diastolic blood pressure) of user, and recommending color and music according to emotional state of user for a user service in self-driving vehicle. The proposed system is designed to classify the four emotional information (stability, relaxation, tension, and excitement) by using EEG data to infer and classify emotional state according to user’s stress. SVM algorithm was used to classify bio information according to stress index using brain wave data of the fuzzy control system, pulse, and blood pressure data. When 80% of data were learned according to the ratio of training data by using the SVM algorithm to classify the EEG, blood pressure, and pulse rate databased on the biometric emotion information, the highest performance of 86.1% was shown. The bio-information classification system based on the stress index proposed in this paper will help to study the interaction between human and computer (HCI) in the 4th Industrial Revolution by classifying emotional color and emotional sound according to the emotion of the user it is expected.