• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.11-24
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• 2022

For the questions asked for LMS-based online evaluation the professor directly set exam questions, or use the automatic question-taking method according to the level of difficulty using the question bank divided by category. Among them, it is important to manage the difficulty of questions in an objective and efficient way, above all, in the automatic question-taking method according to difficulty. Because the questions presented to the evaluators may be different. In this paper, we propose an difficulty re-adjustment algorithm that considers not only the correct rate of a problem but also the time taken to solve the problem. For this, a logistic regression classification algorithm was used of machine learning, and a reference threshold was set based on the predicted probability value of the learning model and used to readjust the difficulty of each item. As a result, it was confirmed that there were many changes in the difficulty of each item that depended only on the existing correct rate. Also, as a result of performing group evaluation using the adjustment difficulty problem, it was confirmed that the average score improved in most groups compared to the difficulty problem based on the percentage of correct answers.

• Journal of the Korean Institute of Gas
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• v.26 no.4
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• pp.18-26
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• 2022

The Korean government is conducting heat-using equipment(industrial boiler) inspection in accordance with the Energy Use Rationalization Act because of the heat-using equipment(industrial boiler)'s risks such as explosion and fire, and safe use and management. This paper aimed to setup the safe and performance vulnerabilities from database based on the inspection results for heat-using equipment(industrial boiler). This study surveyed the inspection results of 1,249 heat-using equipment(industrial boiler) which were failed inspection of heat-using equipment(industrial boiler) from january 2016 to december 2020. And the analysis method is to inform safety and performance vulnerability categories of heat-using equipment(industrial boiler) by statistically analyzing the failure reasons of boiler type and inspection type which are high variance in failure rate. The safety and performance vulnerability categories was abbreviated into 18 cases. And each catagory's main reason for failure was suggested by additional analyzing the opinions of inspectors. This paper would be the basic source and the comprehensive information dealing with the safety and performance vulnerability of heat-using equipment(industrial boiler).

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.113-120
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• 2024

With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

• 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.

• Information Systems Review
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• v.10 no.2
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• pp.137-158
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• 2008

Rapid development of various information technologies creates new opportunities in online and offline markets. In this changing market environment, customers have various demands on new products and services. Therefore, their power and influence on the markets grow stronger each year. Companies have paid great attention to customer relationship management. Especially, personalized product recommendation systems, which recommend products and services based on customer's private information or purchasing behaviors in stores, is an important asset to most companies. CRM is one of the important business processes where reliable information is mined from customer database. Data mining techniques such as artificial intelligence are popular tools used to extract useful information and knowledge from these customer databases. In this research, we propose a recommendation system that predicts customer's purchase intention. Then, customer's purchasing intention of specific product is predicted by using data mining techniques using receipt data set. The performance of this suggested method is compared with that of other data mining technologies.

• Journal of the Korean Institute of Landscape Architecture
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• v.14 no.2
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• pp.81-90
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• 1986

The full grasp of recreation demand and factors affecting on recreation demand can be very important information for park planning and management. The object-tives of this study are to investigate factors affecting the fluctuation of urban park visitors and to analyze the relationship between these factors and the daily parti-cipations. The results were as follows; 1) The peak of monthly participations comes on May, April, August and October in order. And these months are specified as school picnic period and vacation of school children. 2) In correlation analysis, the variables such as ‘Day of a week(D)’, ‘Monthly mean temp.(T)’and ‘Monthly character(M)’have high correlations with ‘No. of visitors’in order. And it is better to categorize months by its charater(picnic period in school, vacation etc) than by seasons. 3) Candidate regression model were established, as for 1984 log U= 1.51 + 0.64D1 + 0.02T + 0.36W1 - 0.23M4 + 0.003SS + 0.24Ml($R^2$=0.5326) where, U=no. of daily visitors D1 = sunday.ho1iday(1), weekday(0) T=monthly mean temperature($^{\circ}C$) W1= weather (sunny.cloudy(1) , rainy (>5mm)(0)> M4=non vacations and non school picnic period(1) , if not (0) SS=monthly sunshining hours M1=summer vacation(1), if not(0) 4) The most important variable was ‘Day of a week’(sunday.holiday or not). And temperature, weather and monthly charcter(especially picnic period of school and vacation) were in turn, hence ‘Children's grand park’shows the use pattern of park.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.217-242
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• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.45-63
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• 2019

The number of domestic construction company is expanding every year while the construction workers' exposure to disaster risk is increasing due to technological advancements and popularity of high-rise buildings. In particular, the industry faces greater fatalities and severe large scale accidents because of construction industry characteristics including influx of foreign workers with different language and culture, large number of aged workers, outsourcing, high place work, heavy machine construction. The construction industry is labor-intensive, which is to be completed under given timeline and consists of unique working environment with a lot of night shifts. In addition, when a fixed construction budget is not secured, there is less investment in safety management resulting in poor risk management at the construction site. Taking account that the construction industry has higher accident risk rate and fatality rate, risky and unique working environment, and various labor pool from foreign to aged workers, preemptive safety management through risk factor identification is a mandatory requirement for the construction industry and site. The study analyzes about 8,500 cases of construction accidents that occurred over the past 10 years and identified risk factor by construction industry sector to secure a systematic insight for risk management. Based on interrelation analysis between accident types, work types, original cause materials and assailing materials, there is correlation between each analysis factor and work industry. Especially for work types, there is great correlation between work tasks and industry type. For reinforced concrete and earthwork are among the most frequent types of accidents, and they are not only high in frequency of accidents, but also have a high risk in categories of occurrence.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.51-64
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• 2014

It becomes more and more important for business survival to have the ability to predict the future with uncertainties increasing faster and faster. To predict the future, text mining tools are one of the main candidate other than traditional quantitative analyses, but those efforts are still at their infancy. This paper is to introduce one of those efforts using the case of company "P" in the steel industry. Even with only four month pilot studies, we found strong possibilities, if not testified robustly, to predict future industrial trends using text mining tools. For these text mining case studies, we categorized steel industry trend keywords into ten components (10 categories) to study ten different subjects for each category. Once found any meaningful changes in a trend, we had investigated in more detail what and how some trend happened so. To be more roust, firstly we need to define more cleary the purpose of text mining analyses. Then we need to categorize industry trend key words in a more systematic way using systems thinking models. With these improvements, we are quite sure that applying text mining tools to analyzing industry trends will contribute to predicting the future industry trends as well as to identifying the unseen trends otherwise.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.777-787
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• 2020

With the development of the Internet and the spread of mobile devices, the online market is growing rapidly. As the number of customers using online shopping malls explodes, research is being conducted on the analysis of usage behavior from customer data, personalized product recommendations, and service development. Thus, this paper seeks to analyze the overall process of online shopping malls through process mining, and to identify the factors that influence users' purchases. The data used are from a large online shopping mall, and R was the analysis tool. The results show that customer activity was most prominent in categories with event elements, such as unconventional discounts and monthly giveaway events. On the other hand, searches, logins, and campaign activity were found to be less relevant than their importance. Those are very important, because they can provide clues to a customer's information and needs. Therefore, it is necessary to refine the recommendations from related search words, and to manage activity, such as coupons provided when customers log in. In addition to the previous discussion, this paper proposes various business strategies to enhance the competitiveness of online shopping malls and to increase profits.