• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.19-36
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• 2023

As advances in information and communication technology have made it easier for anyone to produce and disseminate information, a new problem has emerged: fake news, which is false information intentionally shared to mislead people. Initially spread mainly through text, fake news has gradually evolved and is now distributed in multimedia formats. Since its founding in 2005, YouTube has become the world's leading video platform and is used by most people worldwide. However, it has also become a primary source of fake news, causing social problems. Various researchers have been working on detecting fake news on YouTube. There are content-based and background information-based approaches to fake news detection. Still, content-based approaches are dominant when looking at conventional fake news research and YouTube fake news detection research. This study proposes a fake news detection method based on background information rather than content-based fake news detection. In detail, we suggest detecting fake news by utilizing related video information from YouTube. Specifically, the method detects fake news through CNN, a deep learning network, from the vectorized information obtained from related videos and the original video using Doc2vec, an embedding technique. The empirical analysis shows that the proposed method has better prediction performance than the existing content-based approach to detecting fake news on YouTube. The proposed method in this study contributes to making our society safer and more reliable by preventing the spread of fake news on YouTube, which is highly contagious.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.189-200
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• 2022

Ground subsidence on urban roads is a social issue that can lead to human and property damages. Therefore, it is crucial to detect underground cavities in advance and repair them. Underground cavity detection is mainly performed using ground penetrating radar (GPR) surveys. This process is time-consuming, as a massive amount of GPR data needs to be interpreted, and the results vary depending on the skills and subjectivity of experts. To address these problems, researchers have studied automation and quantification techniques for GPR data interpretation, and recent studies have focused on deep learning-based interpretation techniques. In this study, we described a hyperbolic event detection process based on deep learning for GPR data interpretation. To demonstrate this process, we implemented a series of algorithms introduced in the preexisting research step by step. First, a deep learning-based YOLOv3 object detection model was applied to automatically detect hyperbolic signals. Subsequently, only hyperbolic signals were extracted using the column-connection clustering (C3) algorithm. Finally, the horizontal locations of the underground cavities were determined using regression analysis. The hyperbolic event detection using the YOLOv3 object detection technique achieved 84% precision and a recall score of 92% based on AP50. The predicted horizontal locations of the four underground cavities were approximately 0.12 ~ 0.36 m away from their actual locations. Thus, we confirmed that the existing deep learning-based interpretation technique is reliable with regard to detecting the hyperbolic patterns indicating underground cavities.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.267-283
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• 2023

The COVID-19 pandemic, which began in December 2019 and continues to this day, has left the public needing information to help them cope with the pandemic. However, COVID-19-related fake news on social media seriously threatens the public's health. In particular, if fake news related to COVID-19 is massively spread with similar content, the time required for verification to determine whether it is genuine or fake will be prolonged, posing a severe threat to our society. In response, academics have been actively researching intelligent models that can quickly detect COVID-19-related fake news. Still, the data used in most of the existing studies are in English, and studies on Korean fake news detection are scarce. In this study, we collect data on COVID-19-related fake news written in Korean that is spread on social media and propose an intelligent fake news detection model using it. The proposed model utilizes the frequency information of parts of speech, one of the linguistic characteristics, to improve the prediction performance of the fake news detection model based on Doc2Vec, a document embedding technique mainly used in prior studies. The empirical analysis shows that the proposed model can more accurately identify Korean COVID-19-related fake news by increasing the recall and F1 score compared to the comparison model.

• Asia Marketing Journal
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• v.8 no.1
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• pp.49-76
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• 2006

This study investigates the effect of market orientation of knowledge-based service suppliers on the sourcing process of service recipients. Focusing on a dyadic relationship between a supplier and a buyer, this study proposed a conceptual model of market orientation incorporating the antecedents and consequences of market orientation. This study empirically tested research hypotheses delineated from the conceptual framework. The present study revealed that the impact on the buyer's performance of the supplier's customer and competitor orientation turned out to be more influential than that of inter-departmental cooperation. Also these two dimensions of customer and competitor orientation played a positive role in reducing buyer's perceived risk and uncertainty related to the evaluation of services out-sourced. Interestingly enough, the supplier's perceived importance on the distance between the buyer and supplier remains important especially when the degree of buyer's market orientation is high. This finding is somewhat contrary to the fact that the geographic location of the buyer becomes less important for the internet-based B2B service providers. Based on the findings, this study suggested managerial implications and broadened the scope of academic research in the field of business services. Future research directions and the limitations of this study are also discussed.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.433-440
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• 2021

The problem of maritime accidents due to the carelessness of fishing vessels, which is affected by the aging of fishing vessel operators. And there is navigation, communication and fish finder that is installed inside the narrow bridge of a fishing vessel. Therefore these system are monitors as many as of each terminal, which is bad influence on obscuring view of front sea from a fishing vessel bridge. In addition a large problem, it is occurs to reduce of the information recognition ability due to the confusion, which is can not check the display information each of screen equipments. Therefore, there has been demand to simply integrated the equipment, and it has wanted the integrated support system of these equipment. The display must be provided on a fishing vessels such as electronic charts, communications equipments and fish detection into one case. In this paper, the integrated system will be installed the GPS plotter, AIS, VHF-DSC, V-pass, fish finder and power supply in the narrow wheelhouse on a fishing vessel, which is configured in one case and operated by multi function display (MFD). The MFD is integrated to simplify for several multi terminals and provided necessary information on a single screen. This integration fishery support system will has improved in sea safety operation and fishery environment of fishing vessels by this implementation.

• Information Systems Review
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• v.23 no.1
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• pp.1-22
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• 2021

This study aims to analyze online game user's game items transacting behaviors for the two game genres such as MMORPG and sports game. For the sake of conducting the analysis, we adopted a fuzzy logic agent-based modeling. In the online game fields, game items transactions are crucial to game company's profitability. However, there are lack of previous studies investigating the online game user's game items transacting activities. Since many factors need to be addressed in a complicated way, ABM (agent-based modeling) simulation mechanism is adopted. Besides, a fuzzy logic is also considered due to the fact that a number of uncertainties and ambiguities exist with respect to online game user's complex behaviors in transacting game items. Simulation results from applying the fuzzy logic ABM method revealed that MMORPG game users are motivated to pay expensive price for high-performance game items, while sports game users tend to transact game items within a reasonable price range. We could conclude that the proposed fuzzy logic ABM simulation mechanism proved to be very useful in organizing an effective strategy for online game items management and customers retention.

• Tunnel and Underground Space
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• v.34 no.1
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• pp.15-27
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• 2024

In this case study, detailed survey of the Excavation Damaged Zone (EDZ) evaluation for the deep geological repository for high level nuclear waste was conducted. Oversea and Domestic case studies were compiled and investigated. EDZ is considered a crucial factor in the performance assessment of spent fuel disposal, leading to numerous studies worldwide aiming to understand the characteristics of the EDZ and quantitatively assessment of its extent through field and laboratory tests at Underground Research Laboratory (URL) sites. To enhance the understanding of EDZ, this study begins with defining and exploring the history of EDZ, compiling factors influencing EDZ, and summarizing the impacts caused by EDZ. Subsequently, an analysis of EDZ and rock properties is performed, followed by presenting generalized outcomes, limitations drawn from previous research, and proposing future research directions.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.588-597
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• 2016

In this study, we attempted to improve the biogas production efficiency by varying the mixing ratio of the mixed sludge of organic wastes in the improved single-phase anaerobic digestion process. The types of organic waste used in this study were raw sewage sludge, food wastewater leachate and livestock excretions. The biomethane potential was determined through the BMP test. The results showed that the biomethane potential of the livestock excretions was the highest at $1.55m^3CN4/kgVS$, and that the highest value of the composite sample, containing primary sludge, food waste leachate and livestock excretions at proportions of 50%, 30% and 20% respectively) was $0.43m^3CN4/kgVS$. On the other hand, the optimal mixture ratio of composite sludge in the demonstration plant was 68.5 (raw sludge) : 18.0 (food waste leachate) : 13.5 (livestock excretions), which was a somewhat different result from that obtained in the BMP test. This difference was attributed to the changes in the composite sludge properties and digester operating conditions, such as the retention time. The amount of biogas produced in the single-phase anaerobic digestion process was $2,514m^3/d$ with a methane content of 62.8%. Considering the value of $2,319m^3/d$ of biogas produced as its design capacity, it was considered that this process demonstrated the maximum capacity. Also, through this study, it was shown that, in the case of the anaerobic digestion process, the two-phase digestion process is better in terms of its stable tank operation and high efficiency, whereas the existing single-phase digestion process allows for the improvement of the digestion efficiency and performance.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.113-129
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• 2015

Location branding is a very important income making activity, by giving special meanings to a specific location while producing identity and communal value which are based around the understanding of a place's location branding concept methodology. Many other areas, such as marketing, architecture, and city construction, exert an influence creating an impressive brand image. A place brand which shows great recognition to both native people of S. Korea and foreigners creates significant economic effects. There has been research on creating a strategically and detailed place brand image, and the representative research has been carried out by Anholt who surveyed two million people from 50 different countries. However, the investigation, including survey research, required a great deal of effort from the workforce and required significant expense. As a result, there is a need to make more affordable, objective and effective research methods. The purpose of this paper is to find a way to measure the intensity of the image of the brand objective and at a low cost through text mining purposes. The proposed method extracts the keyword and the factors constructing the location brand image from the related web documents. In this way, we can measure the brand image intensity of the specific location. The performance of the proposed methodology was verified through comparison with Anholt's 50 city image consistency index ranking around the world. Four methods are applied to the test. First, RNADOM method artificially ranks the cities included in the experiment. HUMAN method firstly makes a questionnaire and selects 9 volunteers who are well acquainted with brand management and at the same time cities to evaluate. Then they are requested to rank the cities and compared with the Anholt's evaluation results. TM method applies the proposed method to evaluate the cities with all evaluation criteria. TM-LEARN, which is the extended method of TM, selects significant evaluation items from the items in every criterion. Then the method evaluates the cities with all selected evaluation criteria. RMSE is used to as a metric to compare the evaluation results. Experimental results suggested by this paper's methodology are as follows: Firstly, compared to the evaluation method that targets ordinary people, this method appeared to be more accurate. Secondly, compared to the traditional survey method, the time and the cost are much less because in this research we used automated means. Thirdly, this proposed methodology is very timely because it can be evaluated from time to time. Fourthly, compared to Anholt's method which evaluated only for an already specified city, this proposed methodology is applicable to any location. Finally, this proposed methodology has a relatively high objectivity because our research was conducted based on open source data. As a result, our city image evaluation text mining approach has found validity in terms of accuracy, cost-effectiveness, timeliness, scalability, and reliability. The proposed method provides managers with clear guidelines regarding brand management in public and private sectors. As public sectors such as local officers, the proposed method could be used to formulate strategies and enhance the image of their places in an efficient manner. Rather than conducting heavy questionnaires, the local officers could monitor the current place image very shortly a priori, than may make decisions to go over the formal place image test only if the evaluation results from the proposed method are not ordinary no matter what the results indicate opportunity or threat to the place. Moreover, with co-using the morphological analysis, extracting meaningful facets of place brand from text, sentiment analysis and more with the proposed method, marketing strategy planners or civil engineering professionals may obtain deeper and more abundant insights for better place rand images. In the future, a prototype system will be implemented to show the feasibility of the idea proposed in this paper.