• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.1
• /
• pp.51-58
• /
• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.3
• /
• pp.337-347
• /
• 2020

In CRYPTO 2019, Gohr presents that Deep-learning can be used for cryptanalysis. In this paper, we verify whether Deep-learning can identify the structures of S-box. To this end, we conducted two experiments. First, we use DDT and LAT of S-boxes as the learning data, whose structure is one of mainly used S-box structures including Feistel, MISTY, SPN, and multiplicative inverse. Surprisingly, our Deep-learning algorithms can identify not only the structures but also the number of used rounds. The second application verifies the pseudo-randomness of and structures by increasing the nuber of rounds in each structure. Our Deep-learning algorithms outperform the theoretical distinguisher in terms of the number of rounds. In general, the design rationale of ciphers used for high level of confidentiality, such as for military purposes, tends to be concealed in order to interfere cryptanalysis. The methods presented in this paper show that Deep-learning can be utilized as a tool for analyzing such undisclosed design rationale.

• Journal of KIISE:Information Networking
• /
• v.33 no.1
• /
• pp.103-112
• /
• 2006

Priority-based network service has been widely adopted for the Internet traffic management in the context of IETF differentiated services, and computing optimal prices for such priority-based service is the key topic in many pricing literature. While the equilibrium analysis has been commonly used to this end, many have criticized the validity of the underlying assumption of equilibrium analysis that user utility functions are precisely known. In this paper, we propose a solution for bridging the gap between the existing theoretical work on optimal pricing and the unavailability of precise user utility information in real networks. In the proposed method, the service provider obtains more and more accurate estimates of user utility functions from the initial imprecise knowledge by iteratively changing the price of service levels and observing the users' decisions under the changed price. Our contribution is two-fold. First, we have developed a general principle for estimating the user utility functions. Second, we have developed a novel method for setting the prices that can optimize the extraction of the knowledge about user utility functions. The extensive simulation results demonstrate the effectiveness of our method.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.2
• /
• pp.227-234
• /
• 2023

In this paper, we developed a design thinking-based artificial intelligence education program for middle school students and applied it to verify the impact on creative problem-solving skills. The inspection tool used the Creative Problem Solving Profile Inventory (CPSPI), an inspection tool for measuring creative thinking type ability based on the CPS theory of Hwasun Lee, Jungmin Pyo, Insoo Choe(2014). CPSPI included the steps of evaluating cognitive preferences and cognitive abilities by supplementing the limitations of existing tests, and sharing and persuading one's ideas with others. Before and after applying the design thinking-based artificial intelligence education program, as a result of analyzing the creative problem-solving ability, it increased significantly in all areas. As a result of analyzing the creative problem-solving ability of middle school students, significant results were found in the areas of Problem Detection and Analysis, Idea Generation, Action plan, Execution, Persuasion and Communication. The effect of design thinking was confirmed as a teaching and learning method to improve creative problem-solving ability in artificial intelligence education.

• Journal of Intelligence and Information Systems
• /
• v.29 no.1
• /
• pp.41-63
• /
• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• Journal of Internet Computing and Services
• /
• v.13 no.5
• /
• pp.21-31
• /
• 2012

Threat Evaluation(TE) which has air intelligence attained by identifying friend or foe evaluates the target's threat degree, so it provides information to Weapon Assignment(WA) step. Most of TE data are passed by sensor measured values, but existing techniques(fuzzy, bayesian network, and so on) have many weaknesses that erroneous linkages and missing data may fall into confusion in decision making. Therefore we need to efficient Threat Evaluation system that can refine various sensor data's linkages and calculate reliable threat values under unpredictable war situations. In this paper, we suggest new threat evaluation system based on information fusion JDL model, and it is principle that combine fuzzy which is favorable to refine ambiguous relationships with bayesian network useful to inference battled situation having insufficient evidence and to use learning algorithm. Finally, the system's performance by getting threat evaluation on an air defense scenario is presented.

• International Commerce and Information Review
• /
• v.17 no.3
• /
• pp.251-278
• /
• 2015

Previous researches have been conducted on the drivers of alliance performance up to date. Unlike existing literatures, we examine the influence of alliance management capability as direct drivers on venture's alliance performance. Based upon organizational capability theory, we proposed that coordination capability, relationship capability, learning capability are main components of alliance management capabilities. In addition, we proposed capability of CEO as forth alliance management capability in global ventures. Using 78 global ventures as research sample, this study shows that these four alliance capabilities have considerable influence on financial performance of ventures with global alliances.

• Journal of Intelligence and Information Systems
• /
• v.29 no.1
• /
• pp.175-201
• /
• 2023

In the midst of going through a non-face-to-face society, most of human activities narrowed down to the platform, restrictions on external activities are bringing the internal scalability of digital technology. Metaverse is virtually shifting reality and increasing the possibility of utilization in various areas. However, researches linked to the educational effects of metaverse, especially students with disabilities, are still an unknown area that lacks exploration. This paper focuses on the fact that metaverse-education is widening educational fields that meets the various needs of disabled students to realize social good and inclusive education, and communication effects such as resolving barriers to interaction are prominent. As a research method, examining literature research papers linked to AR/VR, metaverse with communication skills, interviews, articles, and columns by experts, and policy suggestions and implications for the special education was conducted. Although the limitations of research are confirmed, significant results are found on inclusive education, which provides educational maximizing effects and realizing human rights through direct immersive experience reflecting the Cone of Experience Theory. Hopefully follow-up studies on meta-edu for disabled students will be carried out in the future, and various interdisciplinary discussions are needed to carefully observe inclusive policies and benefits so that the socially vulnerable are not excluded from technologies in ICT society.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2002.05a
• /
• pp.473-476
• /
• 2002

In this paper by realizing the knowledge base on the web, for example, the information of the technology and human resources of the universities, which is the mecca of the knowledge management, technology information in various segment, education information, visible anti invisible knowledge information like technical advice, etc, and scholaristic knowledge like basic science technologies, economic theories which are verified by experts of the field, and pragmatic knowledge like technical patent, software, data base, also, hands on experience and knowledge like field technology, customer service, front line management. All those related information provides easy access to anyone who need of. This system is also designed for the utilization of the study, company activities, and professionals of the basic science technology which enables not only professor to exchange the research results but also, general public, companies, students can effectively share and exchange the information.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.5
• /
• pp.921-929
• /
• 2011

In Information age, the academic liberal art Computer education course set up goals for promoting computer literacy and for developing the ability to cope actively with in Information Society and for improving productivity and competition among nations. In this paper, we analyze on discovering of decisive property and satisfaction index to have a influence on computer education on university students. As a preprocessing method, the proposed method select optimum property using correlation feature selection of machine learning tool based on Java and then we use multiclass least square support vector machine based on statistical learning theory. After applying that compare with multiclass support vector machine and multiclass least square support vector machine, we can see the fact that the proposed method have a excellent result like multiclass support vector machine in analysis of the academic liberal art computer education satisfaction index data.