Hyogun Yoon

A multi-agent based user context Bayesian neural network analysis system

The increasing user mobility demands placed upon IT services necessitates an environment that enables users to access optimal services at any time and in any place. This study presents research conducted to develop a system that is capable of analyzing user IT service patterns and tendencies and provides the necessary service resources by sharing each user’s context information. First, each user’s context information is gathered to provide the multi-agent software training data necessary to describe user operations in a hybrid peer-to-peer (P2P) structured communication environment. Next, the data collected about each user’s mobile device is analyzed through a Bayesian based neural network system to identify the user’s tendency and extract essential service information. This information provides a communication configuration allowing the user access to the best communication service between the user’s mobile device and the local server at any time and in any place, thereby enhancing the ubiquitous computing environment.

An Intelligent Type 2 Diabetic Patient Management System Using Evolutionary Computation and FCM Algorithm

The present study proposes a management system combining fuzzy c-means (FCM) and evolutionary computation in order to provide an optimal treatment method based on the patients context information in ubiquitous environment. Because FCM has the shortcoming of falling easily into a local solution, we adjusted the initial values sensitively through evolutionary computation. In fitness evaluation, we used Bayesian validation so that superior solutions are selected, and in performance evaluation, experiment and evaluation were made with type 2 diabetic patients.