• Journal of Information Processing Systems
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• v.20 no.2
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• pp.215-225
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• 2024

With the rapid development of Internet of Things (IoT) and big data technology, a large amount of data will be generated during the operation of related industries. How to classify the generated data accurately has become the core of research on data mining and processing in IoT industry chain. This study constructs a classification model of IoT industry chain based on improved random forest algorithm and text analysis, aiming to achieve efficient and accurate classification of IoT industry chain big data by improving traditional algorithms. The accuracy, precision, recall, and AUC value size of the traditional Random Forest algorithm and the algorithm used in the paper are compared on different datasets. The experimental results show that the algorithm model used in this paper has better performance on different datasets, and the accuracy and recall performance on four datasets are better than the traditional algorithm, and the accuracy performance on two datasets, P-I Diabetes and Loan Default, is better than the random forest model, and its final data classification results are better. Through the construction of this model, we can accurately classify the massive data generated in the IoT industry chain, thus providing more research value for the data mining and processing technology of the IoT industry chain.

• Korean Management Science Review
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• v.26 no.3
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• pp.101-115
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• 2009

In this paper, we propose a technology mining framework for mobile communication industry. We develop a two phase approach of new technology identification and service enhancement. The new technology identification process consists of R&D issues analysis, technology theme design, and emerging technology sampling. On the other hand, existing service enhancement process has technology landscaping, keyword based search, and technological growth analysis. By implementing these two phase frameworks, we develop a technology portfolio for mobile communication industry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4489-4495
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• 2010

This study aims to examine the current situation of safety education of coal mining industry in Gangwon province, and suggest its problems and improvement directions. And the theoretical background was established through the literature, a survey study for general workers were conducted and its data were analyzed.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.157-161
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• 2022

The coffee industry continues to grow steadily due to the spread of coffee and changes in consumer awareness. Once upon a time, instant coffee was common, People today have distinct personal preferences As consumption needs for favorite foods are segmented, ways to enjoy coffee are diversifying. This study was conducted through analysis of consumption changes for specialty coffee as a changed issue of COVID-19 The goal is to present a vision for the future of the specialty coffee industry. As a research method, text mining through big data analysis was conducted to extract and analyze factors affecting the change in specialty coffee consumption. As a result of the study, we judged that specialty coffee is consumed by using a drip tool that allows you to easily enjoy coffee at home after Corona 19. Therefore, hand drips used in home cafes were found to play a central role in the change in specialty coffee consumption.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.764-770
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• 2019

In this research, text mining techniques were used to analyze the papers related to the "4th Industry". In order to analyze the papers, total of 685 papers were collected by searching with the keyword "4th industry" in Korea Journal Index(KCI) from 2016 to 2019. We used Python-based web scraping program to collect papers and use topic modeling techniques based on LDA algorithm implemented in R language for data analysis. As a result of perplexity analysis on the collected papers, nine topics were determined optimally and nine representative topics of the collected papers were extracted using the Gibbs sampling method. As a result, it was confirmed that artificial intelligence, big data, Internet of things(IoT), digital, network and so on have emerged as the major technologies, and it was confirmed that research has been conducted on the changes due to the major technologies in various fields related to the 4th industry such as industry, government, education field, and job.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.118-122
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• 2005

Data mining is the process of autonomously extracting useful information or knowledge from large data stores or sets. For analyzing data of manufacturing processes obtained from database using data mining, source data should be collected form production process and transformed to appropriate form. To extract those data from database, a computer program should be made for each database. This paper presents a program to extract easily data form database in industry. The advantage of this program is that user can extract data from all types of database and database table and interface with Teamcenter Manufacturing.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.1
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• pp.86-96
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• 2015

A lot of research works in the BPM area focuses on the development of new techniques in process mining. Even though the application of process mining to analyze real life process logs is important, only few case studies are available. Thus, in this paper, we conduct a case study on how to analyze a real life process log which comes from a Korean company in the heavy industry area. We analyze a customer service process that consists of a series of activities to enhance the level of customer satisfaction. In this case study, five research questions are derived based on collected questions from the company. Then we focus on bottleneck analysis, basic performance analysis and pattern analysis that are selected in order to answer the research questions. The analysis shows some abnormal behaviors in the process and possible ways to improve current processes are suggested.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.135-154
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• 2003

Credit scoring system (CSS) starts from an analysis of delinquency trend of each individual or industry. This paper conducts a research on credit card delinquency of bank customers as a preliminary step for building effective credit scoring system to prevent excess loan or bad credit status. To serve this purpose, we use association rules as a rule generating data mining technique. Specifically, we generate sets of rules of customers who are in bad credit status because of delinquency by association rule mining. We expect that the sets of rules generated by association rule mining could act as an estimator of good or bad credit status classifier and basic component of early warning system.

• Journal of Information Technology Applications and Management
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• v.11 no.1
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• pp.161-174
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• 2004

This study is intended to suggest1 the optimized data mining model for the efficient customer credit evaluation in the capital finance industry. To accomplish the research objective, various data mining models for the customer credit evaluation are compared and analyzed. Furthermore, existing models such as Multi-Layered Perceptrons, Multivariate Discrimination Analysis, Radial Basis Function, Decision Tree, and Logistic Regression are employed for analyzing the customer information in the capital finance market and the detailed data of capital financing transactions. Finally, the data from the integrated model utilizing a genetic algorithm is compared with those of each individual model mentioned above. The results reveals that the integrated model is superior to other existing models.

• Tunnel and Underground Space
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• v.30 no.1
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• pp.29-38
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• 2020

Recent research trend of North Korean mining and rock engineering in the past 10 years was analyzed by a literature review of mining and rock engineering papers published in North Korean major mining journals, 'mining engineering', 'geological and geographical science' and 'technology innovation' published in 2008-2017. Basic database was established by organizing bibliographic information and abstract data of research papers in each journal. For each journal, paper submission trend classified by research field was analyzed using the basic database. And further study was conducted to the papers which showed distinguishing methodology and result, to analyze the trend of North Korean mining and rock engineering. The literature study showed a recent trend of quantification and automation in mining and rock engineering researches in North Korea, which seems due to recent changes in North Korea's science and technology policy and deterioration of the mining conditions. The results of this study can be applied in the feasibility studies of North Korea's mineral resource development projects. Future inter-Korean technical cooperation and site survey on North Korean field can secure complement the reliability of this study.