Jaekwon Kim

Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance

This paper proposes new combined methods to classify normal and epileptic seizure EEG signals using wavelet transform (WT), phase-space reconstruction (PSR), and Euclidean distance (ED) based on a neural network with weighted fuzzy membership functions (NEWFM). WT, PSR, ED, and statistical methods that include frequency distributions and variation, were implemented to extract 24 initial features to use as inputs. Of the 24 initial features, 4 minimum features with the highest accuracy were selected using a non-overlap area distribution measurement method supported by the NEWFM. These 4 minimum features were used as inputs for the NEWFM and this resulted in performance sensitivity, specificity, and accuracy of 96.33%, 100%, and 98.17%, respectively. In addition, the area under Receiver Operating Characteristic (ROC) curve was used to measure the performances of NEWFM both without and with feature selections.

Ontology driven interactive healthcare with wearable sensors

Ubiquitous healthcare is the service that offers health-related information and contents to users without any limitations of time and space. Especially, to offer customized services to users, the technology of acquiring context information of users in real time is the most important consideration. In this paper, we researched wearable sensors. We proposed the ontology driven interactive healthcare with wearable sensors (OdIH_WS) to achieve customized healthcare service. For this purpose, wearable-sensor-based smart-wear and methods of data acquisition and processing are being developed. The proposed system has potential value in healthcare. A smart wear using wearable sensors is fabricated as a way of non-tight and comfortable style fitting for the curves of the human body based on clothes to wear in daily life. The design sample of the smart wear uses basic stretch materials and is designed to sustain its wearable property. To offer related information, it establishes an environment-information-based healthcare ontology model needed for inference, and it is composed of inside-outside context information models depending on the users’ context. The modeling of the proposed system involved combinations of information streams, focusing on service context information. With the proposed service inference rules, customized information and contents could be drawn by the inference engine. In the established OdIH_WS, real-time health information monitoring was achieved. The results of system performance and users’ satisfaction evaluations confirmed that the proposed system is superior to other existing systems.

U-health Service Model for Managing Health of Chronic Patients in Multi-platform Environment

U-health services have been progressed as treatment and management for specific diseases and prevention services for providing the behavior management to customers according to the increase in chronic patients. The conventional U-health services provide required services and bio-information monitoring only through remote diagnoses and counsels and that represent limitations in preventing and managing metabolic syndrome patients like chronic patients. Thus, in this study a multi platform based U-health service model for managing the health of chronic patients is proposed. The multi-platform based U-health service model can provide continuous health information, diet, and exercise services regardless of the location of customers through PCs and smart phones. In addition, it is able to provide prescription services to doctors and nurses using a CDS (Clinical Decision Support) module based on clinical information. Doctors can identify the life pattern of patients through a behavior modification program and provide customized services to patients. The U-health service model provides effective services in multi-platform environments to customers and that will improve the health of chronic patients.

Context-aware based U-health Environment Information Service

U-health care services have been attracted to effectively solve some problems in promoting health and preparing aging society. Although the recent U-health care services have been developed to treat diseases, it requires environment information related to health for preventing fundamental diseases and for promoting health. In this study, a U-health environment service that reflects context recognition information is proposed. The proposed service draws environment information using local weather and healthcare information in users` residential areas. In the context recognition based U-health environment services, various services are provided to users not only health, living weather based menu, and exercise services but user location based warning messages for dangerous regions and remote emergency services. That is, based on such context recognition, some events that are to be occurred to users are detected and then it will provide proper services. Thus, it improves the satisfaction of U-health services and its service qualities.

Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree

Objectives The importance of the prediction of coronary heart disease (CHD) has been recognized in Korea; however, few studies have been conducted in this area. Therefore, it is necessary to develop a method for the prediction and classification of CHD in Koreans. Methods A model for CHD prediction must be designed according to rule-based guidelines. In this study, a fuzzy logic and decision tree (classification and regression tree [CART])-driven CHD prediction model was developed for Koreans. Datasets derived from the Korean National Health and Nutrition Examination Survey VI (KNHANES-VI) were utilized to generate the proposed model. Results The rules were generated using a decision tree technique, and fuzzy logic was applied to overcome problems associated with uncertainty in CHD prediction. Conclusions The accuracy and receiver operating characteristic (ROC) curve values of the propose systems were 69.51% and 0.594, proving that the proposed methods were more efficient than other models.

Smart Phone based Personalized Menu Management System for Diabetes Patient

Diabetes is a type of metabolic disease presented by high blood sugar and that leads to significantly decrease the quality of life causing various symptoms. It is essential to manage a systematic menu for preventing such diabetes even though there are some ways for it including diet, physical exercise, medicinal prescription, and so on. This study proposes a smart phone based personalized menu management system for achieving the systematic diabetes management. At the present time almost menu systems for diabetes patients are subjectively prescribed by dietitians or doctors and that does not reflect current situations and personal preferences. The system proposed in this study provides the menu for diabetes patients according to season, weather, time, and personal preferences. In particular, the recipe and personalized menu for patients can be provided without limiting any time and location based on smart phone services, and its menu can easily be changed or selected by the phone.

Fuzzy Logic-driven Virtual Machine Resource Evaluation Method for Cloud Provisioning Service

Cloud computing is one of the distributed computing environments and utilizes several computing resources. Cloud environment uses a virtual machine to process a requested job. To balance a workload and process a job rapidly, cloud environment uses a provisioning technique and assigns a task with a status of virtual machine. However, a scheduling method for cloud computing requires a definition of virtual machine availabilities, which have an obscure meaning. In this paper, we propose Fuzzy logic driven Virtual machine Provisioning scheduling using Resource Evaluation(FVPRE). FVPRE analyzes a state of every virtual machine and actualizes a value of resource availability. Thus FVPRE provides an efficient provisioning scheduling with a precise evaluation of resource availability. FVPRE shows a high throughput and utilization for job processing on cloud environments.

Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

Background Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy. Objective Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC) curve of the proposed model (0.749 ± 0.010) was larger than the Framingham risk score (FRS) (0.393 ± 0.010). Conclusions The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.Background Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy. Objective Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC) curve of the proposed model (0.749 ± 0.010) was larger than the Framingham risk score (FRS) (0.393 ± 0.010). Conclusions The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

분산 환경에서 계산 자원의 효율 증대를 위한 데이터 특성 기반의 작업 분류방법

Various computational resources in distributed environment are to build a high-performance computing environments through virtualization technology. Recently, there is a growing need for a complicated process due to the improvement of the user-level application, which has led to demand for high-performance computing. The requested job from users is composed of data. And because of each data has own characteristics, the classifier may consider the features of data. In this paper, we propose Job Classifying method based on Data Traits for Increased Efficiency of Computational Resources in Distributed Environment (JCDT). JCDT classifies the job by data traits of the users’ request, is expected to improve the job processing time and increase the processing speed of the calculation resources.

Virtual Machine Provisioning Scheduling with Conditional Probability Inference for Transport Information Service in Cloud Environment

There is a growing tendency toward a vehicle demand and a utilization of traffic information systems. Due to various kinds of traffic information systems and increasing of communication data, the traffic information service requires a very high IT infrastructure. A cloud computing environment is an essential approach for reducing a IT infrastructure cost. And the traffic information service needs a provisioning scheduling method for managing a resource. So we propose a provisioning scheduling with conditional probability inference (PSCPI) for the traffic information service on cloud environment. PSCPI uses a naive bayse inference technique based on a status of a virtual machine. And PSCPI allocates a job to the virtual machines on the basis of an availability of each virtual machine. Naive bayse based PSCPI provides a high throughput and an high availability of virtual machines for real-time traffic information services.