• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.473-481
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• 2020

Data science starts with small data analysis and includes machine learning and deep learning for big data analysis. Data science is a core area of artificial intelligence technology and should be systematically reflected in the school curriculum. For data science education, The Entry also provides a data analysis tool for elementary education. In a big data analysis, data samples are extracted and analysis results are interpreted through statistical guesses and judgments. In this paper, the big data analysis area that requires statistical knowledge is excluded from the elementary area, and data science education examples focusing on the elementary area are proposed. To this end, the general data science education stage was explained first, and the elementary data science education stage was newly proposed. After that, an example of comparing values of data variables and an example of analyzing correlations between data variables were proposed with public small data provided by Entry, according to the elementary data science education stage. By using these Entry data-analysis examples proposed in this paper, it is possible to provide data science convergence education in elementary school, with given data generated from various subjects. In addition, data science educational materials combined with text, audio and video recognition AI tools can be developed by using the Entry.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.1-9
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• 2023

Research using AI and big data is also being actively conducted in the health and medical fields such as disease diagnosis and treatment. Most of the existing research data used cohort data from research institutes or some patient data. In this paper, the difference in the prediction rate of survival and the factors affecting survival between breast cancer patients in their 40~50s and other age groups was revealed using health insurance review claim data held by the HIRA. As a result, the accuracy of predicting patients' survival was 0.93 on average in their 40~50s, higher than 0.86 in their 60~80s. In terms of that factor, the number of treatments was high for those in their 40~50s, and age was high for those in their 60~80s. Performance comparison with previous studies, the average precision was 0.90, which was higher than 0.81 of the existing paper. As a result of performance comparison by applied algorithm, the overall average precision of Decision Tree, Random Forest, and Gradient Boosting was 0.90, and the recall was 1.0, and the precision of multi-layer perceptrons was 0.89, and the recall was 1.0. I hope that more research will be conducted using machine learning automation(Auto ML) tools for non-professionals to enhance the use of the value for health insurance review claim data held by the HIRA.

• Design & Manufacturing
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• v.11 no.1
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• pp.45-49
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• 2017

Currently, a lot of efforts to make increases the manufacturing efficiency have tried and there is growing the interest to implementing the machining operation through CAM automation and optimization. This kind of movement has shown gradually in 5X milling as well as 3X milling task. By the way, in case of 5X milling, it is difficult to hire the CAM experts who is an experience for 5X machining and also it has too big trouble to use them due to high cost. For this reason, you can see the manufacturer who is concern the CAM S/W to provide the NC automation program that beginners can generate easily the 5X milling in short term and the existing 5X milling process can be improved. These requirements need to make a NC automation process including the practical machining strategies same as the generation by NC expert. In order to support this, it is necessary to directly apply the 3D machining part based on NC template which includes the machining procedures, standard cutter library, auto machine area selection, analyze tool for part shape, machining condition setting considering the material stiffness to be provided by CimatronE and it should be created the 5axis machining data by a minimized operation. With user-friendly, CimatronE's NC machining automation tools improve the 5-axis machining process and speed up the process, maximizing work efficiency and improving product productivity compared to existing machining tasks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.277-283
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• 2019

Advances in technology have allowed the development and commercialization of various parts; however this has shortened the discontinuation cycle of the components. This means that repair and logistic support of weapon system which is applied to thousands of part components and operated over the long-term is difficult, which is the one of main causes of the decrease in the availability of weapon system. To improve this problem, the United States has created a special organization for this problem, whereas in Korea, commercial tools are used to predict and manage DMSMS. However, there is rarely a method to predict life cycle of parts that are not presented DMSMS information at the commercial tools. In this study, the structured and unstructured data of parts of a commercial tool were gathered, preprocessed, and embedded using neural network algorithm. Then, a method is suggested to predict the life cycle risk (LC Risk) and year to end of life (YTEOL). In addition, to validate the prediction performance of LC Risk and YTEOL, the prediction value is compared with descriptive statistics.

• Journal of Digital Convergence
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• v.16 no.3
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• pp.201-210
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• 2018

WorldJob+, being operated by The Human Resources Development Service of Korea, provides a recruitment support services to overseas companies wanting to hire talented Korean applicants and interns, and support the entire course from overseas advancement information check to enrollment, interview, and learning for young job-seekers. More than 300,000 young people have registered in WorldJob+, an overseas united information network, for job placement. To innovate WorldJob+'s services for young job-seekers, Splunk, a powerful platform for analyzing machine data, was introduced to collate and view system log files collected from its website. Leveraging Splunk's built-in data visualization and analytical features, WorldJob+ has built custom tools to gain insight into the operation of the recruitment supporting service system and to increase its integrity. Use cases include descriptive and predictive analytics for matching up services to allow employers and job seekers to be matched based on their respective needs and profiles, and connect jobseekers with the best recruiters and employers on the market, helping job seekers secure the best jobs fast. This paper will cover the numerous ways WorldJob+ has leveraged Splunk to improve its recruitment supporting services.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.164-170
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• 2017

The CNC machine tool field is showing a growing trend with the recent rapid development of manufacturing industries such as semiconductors, automobiles, medical devices, various inspection and test equipment, mechanical metal processing equipment, aircraft, shipbuilding and electronic equipment. However, small and medium-sized machining companies that use CNC machine tools are experiencing difficulties in increasingly intense competition. Especially, small companies which are receiving orders from 3rd or 4th venders are very difficult in business management. In recent years, company S experienced difficulty to make product quality and delivery time due to the ignorance of the processing method when manufacturing cooling plate jig made of SUS304 material used for cell phone liquid crystal glass processing. In order to solve these problems, we redesigned the process according to the size of our company and tried to manage all processes with quantified data. In the meantime, we have found that there is a need to improve the cutter process, which accounts for most of the machining process. Therefore, we have investigated the correlation between RPM and FEED of three cutters that have been used in the past. As a result, we found that it is the most urgent problem to solve the roughing process during the cutter operation which occupies more than 70% of the total machining. In order to shorten the machining time and improve the quality in machining of SUS304 cooling plate jig, we select the main factors such as price, tool life, maintenance cost, productivity, quality, RPM, and FEED and use AHP to find the most suitable milling cutter. We also tried to solve the problem of delivery, quality and production capacity which was a big problem of S company through experiment operation with selected cutter tool. As a result, the following conclusions were drawn. First, the most efficient of the three cutters currently available in the machining center has proven to be an M-cutter. Second, although one additional facility was required, it was possible to produce the existing facilities without additional investment by supplementing the lack of production capacity due to productivity improvement. Third, the Company's difficulties in delivery and capacity shortfalls have been resolved. Fourth, annual sales increased by KRW 109 million and profits increased by KRW 32 million annually. Fifth, it can confirm the usefulness of AHP method in corporate decision making and it can be utilized in various facility investment and process improvement in the future.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.76-86
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• 2016

SQL Injection attacks are the most widely used and also they are considered one of the oldest traditional hacking techniques. SQL Injection attacks are getting quite complicated and they perform a high portion among web hacking. The big data environments in the future will be widely used resulting in many devices and sensors will be connected to the internet and the amount of data that flows among devices will be highly increased. The scale of damage caused by SQL Injection attacks would be even greater in the future. Besides, creating security solutions against SQL Injection attacks are high costs and time-consuming. In order to prevent SQL Injection attacks, we have to operate quickly and accurately according to this data analysis techniques. We utilized data analytics and machine learning techniques to defend against SQL Injection attacks and analyzed the execution plan of the SQL command input if there are abnormal patterns through checking the web log files. Herein, we propose a way to distinguish between normal and abnormal SQL commands. We have analyzed the value entered by the user in real time using the automated SQL Injection attacks tools. We have proved that it is possible to ensure an effective defense through analyzing the execution plan of the SQL command.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.