• Korean Journal of Clinical Laboratory Science
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• v.51 no.3
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• pp.351-359
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• 2019

Neurocognitive testing commonly uses the MMSE (Mini-Mental State Examination) to evaluate the overall cognitive function of patients at outpatient clinics, but the MMSE has recently been extensively used in the SNSB II (Seoul Neuropsychological Screening Battery II) for making diagnoses. We retrospectively investigated the results of routine neurocognitive tests and the results of the blood tests of 120 elderly patients who had been referred to a South Central Medical Center from 2017 to 2018 and who had been examined at a public health center. These subjects' space-time capability was high on the sub-region of the global deterioration scale (GDS). GDS showed a significant increase as the Na decreased on the electrolyte analysis. The subjects' concentration, their language-based orientation for space and time, their memory, and their scores for the frontal lobe function on GDS showed statistically significant reductions (P<0.001) For the normal and abnormal groups according to the ALT and creatinine levels, the frontal/execute function areas showed statistically significant differences (P<0.001) as well as negative correlation between GDS and ALT (P<0.01). In conclusion, this study provides basic information to develop test items that are important for patient screening and diagnosis, and several routine blood chemistry factors provide basic information for diagnosing and assessing the status and progress of cognitively impaired patients.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.1
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• pp.381-385
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• 2015

Background:Hepatocellular carcinoma (HCC) is the second most common malignancy in Egypt. Data mining is a method of predictive analysis which can explore tremendous volumes of information to discover hidden patterns and relationships. Our aim here was to develop a non-invasive algorithm for prediction of HCC. Such an algorithm should be economical, reliable, easy to apply and acceptable by domain experts. Methods: This cross-sectional study enrolled 315 patients with hepatitis C virus (HCV) related chronic liver disease (CLD); 135 HCC, 116 cirrhotic patients without HCC and 64 patients with chronic hepatitis C. Using data mining analysis, we constructed a decision tree learning algorithm to predict HCC. Results: The decision tree algorithm was able to predict HCC with recall (sensitivity) of 83.5% and precession (specificity) of 83.3% using only routine data. The correctly classified instances were 259 (82.2%), and the incorrectly classified instances were 56 (17.8%). Out of 29 attributes, serum alpha fetoprotein (AFP), with an optimal cutoff value of ${\geq}50.3ng/ml$ was selected as the best predictor of HCC. To a lesser extent, male sex, presence of cirrhosis, AST>64U/L, and ascites were variables associated with HCC. Conclusion: Data mining analysis allows discovery of hidden patterns and enables the development of models to predict HCC, utilizing routine data as an alternative to CT and liver biopsy. This study has highlighted a new cutoff for AFP (${\geq}50.3ng/ml$). Presence of a score of >2 risk variables (out of 5) can successfully predict HCC with a sensitivity of 96% and specificity of 82%.

• Korean journal of communication and information
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• v.55
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• pp.164-183
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• 2011

Technology innovation of digital convergence in recent years of the media sector has produced a series of significant changes in journalist labor. This study analyzes how recent introduction of convergent newsroom changed the nature of journalist labor and what strategy the management used to control journalists within the technologically innovated working condition with case of CBS. As the labor process theory tells us, the analysis found that technological innovation in the newsroom has encouraged a couple of aspects regarding labor process. First, losing control over their own labor journalists have undergone the process of significant deskilling. Second, the management have made a constant effort to introduce ideological and political apparatuses with twofold purposes, effective control over workers on one hand and concealing oppressive labor conditions on the other. The effort generated journalists' acceptance of new news-making routine and their consent on labor-management culture founded upon naive familism, which at last resulted in reinforcement of corporate power and isolation of labor society by separating internal labor market.

• Journal of the Society of Disaster Information
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• v.9 no.1
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• pp.1-10
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• 2013

The main objective of this research is to develop the Mechanistic-Empirical (M-E) guidelines for evaluating the capacity of existing highways to sustain route overweight truck traffic over a specified performance period due to a growing concern on the impact of increasing overweight truck loads on highways. In this study, a two-stage framework was developed for this purpose. Level I procedure involves the use of pavement evaluation charts to identify the best possible route from among the alternatives considered and to determine what additional tests and analyses are needed as a screening tool. Level II involves the application of the Overweight Truck Route Analysis (OTRA) program to evaluate the structural adequacy of an existing route to carry routine overweight truck traffic over the specified performance period along with estimating asphalt concrete overlay thickness, if necessary.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.4
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• pp.29-39
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• 2009

In order to respond against worms which are malicious programs automatically spreading across communication networks, worm detection approach by generating signatures resulting from analyzing worm-related packets is being mostly used. However, to avoid such signature-based detection techniques, usage of exploits employing mutated polymorphic types are becoming more prevalent. In this paper, we propose a novel static analysis approach for detecting the decryption routine of polymorphic exploit code, Our approach detects a code routine for performing the decryption of the encrypted original code which are contained with the polymorphic exploit code within the network flows. The experiment results show that our approach can detect polymorphic exploit codes in which the static analysis resistant techniques are used. It is also revealed that our approach is more efficient than the emulation-based approach in the processing performance.

• The Journal of Information Systems
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• v.30 no.2
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• pp.29-56
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• 2021

Purpose Cyber deviance of adolescents has become a serious social problem. With a widespread use of smartphones, incidents of cyber deviance have increased in Korea and both quantitative and qualitative damages such as suicide and depression are increasing. Research has been conducted to understand diverse factors that explain adolescents' delinquency in cyber space. However, most previous studies have focused on a single theory or perspective. Therefore, this study aims to comprehensively analyze motivations of juvenile cyber deviance and to develop a predictive model for delinquent adolescents by integrating four different theories on cyber deviance. Design/methodology/approach By using data from Korean Children & Youth Panel Survey 2010, this study extracts 27 potential factors for cyber deivance based on four background theories including general strain, social learning, social bonding, and routine activity theories. Then this study employs econometric analysis to empirically assess the impact of potential factors and utilizes a machine learning approach to predict the likelihood of cyber deviance by adolescents. Findings This study found that general strain factors as well as social learning factors have positive effects on cyber deviance. Routine activity-related factors such as real-life delinquent behaviors and online activities also positively influence the likelihood of cyber diviance. On the other hand, social bonding factors such as community commitment and attachment to community lessen the likelihood of cyber deviance while social factors related to school activities are found to have positive impacts on cyber deviance. This study also found a predictive model using a deep learning algorithm indicates the highest prediction performance. This study contributes to the prevention of cyber deviance of teenagers in practice by understanding motivations for adolescents' delinquency and predicting potential cyber deviants.

• Information Systems Review
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• v.18 no.4
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• pp.173-187
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• 2016

This study on the social effects of Internet of Things (IoTs) provides an overview of future job prospects through the scenario planning approach, highlighting the challenges and opportunities that IoTs will bring in the future. IoTs and the related field of technological innovations have become increasingly important in both academic and business communities in the past few years because of computing power breakthrough and its price drop. IoTs enables people to deal with routine works efficiently and challenges them even in non-routine and/or cognitive tasks, which are considered a unique area for individuals. The scenario planning analysis helps us to define the uncertain boundary and to estimate the potential opportunities and inherent threats to provide decision makers with a mind map on how the development of IoTs can influence employment. To assess the potential effects on jobs described in our scenarios, we briefly examine the local structure of employment and discuss which careers are expected to decline or grow in particular among the 52 standard occupational classifications in Korea.

• Language and Information
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• v.7 no.1
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• pp.23-40
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• 2003

In this paper, we have proposed Auto-TUS, an automatic transcription module of three union symbols-hyphen, dash and tilde (‘­’, ‘―’, ‘∼’)-using their linguistic contexts. Few previous studies have discussed the problems of ambiguities in transcribing symbols into Korean alphabetic letters. We have classified six different reading formulae of the union symbols, analyzed the left and right contexts of the symbols, and investigated selection rules and distributions between the symbols and their contexts. Based on these linguistic features, 86 stereotyped patterns, 78 rules and 8 heuristics determining the types of reading formulae are suggested for Auto-TUS. This module works modularly in three steps. The pilot test was conducted with three test suites, which contains respectively 418, 987 and 1,014 clusters of words containing a union symbol. Encouraging results of 97.36%, 98.48%, 96.55% accuracy were obtained for three test suites. Our next phases are to develop a guessing routine for unknown contexts of the union symbols by using statistical information; to refine the proper nouns and terminology detecting module; and to apply Auto-TUS on a larger scale.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.182-186
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• 2017

Maintaining a proper lookout activity routine is integral to preventing ship collision accidents caused by human errors. Various subjective measures such as interviewing, self-report diaries, and questionnaires have been widely used to monitor the lookout activity patterns of navigators. An objective measurement of a lookout activity pattern classification system is required to improve lookout performance evaluation in a real navigation setting. The purpose of this study was to develop an objective navigator lookout activity classification system using wearable accelerometers. In the training session, 90.4% accuracy was achieved in classifying five fundamental lookout activities. The developed model was then applied to predict real-lookout activity in the second session during an actual ship voyage. 86.9% agreement was attained between the directly observed activity and predicted activity. Based on these promising results, the proposed unobstructed wearable system is expected to objectively evaluate navigator lookout patterns to provide a better understanding of lookout performance.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.4
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• pp.151-160
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• 2009

While portable and wireless devices are pouring, a new network technology is needed as a breakthrough. The new network technology features large delays, intermittent connectivity, and absence of an end-to-end path from sources to destinations. A network which has one of those characteristics is called DTNs(Delay Tolerant Networks). The main 4 routing methods have been researched so far in extream environment. In this paper, we look into the reliability of DTMNs(Delay Tolerant Mobile Networks) in several different situations, and propose an algorithm that selects a positive routine by sending the only information of its position when making a connection to a detected node. We simulate the proposed algorithm here in DTN using ONE simulator. As a result, it shows that the algorithm reduces the number of message transmission each node.