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Analysis of Initial Response Status in Online Information Provider Systems
http://doi.org/10.5626/JOK.2019.46.7.620
Online information provider system (OIPS) can contribute to the fast dissemination of information and build new public opinions. However, the increase in OIPS leads to side effects such as fake news, crucial invasion of privacy, online bullying etc. To promote positive functions and prevent side effects, an automated system for analyzing the initial status of information is necessitated. In this paper, for the first time in literature, we provide four criteria for automated analysis and suggest a new model for analysis. After the design of the online information analysis concept, we crawl real data from an OIPS and successfully conducted the initial status analysis.
Analyzing the Impact of Convolution Coding Effect on the Performance Improvement for the Link-16 Enhanced Throughput
http://doi.org/10.5626/JOK.2019.46.4.391
Modern warfare requires the implementation of a combined/jointed operational environment, in which the multinational and three-dimensional powers are simultaneously deployed. The United States, NATO and its allies, including Korea, are using Link-16, which is noted as a tactical data link that enables combined/jointed operations. Currently, Link-16 is undergoing a performance improvement for utilizing crypto modernization, enhanced throughput and frequency re-mapping. In this paper, we will analyze the operability and efficiency of the Link-16 performance improvement factors, and analyze performance improvement status for double throughput improvement. We also analyze the effect of the newly applied convolutional coding to improve Link-16 throughput. Analysis of the impact of convolutional coding to improve Link-16 throughput has shown that it can gain twice as much efficiency of use in regard to the current Link-16 throughput.
Enhancing the Performance of Recommender Systems Using Online Review Clusters
Giseop Noh, Hayoung Oh, Jaehoon Lee
http://doi.org/10.5626/JOK.2018.45.2.126
The recommender system (RS) has emerged as a solution to overcome the constraints of excessive information provision and to maximize profit and reputation for information providers. Although the RS can be implemented with various approaches, there is no study on how to appropriately utilize the information generated from the review of the recommended object. We propose a method to improve the performance of RS by using cluster information generated from online review. We implemented the proposed method and experimented with real data, and confirmed that the performance is significantly improved compared to the existing approaches.
A Re-configuration Scheme for Social Network Based Large-scale SMS Spam
Sihyun Jeong, Giseop Noh, Hayoung Oh, Chong-Kwon Kim
The Short Message Service (SMS) is one of the most popular communication tools in the world. As the cost of SMS decreases, SMS spam has been growing largely. Even though there are many existing studies on SMS spam detection, researchers commonly have limitation collecting users" private SMS contents. They need to gather the information related to social network as well as personal SMS due to the intelligent spammers being aware of the social networks. Therefore, this paper proposes the Social network Building Scheme for SMS spam detection (SBSS) algorithm that builds synthetic social network dataset realistically, without the collection of private information. Also, we analyze and categorize the attack types of SMS spam to build more complete and realistic social network dataset including SMS spam.
Designing an Algorithm for the Priority Deciding and Recommending of the Logistic Support with Stationary Distribution
Giseop Noh, Sihyun Jeong, Chong-Kwon Kim, Hayoung Oh
One of the important roles used to ensure victory in a war is to maximize the overall military forces and to make sure that the capability of the military forces can be sustained as much as possible. Although several researchers have proposed various possible methodologies for logistics support, no research trials have been undertaken to investigate logistics support that considers all relevant elements of such. Unlike previous in trials that consider and analyze the system fault ratio as the main methodology, we propose an approach that simultaneously decides and recommends logistic priority by reflecting and combining item costs, transportation, fault-ratio, and system complexity. Also, we designed an algorithm that can recommend optimized logistics support priority using stationary distribution.
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