• Tunnel and Underground Space
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• v.14 no.2
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• pp.133-141
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• 2004

Through the many site investigations, different methods for evaluating stability of rock slopes have been proposed. Those methods, however, may lead to different results depending on the subjective judgments associated with the selection of the evaluation items and the application of weighting factor. Accordingly, binary logistic regression analysis was carried out to ensure fair appliction of the weighting factor, leading to an equation for evaluating the stability of rock slopes.

• Proceedings of the KSRS Conference
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• v.2
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• pp.547-550
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• 2006

This study is to suggest a methodology to produce landslide hazard map by combining LRA (Logistic Regression Analysis) and AHP (Analytic Hierarchy Program) Approach. Topographic factors (slope, aspect, elevation), soil drain, soil depth and land use were adopted to classify landslide hazard areas. The method was applied to a 520 $km^2$ region located in the middle of South Korea which have occurred 39 landslides during 1999 and 2003. The suggested method showed 58.9 % matching rate for the real landslide sites comparing with the classified areas of high-risk landslide while LRA and AHP showed 46.1 % and 48.7 % matching rates respectively.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.1
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• pp.279-288
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• 2016

The purpose of this research is to examine the impact between the export education of SMEs in Busan Region and export possibility. 62 SMEs in Busan Region were participated in survey. For the empirical analysis, Logistic regression analysis was used with Spss12 to analyze the impact between the involvement on export education and SME's export possibility. In a result, among 3 variables (support, participancy, importance) of involvement on export education, the importance had the influence significantly on export possibility of SMEs. The results indicated that having the intention about the importance of export education is very notable, and implied that it is necessary to develop the education program emphasizing the importance of export education for SMEs' successful export.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.7-13
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• 2023

In this paper, Depression is a mental disorder characterized by a lack of enthusiasm and feelings of sadness, which significantly impairs daily functioning. In 2018, there was an increase in book sales in the essay genre, particularly the popularity of "healing essays." This trend is seen as challenging the negative image and prejudices associated with depression. In 2021, a significant rise in the proportion of 20-year-old patients with depression is attributed to factors like job-related stress, interpersonal issues, and financial burdens. Additionally, there is a strong correlation between depression and suicidal thoughts, particularly among individuals who have experienced feelings of depression. Despite the increasing prevalence of depression among young adults, research in this area is lacking. To address this gap, statistical tools such as logistic regression and chi-squared tests are employed. The analysis reveals various independent variables associated with feelings of depression, shedding light on the relationships between these factors.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.303-309
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• 1995

Many measures to detect multicollinearity in linear regression have been proposed in statistics and numerical analysis literature. Among them, condition number and variance inflation factor(VIF) are most popular. In this study, we give new interpretations of condition number and VIF in linear regression, using geometry on the explanatory space. In the same line, we derive natural measures of condition number and VIF for logistic regression. These computer intensive measures can be easily extended to evaluate multicollinearity in generalized linear models.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.9-20
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• 2013

OSHA(Occupational Safety and Health Act) generally regulates employer's business principles in the workplace to maintain safety environment. This act has the fundamental purpose to protect employee's safety and health in the workplace by reducing industrial accidents. Authors tried to investigate the correlation between 'occupational injuries and illnesses' and level of regulation compliance using Survey on Current Status of Occupational Safety & Health data by the various statistical methods, such as generalized regression analysis, logistic regression analysis and poison regression analysis in order to compare the results of those methods. The results have shown that the significant affecting compliance factors were different among those statistical methods. This means that specific interpretation should be considered based on each statistical method. In the future, relevant statistical technique will be developed considering the distribution type of occupational injuries.

• Convergence Security Journal
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• v.11 no.4
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• pp.85-91
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• 2011

Due to the large-scale application software syslmls, software reliability, software development has animportantrole. In this paper, software truncated software reliability growth model was proposed based on log-logistic distribution. According to fixed time, the intensity function, the mean value function, the reliability was estimated and the parameter estimation used to maximum likelihood. In the empirical analysis, Poisson execution time model of the existiog model in this area and the log-logistic model were compared Because log-logistic model is more efficient in tems of reliability, in this area, the log-logistic model as an alternative 1D the existiog model also were able to confim that you can use.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.1
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• pp.127-143
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• 2016

This study applied DEA analysis to measure the relative efficiency of 35 companies that produce automobile body components. First, the input and output, the improvement target value of the calculated variables, and the reference group for benchmarking for inefficient groups to become efficient groups were established through DEA analysis. In addition, whether inefficiency was due to technical inefficiency or size was analyzed in connection with the cases of the actual companies through the measurement of scale efficiency. Second, a route for efficiency improvement was derived through DEA-Tier analysis by defining the possible group for benchmarking in actuality within the production industry of automobile body components where the primary cooperative company belonged. Third, the financial variables that generate the difference between efficient and inefficient groups were derived through logistic regression analysis. Financial strategies that determine the direction the indices should be improved to allow the inefficient group to become an efficient one were recommended. This research is expected to provide diagnostic methods for management efficiency and the direction of improvement to enhance the management efficiency of automotive parts makers by identifying the causes of the inefficiency of domestic automotive parts makers empirically. The study also provides financial strategies together with the target values of efficiency improvement for each individual company.