• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Management & Information Systems Review
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• v.38 no.3
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• pp.55-80
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• 2019

Recently, many companies improving their management performance by building a powerful brand value which is recognized for trademark rights. However, as growing up the size of online commerce market, the infringement of trademark rights is increasing. According to various studies and reports, cases of foreign and domestic companies infringing on their trademark rights are increased. As the manpower and the cost required for the protection of trademark are enormous, small and medium enterprises(SMEs) could not conduct preliminary investigations to protect their trademark rights. Besides, due to the trademark image search service does not exist, many domestic companies have a problem that investigating huge amounts of trademarks manually when conducting preliminary investigations to protect their rights of trademark. Therefore, we develop an intelligent similar trademark search model to reduce the manpower and cost for preliminary investigation. To measure the performance of the model which is developed in this study, test data selected by intellectual property experts was used, and the performance of ResNet V1 101 was the highest. The significance of this study is as follows. The experimental results empirically demonstrate that the image classification algorithm shows high performance not only object recognition but also image retrieval. Since the model that developed in this study was learned through actual trademark image data, it is expected that it can be applied in the real industrial environment.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.30-37
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• 2021

Various devices are connected to the Internet, and attacks using the Internet are occurring. Among such attacks, there are attacks that use malicious URLs to make users access to wrong phishing sites or distribute malicious viruses. Therefore, how to detect such malicious URL attacks is one of the important security issues. Among recent deep learning technologies, neural networks are showing good performance in image recognition, speech recognition, and pattern recognition. This neural network can be applied to research that analyzes and detects patterns of malicious URL characteristics. In this paper, performance analysis according to various parameters was performed on a method of detecting malicious URLs using neural networks. In this paper, malicious URL detection performance was analyzed while changing the activation function, learning rate, and neural network structure. The experimental data was crawled by Alexa top 1 million and Whois to build the data, and the machine learning library used TensorFlow. As a result of the experiment, when the number of layers is 4, the learning rate is 0.005, and the number of nodes in each layer is 100, the accuracy of 97.8% and the f1 score of 92.94% are obtained.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.1-6
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• 2021

Determining the percentage of negligence between the parties in the event of road traffic accidents is a significant problem. In order to provide users with more accurate criteria for determining the percentage of negligence, several companies are providing services. However, services currently available are limited to immediate use at the scene of an accident. Generally, the service that determines the percentage of negligence can be used after all accident handling procedures have been completed. This paper provides a real-time traffic accident type and fault rate information provision service utilizing a deep learning-based predictive model to overcome these limitations. Users can immediately identify accident types and fault information by taking pictures at the accident site and check actual precedents of the same accident type. Users will be able to use the service to more accurately and reliably determine the percentage of negligence and handle incidents.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.1-10
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• 2022

Vegetables such as cabbage are greatly affected by natural disasters, so price fluctuations increase due to disasters such as heavy rain and disease, which affects the farm economy. Various efforts have been made to predict the price of agricultural products to solve this problem, but it is difficult to predict extreme price prediction fluctuations. In this study, cabbage prices were analyzed using the ensemble Voting technique, a method of determining the final prediction results through various classifiers by combining a single classifier. In addition, the results were compared with LSTM, a time series analysis method, and XGBoost and RandomForest, a boosting technique. Daily data was used for price data, and weather information and price index that affect cabbage prices were used. As a result of the study, the RMSE value showing the difference between the actual value and the predicted value is about 236. It is expected that this study can be used to select other time series analysis research models such as predicting agricultural product prices

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.198-201
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• 2022

Recently, new cyber threats targeting new technologies are increasing, and hackers' attack targets are becoming broader and more intelligent. To counter these attacks, major security companies are using traditional EDR (Endpoint Detection and Response) solutions. However, the conventional method does not consider the context, so there is a limit to the accuracy and efficiency of responding to an advanced attack. In order to improve this problem, the need for a security solution centered on XDR (Extended Detection and Response) has recently emerged. In this study, we present effective threat detection and countermeasures in a changing environment through XDR trends and development roadmaps using machine learning-based context analysis.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.297-309
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• 2023

The degradation of coastal ecosystems and fishery environments is accelerating due to the recent phenomenon of invertebrate grazers. To effectively monitor and implement preventive measures for this phenomenon, the adoption of remote sensing-based monitoring technology for extensive maritime areas is imperative. In this study, we compared and analyzed the robustness of deep learning-based object detection modelsfor detecting and monitoring invertebrate grazersfrom underwater videos. We constructed an image dataset targeting seven representative species of invertebrate grazers in the coastal waters of South Korea and trained deep learning-based object detection models, You Only Look Once (YOLO)v7 and YOLOv8, using this dataset. We evaluated the detection performance and speed of a total of six YOLO models (YOLOv7, YOLOv7x, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) and conducted robustness evaluations considering various image distortions that may occur during underwater filming. The evaluation results showed that the YOLOv8 models demonstrated higher detection speed (approximately 71 to 141 FPS [frame per second]) compared to the number of parameters. In terms of detection performance, the YOLOv8 models (mean average precision [mAP] 0.848 to 0.882) exhibited better performance than the YOLOv7 models (mAP 0.847 to 0.850). Regarding model robustness, it was observed that the YOLOv7 models were more robust to shape distortions, while the YOLOv8 models were relatively more robust to color distortions. Therefore, considering that shape distortions occur less frequently in underwater video recordings while color distortions are more frequent in coastal areas, it can be concluded that utilizing YOLOv8 models is a valid choice for invertebrate grazer detection and monitoring in coastal waters.

• Journal of Internet Computing and Services
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• v.13 no.6
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• pp.41-53
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• 2012

Open educational resources(OER) enable the spread of mutual information exchange and provide advantages to both their users and institutions, such as reducing costs, improving content quality, and establishing relationships. The recent research on OER was about their connection to formal education, copyright trends, and corporate e-learning. There have been very few studies, however, on the utilization of OER and on the problems related to their practical use. Thus, this study was conducted for the purposes of analyzing the status of OER usage in education-related institutions and of providing suggestions for institution operation based on the analysis results, to promote the use of OER. A survey was conducted among more than 200 institutions in Asia, and the survey results showed that 'images and visual materials' are the most commonly used materials in Asia, and that the factors barring OER usage in the said region are 'lack of awareness', 'lack of skills', 'the absence of a reward system', and poor cooperation in participation. To promote OER usage, each institution should provide training courses about awareness, utilization skills, and copyrights. There is also a need to provide support for the establishment of reward systems and environments for OER usage. Finally, more active participation is required for inter-agency cooperation in OER sharing.

• Journal of the Korea Convergence Society
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• v.8 no.4
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• pp.1-8
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• 2017

In the modern society, the size of the fashion market is continuously increasing both overseas and domestic. When purchasing a product through e-commerce, the evaluation data for the product created by other consumers has an effect on the consumer's decision to purchase the product. By analysing the consumer's evaluation data on the product the company can reflect consumer's opinion which can leads to positive affect of performance to company. In this paper, we propose a method to construct a model to analyze user's sentiment using word embedding space formed by learning review data of amazon fashion products. Experiments were conducted by learning three SVM classifiers according to the number of positive and negative review data using the formed word embedding space which is formed by learning 5.7 million Amazon review data.. Experimental results showed the highest accuracy of 88.0% when learning SVM classifier using 50,000 positive review data and 50,000 negative review data.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.8-13
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• 2019

Recently, In line with the recent trend of the fourth industrial revolution, many companies and institutions have been increasingly applying automated technologies using artificial intelligence to various tasks. Particularly, due to the government's 52-hour workweek system, companies are increasingly struggling with manpower management. Therefore, they are interested in RPA (Robotic Process Automation) for office environment automation for efficient manpower management. It is being introduced in the back-office business in credit card companies, bank, insurance. These RPA solutions require AI-based recognition technology, scripting technology, business software API-related technologies, and various solutions such as Automate One, Automation Anywhere, UiPath, and Blue Prism are provided. This paper analyzes and describes the technology of RPA solution, the market trend, and the efficiency of RPA adoption.