• The Journal of the Korea Contents Association
• /
• v.21 no.12
• /
• pp.13-22
• /
• 2021

The clinical decision support system uses accumulated medical data to apply an AI model learned by machine learning to patient diagnosis and treatment prediction. However, the existing black box-based AI application does not provide a valid reason for the result predicted by the system, so there is a limitation in that it lacks explanation. To compensate for these problems, this paper proposes a system model that applies XAI that can be explained in the development stage of the clinical decision support system. The proposed model can supplement the limitations of the black box by additionally applying a specific XAI technology that can be explained to the existing AI model. To show the application of the proposed model, we present an example of XAI application using LIME and SHAP. Through testing, it is possible to explain how data affects the prediction results of the model from various perspectives. The proposed model has the advantage of increasing the user's trust by presenting a specific reason to the user. In addition, it is expected that the active use of XAI will overcome the limitations of the existing clinical decision support system and enable better diagnosis and decision support.

• International Journal of Contents
• /
• v.4 no.4
• /
• pp.18-23
• /
• 2008

With the development of information communication and network technologies, ubiquitous era that supports various services regardless of places and time has been advancing. The development of such technologies has a great influence on educational environments. As a result, e-learning concepts that learners use learning contents in anywhere and anytime have been proposed. The various learning contents authoring systems that consider the e-learning environments have also been developed. However, since most of the existing authoring systems support only PC environments, they are not suitable for various ubiquitous mobile devices. In this paper, we design and implement a contents authoring system based on learning activities for u-learning environments. Our authoring system significantly improves the efficiency for authoring contents and supports various ubiquitous devices as well as PCs.

• Korean Journal of Artificial Intelligence
• /
• v.11 no.2
• /
• pp.19-27
• /
• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• Proceedings of the Korean Information Science Society Conference
• /
• 1998.10c
• /
• pp.75-77
• /
• 1998

Perhaps one of the most versatile approaches to learning in practical domains lies in case based reasoning. To date, however, most case based reasoning systems have tended to focus on relatively simple domains. The current study involves the development of a decision support system for a complex production process with a limited database. This paper presents a set of critical issues underlying CBR, then explores their consequences for a complex domain. Finally, the performance of the system is examined for resolving various types of quality control problems.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.7
• /
• pp.17-24
• /
• 2009

Increasing use of information system has led to larger amount of developing expenses and demands on software. Until recent days, the model using regression analysis based on statistical algorithm has been used. However, Machine learning is more investigated now. This paper estimates the software cost using SVR(Support Vector Regression). a sort of machine learning technique. Also, it finds the best set of parameters applying immune algorithm. In this paper, software cost estimation is performed by SVR based on immune algorithm while changing populations, memory cells, and number of allele. Finally, this paper analyzes and compares the result with existing other machine learning methods.

• The Journal of Information Systems
• /
• v.11 no.1
• /
• pp.51-74
• /
• 2002

The purpose of this study is to empirically investigate critical success factors for effective implementation of web-based distance learning system. First of all, four critical success factors are theoretically derived from reviewing previous research. They are: (1) learner-related factor including the variables such as teaming ability, learning attitude, and attending motivation, (2) environmental factor including the variables of physical and mental support for learners, (3) instructional design factor represented by one variable, the degree of appropriateness of learning contents, and (4) the factor concerning the level of self-directed learning readiness embracing the variables such as curiosity for learning, openness towards challenge of learning and affection for learning. Subsequently, the relationships between these four critical success factors and the degree of learning satisfaction are empirically investigated. The data for empirical analysis of the research are collected from 1,020 respondents who have already passed the web-based distance learning courses which have been implemented in Information and Communication Officials Training Institute. Out of 1,020 responded questionnaires, 875 data were available for statistical analyses. The main results of this study are as follows. Firstly, the most important factor for successful implementation of the web-based distance learning system is shown to be the instructional design factor, and in the next place, the self-directed learning readiness factor, the environmental factor and the learner-related one in sequence. Secondly, additional analysis of the variables included in the instructional design factor shows that availability of practical information and knowledge is the most influencing variable, and next, interesting composition of contents, reasonable learning amount, optimal level of instruction, and understandable explanation are significantly important in the descending order. Lastly, among learning motivators, strong intention of acquiring business knowledges and skills is found to be the most important satisfier in the web-based distance learning. The theoretical contribution of this study is to derive a comprehensive model of critical success factors for implementing the web-based distance learning system. And, the practical implication of this study is to propose efficient and effective guidelines for developing and operating the web-based distance learning system in the various kinds of organizations.

• Journal of vocational education research
• /
• v.35 no.6
• /
• pp.65-84
• /
• 2016

This study aims to explore the duty system of the lifelong learning counseling, and to analyze the needs of counseling practice which are conceived by lifelong education practitioners. Based on the related prior studies, the duty system of lifelong learning counseling was investigated and classified. Also, differences of how to recognize the importance of counseling job and how to practice counseling are assessed by Borich method. After data were collected by practitioners from lifelong education field, the dependent t-test and the Borich needs assessment formula were used for analysis of the collected data. The results are as follows: the 4 subdivided duties of lifelong learning counseling are formation of relationship, learner's analysis, learning promotion, and follow-up management. The 11 tasks are learner's interview, providing learning information, analysis of learner's characteristics and needs, learning level diagnosis, diagnosis of learning inhibiting factors, promotion of learning motivation, advice of learning course and learning method, support of study circle activity, career planning counseling, follow-up counseling, and counseling evaluation. According to the needs assessment, learner's analysis is conceived as the most important duty among the 4 sub-duties, and learner's analysis is regarded as second important duty by the counseling practitioners. Among the 11 tasks, providing learning information is the most important tasks among counseling practitioners, and analysis of learner's characteristics and needs is followed as second task. The duty system of the lifelong learning counseling and needs assessment data can be used as the basic data for lifelong education practitioners to conduct the duty of lifelong learning counseling efficiently and to support the lifelong learning plan according to learner's characteristics.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.7
• /
• pp.249-254
• /
• 2019

The concept of 'Edu-Tech', which is rapidly reorganized around e-Learning, has been spreading along with the development of intelligent information technology according to the fourth industrial revolution such as Artificial Intelligence (AI), Internet of Things (IoT), BigData. Currently, leading companies are conducting online education services, but real-time two-way communication is difficult. In addition, in the case of off-line class, there are many students, and not only the time is limited, but also they often miss the opportunities to ask questions. In order to solve these problems, this paper develops a real - time interactive question and answer management system that can freely questions both on - line and off - line by combining the benefits of offline instant answers and the advantages of online openness. The developed system is a real-time personalized education system that enables the respondent to check the situation of the questioner in real time and provide a customized answer according to the inquirer's request. In addition, by measuring and managing the system usage time in seconds, the questioner and the respondent can efficiently utilize the system.

• Journal of Advanced Navigation Technology
• /
• v.13 no.2
• /
• pp.296-301
• /
• 2009

In the self-directed learning, learner can construct learning course. But it is very difficult for learner to construct learning course with understanding the various learning contents's characteristics. This research proposed the method to support to learner the information of learning contents type to fit the learner as calculate the learner's learning preference when learner construct the learning course. The calculating method of learning preference used preference vector value of topic map. To apply this method, we tested 20 learning sampling group and presented that this method help to learner to construct learning course as getting the high average degree of learning satisfaction.

• The Journal of Korean Institute of Information Technology
• /
• v.17 no.5
• /
• pp.47-55
• /
• 2019

As the damage caused by GPS jamming has been increased, researches for detecting and preventing GPS jamming is being actively studied. This paper deals with a GPS jamming detection method using multiple GPS receiving channels and three-types machine learning techniques. Proposed multiple GPS channels consist of commercial GPS receiver with no anti-jamming function, receiver with just anti-noise jamming function and receiver with anti-noise and anti-spoofing jamming function. This system enables user to identify the characteristics of the jamming signals by comparing the coordinates received at each receiver. In this paper, The five types of jamming signals with different signal characteristics were entered to the system and three kinds of machine learning methods(AB: Adaptive Boosting, SVM: Support Vector Machine, DT: Decision Tree) were applied to perform jamming detection test. The results showed that the DT technique has the best performance with a detection rate of 96.9% when the single machine learning technique was applied. And it is confirmed that DT technique is more effective for GPS jamming detection than the binary classifier techniques because it has low ambiguity and simple hardware. It was also confirmed that SVM could be used only if additional solutions to ambiguity problem are applied.