• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.12
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• pp.1980-1991
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• 2010

This study proposes the O P E N Triad framework as a future set of tools and perspectives for individual members and institutes to further their professional and academic potential as well as prospect and vitalize the future of the Korean Clothing and Textiles discipline through a global perspective. The millennial generation desires On-demand, Personal, Engaging, and Networked (O P E N) experiences effecting cultural change for creative and influential interaction in transactions, communication, and education. O P E N Individuals offers a WebSphere model as a holistic learning system that has a synergizing value of education across academic courses, industries, and cultures. Through a digitalized and virtualized class, it complements relevant technologies already familiar to the student population. By employing environmental scanning approaches, the most influential and viable future global issues related to the clothing and textiles discipline are identified and dialogued within O P E N Institutes. For future clothing and textiles institutes, this scanning allows them to be open to new ideas, to focus on inter-engagements, to collaborate among individuals, to associate as a part of web of people, organizations, and ideas, to personalize an institutes curricula, and to dialogue generative knowledge. O P E N Industries reveals three dominant future issues that cross academia and industry, sustainability, supply chain management, and social networking. In-depth interviews with U.S. industry experts identified interdependent gaps in global consumer experience practices and suggested the following gaps as future research areas: a standardized business model to the entrepreneurial model, strategic management to a sustainable competitive advantage, standardized to differentiated products, services and operations, market segmentation to global consumer clusters, business-driven marketplaces to consumer-engaged marketspaces, and excellent services to optimal experience. This O P E N Triad framework empowers millennial students, universities, and industries to anticipate and prepare for a radically changing world.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.71-101
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• 2017

Since late 2016, protectionism has been a major trend in world trade with the Great Britain exiting the European Union and the United States electing Donald Trump as the 45th president. Consequently, there has been a huge public outcry regarding the negative prospects of heavy industry firms in Korea, which are highly dependent upon international trade with Western countries including the United States. In light of such trend and concerns, we have tried to predict business performance of heavy industry firms in Korea with data regarding trade policy of the United States. United States International Trade Commission (USITC) levies countervailing duties and anti-dumping duties to firms that violate its fair-trade regulations. In this study, we have performed data analysis with past records of countervailing duties and anti-dumping duties. With results from clustering analysis, it could be concluded that trade policy trends of the Unites States significantly affects the business performance of heavy industry firms in Korea. Furthermore, we have attempted to quantify such effects by employing long short-term memory (LSTM), a popular neural networks model that is well-suited to deal with sequential data. Our major contribution is that we have succeeded in empirically validating the intuitive argument and also predicting the future trend with rigorous data mining techniques. With some improvements, our results are expected to be highly relevant to designing regulations regarding heavy industry in Korea.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.63-79
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• 2022

Recently, various studies have been conducted on stock price prediction using machine learning and deep learning techniques. Among these studies, the latest studies have attempted to predict stock prices using limit order books, which contain buy and sell order information of stocks. However, most of the studies using limit order books consider only the trend of limit order books over the most recent period of a specified length, and few studies consider both the medium and short term trends of limit order books. Therefore, in this paper, we propose a deep learning-based prediction model that predicts stock price more accurately by considering both the medium and short term trends of limit order books. Moreover, the proposed model considers news headlines during the same period to reflect the qualitative status of the company in the stock price prediction. The proposed model extracts the features of changes in limit order books with CNNs and the features of news headlines using Word2vec, and combines these information to predict whether a particular company's stock will rise or fall the next day. We conducted experiments to predict the daily stock price fluctuations of five stocks (Amazon, Apple, Facebook, Google, Tesla) with the proposed model using the real NASDAQ limit order book data and news headline data, and the proposed model improved the accuracy by up to 17.66%p and the average by 14.47%p on average. In addition, we conducted a simulated investment with the proposed model and earned a minimum of $492.46 and a maximum of $2,840.93 depending on the stock for 21 business days.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.85-93
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• 2021

The government is pursuing plans to create new e-government services. In terms of improving business procedures, dBrain (finance), e-people (personnel), and Onnara (electronic payment and business management) have achieved considerable results, and are currently making efforts to improve existing administrative services using newly emerged ICT. Among them, this paper attempted to study whether self-learning-based intelligent administrative services are efficient in the work process of public officials promoting actual work and affect their continued use. Based on individual perceptions and attitudes toward advanced ICTs such as AI, big data, and blockchain, public officials' influences on administrative services were identified and verified using UTAUT variables. They believe that the establishment and introduction of innovative administrative services can be used more efficiently, and they have high expectations for the use and provision of services as ICT develops. In the future, a model will be also applied to citizens

• The Journal of Bigdata
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• v.8 no.2
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• pp.201-222
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• 2023

As the e-Commerce gets increased based on the platform, a lot of small and medium sized sellers have tried to develop the more effective strategies to maximize the profit. In order to increase the profitability, it is quite important to make the strategic decisions based on the range of promotion, discount rate and categories of products. This research aims to develop the business intelligence application which can help sellers of e-Commerce platform make better decisions. To decide whether or not to promote, it is needed to predict the level of increase in sales after promotion. I n this research, we have applied the various machine learning algorithm such as MLP(Multi Layer Perceptron), Gradient Boosting Regression, Random Forest, and Linear Regression. Because of the complexity of data structure and distinctive characteristics of product categories, Random Forest and MLP showed the best performance. It seems possible to apply the proposed approach in this research in support the small and medium sized sellers to react on the market changes and to make the reasonable decisions based on the data, not their own experience.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.835-846
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• 2020

BEC (Business Email Compromise) attacks are frequently occurring by impersonating accounts or management through e-mail and stealing money or sensitive information. This type of attack accounts for the largest portion of the recent trade fraud, and the FBI estimates that the estimated amount of damage in 2019 is about $17 billion. However, if you look at the response status of the companies compared to this, it relies on the traditional SPAM blocking system, so it is virtually defenseless against the BEC attacks that social engineering predominates. To this end, we will analyze the types and methods of BEC accidents and propose ways to effectively counter BEC attacks by companies through AI(Artificial Intelligence).

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.699-706
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• 2021

Existing license plate recognition system is used as an optical character recognition method, but a method of using deep learning has been proposed in recent studies because it has problems with image quality and Korean misrecognition. This requires a lot of data collection, but the collection of license plates is not easy to collect due to the problem of the Personal Information Protection Act, and labeling work to designate the location of individual license plates is required, but it also requires a lot of time. Therefore, in this paper, to solve this problem, five types of license plates were created using a virtual Korean license plate generation program according to the notice of the Ministry of Land, Infrastructure and Transport. And the generated license plate is synthesized in the license plate part of collectable vehicle images to construct 10,147 learning data to be used in deep learning. The learning data classifies license plates, Korean, and numbers into individual classes and learn using YOLOv5. Since the proposed method recognizes letters and numbers individually, if the font does not change, it can be recognized even if the license plate standard changes or the number of characters increases. As a result of the experiment, an accuracy of 96.82% was obtained, and it can be applied not only to the learned license plate but also to new types of license plates such as new license plates and eco-friendly license plates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.263-283
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• 2024

Users' comments after online shopping are critical to product reputation and business improvement. These comments, sometimes known as e-commerce reviews, influence other customers' purchasing decisions. To confront large amounts of e-commerce reviews, automatic analysis based on machine learning and deep learning draws more and more attention. A core task therein is sentiment analysis. However, the e-commerce reviews exhibit the following characteristics: (1) inconsistency between comment content and the star rating; (2) a large number of unlabeled data, i.e., comments without a star rating, and (3) the data imbalance caused by the sparse negative comments. This paper employs Bidirectional Encoder Representation from Transformers (BERT), one of the best natural language processing models, as the base model. According to the above data characteristics, we propose the F_MixBERT framework, to more effectively use inconsistently low-quality and unlabeled data and resolve the problem of data imbalance. In the framework, the proposed MixBERT incorporates the MixMatch approach into BERT's high-dimensional vectors to train the unlabeled and low-quality data with generated pseudo labels. Meanwhile, data imbalance is resolved by Focal loss, which penalizes the contribution of large-scale data and easily-identifiable data to total loss. Comparative experiments demonstrate that the proposed framework outperforms BERT and MixBERT for sentiment analysis of e-commerce comments.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.71-76
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• 2004

The recent ERP and CRM is mostly focused on the conventional function performances. However, the recent business environment has brought the change in market due to the rapid progress of internet and e-commerce. It is mostly becoming e-business and spreading out as development of the relationship with other cooperating companies, the rapid progress of the relationship with customers, and intensification competitive power through the development of business progress in the organization. CRM(custom relationship management) is a kind of the marketing progress which forms, manages, and intensifies the relationship between the customers and companies to manage the acquired customers and increase the worth of customers for the company. It needs the system base which analyzes the information of customers since it functions on the basis of various information about customers and is linked to the business category such as producing, marketing, and decision making. Since ERP is extending its function to SCM, CRM, and SEM(strategic Enterprise Management), the 21 century s ERP develop as the strategy tool of e-business and, as the mediation for this, will subdivide the functions of CRM effectively by the analogic study of data. Also, to accomplish classification work of the file which in existing becomes accomplished with possibility work with an automatic movement with the user will be able to accomplish a more efficiently work the agent which in order leads the machine studying law, it is one thing with system feature.

• The Journal of Society for e-Business Studies
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• v.20 no.2
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• pp.73-91
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• 2015

Nowadays, 'books' are not limited to tangible items no longer, but can be intangible digital contents, along with advancement in e-Book technology and the growth of the markets. However, the role of e-Book viewers that links digital contents and readers has been rarely studied. In this study, therefore, we examined the effects of e-Book viewer's system quality (ease-of-use, functional diversity, interactivity) and design quality (convenient design, intuitive design, emotional design) on user satisfaction, and also tested if these relations are different by the content types (cartoon, novel, and learning contents). Research findings show that all the independent variables, except for interactivity, affect user satisfaction in overall groups. In viewing cartoon content, ease-of-use, convenient design, and emotional design were significant antecedents, and intuitive design and emotional design were found important factors in novel content, while functional diversity and interactivity affect user satisfaction in learning content. We expect these findings can provide useful insights to the providers of e-Book viewers.