• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.293-309
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• 2022

Purpose - The purpose of this study is to explore the possibility of predicting the degree of smartphone overdependence based on mobile phone usage patterns. Design/methodology/approach - In this study, a survey conducted by Korea Internet and Security Agency(KISA) called "problematic smartphone use survey" was analyzed. The survey consists of 180 questions, and data were collected from 29,712 participants. Based on the data on the smartphone usage pattern obtained through the questionnaire, the smartphone addiction level was predicted using machine learning techniques. k-NN, gradient boosting, XGBoost, CatBoost, AdaBoost and random forest algorithms were employed. Findings - First, while various factors together influence the smartphone overdependence level, the results show that all machine learning techniques perform well to predict the smartphone overdependence level. Especially, we focus on the features which can be obtained from the smartphone log data (without psychological factors). It means that our results can be a basis for diagnostic programs to detect problematic smartphone use. Second, the results show that information on users' age, marriage and smartphone usage patterns can be used as predictors to determine whether users are addicted to smartphones. Other demographic characteristics such as sex or region did not appear to significantly affect smartphone overdependence levels. Research implications or Originality - While there are some studies that predict smartphone overdependence level using machine learning techniques, but the studies only present algorithm performance based on survey data. In this study, based on the information gain measure, questions that have more influence on the smartphone overdependence level are presented, and the performance of algorithms according to the questions is compared. Through the results of this study, it is shown that smartphone overdependence level can be predicted with less information if questions about smartphone use are given appropriately.

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.588-594
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• 2020

This study analyzes the current status of drone journalism in Korean broadcasting industry, with a case study of TV Chosun Company's drone use for news gathering. It analyzes TV Chosun's record of drone logs between 2014 and 2018. The log consists of date, time, news content, users, machine and on-air date. In 2014, the total of 31 cases of using drones for news gathering is identified, while in 2018, the frequency increases to 60, with a gradual, annual increase, making the total frequency of 160, during the period between 2014 and 2018. This means that the broadcasting company used the drone news gathering system every week. The analysis also reveals that the company used this drone news gathering system mostly in the metropolitan area in Korea, amounting to more than 76 per cent of its total use: 31.3 per cent in Seoul, 36.9 per cent in Kyunggi, and 8.1 percent in Incheon. The system was more frequently used in the summer and autumn than the winter and spring. Interestingly, it was first popularly used for disaster related news, but the use of drone for social and other issues is increasing every year, which may imply its diverse use in the future.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.682-684
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• 2003

With the explosive rapid expansion of computer using during the past few years, security has become a crucial issue for modem computer systems. Today, there are many intrusion detection systems (IDS) on the Internet. A variety of intrusion detection techniques and tools exist in the computer security community such as enterprise security management system (ESM) and system integrity checking tools. However, there is a potential problem involved with intrusion detection systems that are installed locally on the machines to be monitored. If the system being monitored is compromised, it is quite likely that the intruder will after the system logs and the intrusion logs while the intrusion remains undetected. In this project KIT-I, we adopt remote logging server (RLS) mechanism, which is used to backup the log files to the server. Taking into account security, we make use of the function of SSL of Java and certificate authority (CA) based key management. Furthermore, Support Vector Machine (SVM) is applied in our project to detect the intrusion activities.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1131-1141
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• 2015

In this paper, we proposed a new approach of machine learning based method for detecting game-bots from normal players in MMORPG by inspecting the player's action log data especially in-game money increasing/decreasing event log data. DBSCAN (Density Based Spatial Clustering of Applications with Noise), an one of density based clustering algorithms, is used to extract the attributes of spatial characteristics of each players such as a number of clusters, a ratio of core points, member points and noise points. Most of all, even game-bot developers know principles of this detection system, they cannot avoid the system because moving a wide area to hunt the monster is very inefficient and unproductive. As the result, game-bots show definite differences from normal players in spatial characteristics such as very low ratio, less than 5%, of noise points while normal player's ratio of noise points is high. In experiments on real action log data of MMORPG, our game-bot detection system shows a good performance with high game-bot detection accuracy.

• Journal of Food Hygiene and Safety
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• v.29 no.3
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• pp.181-188
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• 2014

The objective of this study was to analyze the microbiological hazards of Astragalus membranaceus Bunge on the post-harvest processing. Samples from processing equipments (cleaner, water, cart, table, tray and packaging machine), personal hygiene (hand) and harvested crops (before washing, after washing, after sorting, and after drying) were collected from four farms (A, B, C, and D) located in Chungchengbuk-do, Korea. The samples were analyzed for sanitary indication bacteria and pathogenic bacteria. First, total aerobic bacteria and coliform in processing facilities were detected at the levels of 0.93~4.86 and 0.33~2.28 log CFU/$100cm^2$ and/mL respectively. In particular, microbial contamination in hand (5.43~6.11 and 2.52~4.12 log CFU/Hand) showed higher than processing equipments. Among the pathogenic bacteria, Bacillus cereus was detected at the levels of 0.33~2.41 log CFU/$100cm^2$, 1.48~3.27 log CFU/Hand and 0.67~3.65 log CFU/g in equipments, hands, and plants and Staphylococcus aureus were detected in cleaner, table, hand and harvested crops (before washing and after sorting) by qualitative test. Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella spp. were not detected. These results indicated that personal hygiene and processing equipments should be managed to reduce the microbial contamination of A. membranaceus Bunge. Therefore, management system such as good agricultural practices (GAP) criteria is needed for hygienic agricultural products.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.556-560
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• 2002

In this paper, we propose a remote controller for robot manipulator using local area network(LAN) and internet. To do this, we develope a server-client system as used in the network field. The client system is in any computer in remote place for the user to log-in the server and manage the remote factory. the server system is a computer which controls the manipulator and waits for a access from client. The server system consists of several control algorithms which is needed to drive the manipulator and networking system to transfer images that shows states of the work place, and to receive a Tmp data to run the manipulator The client system consists of 3D(dimension) graphic user interface for teaching and off-line task like simulation, external hardware interface which makes it easier for the user to teach. Using this server-client system, the user who is on remote place can edit the work schedule of manipulator, then run the machine after it is transferred and monitor the results of the task.

• Journal of Information Management
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• v.42 no.2
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• pp.193-210
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• 2011

The study was applied to context window sizes and weighting method to obtain the best performance of word sense disambiguation using support vector machine. The context window sizes were used to a 3-word, sentence, 50-bytes, and document window around the targeted word. The weighting methods were used to Binary, Term Frequency(TF), TF ${\times}$ Inverse Document Frequency(IDF), and Log TF ${\times}$ IDF. As a result, the performance of 50-bytes in the context window size was best. The Binary weighting method showed the best performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.321-330
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• 2019

Prevention of corporate confidentiality leakage by insiders in enterprises is an essential task for the survival of enterprises. In order to prevent information leakage by insiders, companies have adopted security solutions, but there is a limit to effectively detect abnormal behavior of insiders with access privileges. In this study, we use the Unsupervised Learning algorithm of the machine learning technique to effectively and efficiently cluster the normal and abnormal access logs of the worker's work screen in the manufacturing information system, which includes the company's product manufacturing history and quality information. We propose an optimal feature selection model for anomaly detection by studying clustering methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.685-703
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• 2024

Cloud computing provides each consumer with a large-scale computing tool. Different Cyber Attacks can potentially target cloud computing systems, as most cloud computing systems offer services to many people who are not known to be trustworthy. Therefore, to protect that Virtual Machine from threats, a cloud computing system must incorporate some security monitoring framework. There is a tradeoff between the security level of the security system and the performance of the system in this scenario. If strong security is needed, then the service of stronger security using more rules or patterns is provided, since it needs much more computing resources. A new way of security system is introduced in this work in cloud environments to the VM on account of resources allocated to customers are ease. The main spike of Fog computing is part of the cloud server's work in the ongoing study tells the step-by-step cloud server to change the tremendous measurement of information because the endeavor apps are relocated to the cloud to keep the framework cost. The cloud server is devouring and changing a huge measure of information step by step to reduce complications. The Medical Data Health-Care (MDHC) records are stored in Cloud datacenters and Fog layer based on the guard intensity and the key is provoked for ingress the file. The monitoring center sustains the Activity Log, Risk Table, and Health Records. Cloud computing and Fog computing were combined in this paper to review data movement and safe information about MDHC.

• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.388-393
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• 2023

The analysis of engineering data using neural network based on supervised learning has been utilized in various engineering fields such as optimization of chemical engineering process, concentration prediction of particulate matter pollution, prediction of thermodynamic phase equilibria, and prediction of physical properties for transport phenomena system. The supervised learning requires training data, and the performance of the supervised learning is affected by the composition and the configurations of the given training data. Among the frequently observed engineering data, the data is given in log-scale such as length of DNA, concentration of analytes, etc. In this study, for widely distributed log-scaled training data of virtual 100×100 images, available loss functions were quantitatively evaluated in terms of (i) confusion matrix, (ii) maximum relative error and (iii) mean relative error. As a result, the loss functions of mean-absolute-percentage-error and mean-squared-logarithmic-error were the optimal functions for the log-scaled training data. Furthermore, we figured out that uniformly selected training data lead to the best prediction performance. The optimal loss functions and method for how to compose training data studied in this work would be applied to engineering problems such as evaluating DNA length, analyzing biomolecules, predicting concentration of colloidal suspension.