• The Journal of Fisheries Business Administration
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• v.45 no.3
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• pp.71-84
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• 2014

This study indicates that 84.5% of fishermen have perceived climate change and 74.9% of fishermen have responded that frequency and intensity of the impacts of climate change are increasing. The results of regression analysis have shown that the level of fishermen experiencing the impacts of climate change differs according to individual's characteristics including age, length of experience, sea area (fishing area) and types of fisheries. About half of the respondents have shown that they are not taking any actions against the effects of climate change. The main reasons are that they either have lack of knowledge on how to respond to the impacts of climate change or have the perception that climate change is irresistible. The majority of respondents have responded that they are not aware of the government's climate change policy and emphasized that it is necessary to have effective countermeasures strengthening the provision of information about climate change policy. The result of perception survey have highlighted that it is essential for the government and the fishermen to share relevant information and to consider method of cooperation.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• Journal of Korean Society of Rural Planning
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• v.20 no.2
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• pp.1-10
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• 2014

Smartphones change the picture of data and information sharing and make it possible to share various real-time flooding data and information. The vulnerability indicators of farmland inundation is needed to calculate the risk of farmland flood based on changeable hydro-meteorological data over time with morphologic characteristics of flood-damaged areas. To find related variables show the vulnerability of farmland inundation using the binary-logit model and correlation analysis and to provide vulnerability indicators were estimated by fuzzy set method. The outputs of vulnerability indicators were compared with the results of Monte Carlo simulation (MCS) for verification. From the result vulnerability indicators are applicable to mobile_based information system of farmland inundation.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.199-202
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• 1996

Although numerous studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective to solve a specific problem. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the prediction performance. This paper proposes the post-model integration method, which means integration is performed after individual techniques produce their own outputs, by finding the best combination of the results of each method. To get the optimal or near optimal combination of different prediction techniques. Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an objective function subject to numerous hard and soft constraints. This study applied three individual classification techniques (Discriminant analysis, Logit and Neural Networks) as base models to the corporate failure prediction context. Results of composite prediction were compared to the individual models. Preliminary results suggests that the use of integrated methods will offer improved performance in business classification problems.

• Korean Management Science Review
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• v.25 no.1
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• pp.123-138
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• 2008

Recently, DMB service markets experience a rapid change with terrestrial DMB test-broadcasting for the nation-wide coverage and paid interactive data broadcasting being offered utilizing TPEG and BIFS technologies. This warrants a reexamination of a consumers' adoption intentions for DMB service. This paper uses a survey data set to analyze DMB adoption intentions and the choice between terrestrial DMB and satellite DMB services according to preferred contents and other media usage characteristics. Empirical results show that consumer who prefer TV, music, and movie contents are more likely to adopt DMB service, whereas consumers with high intentions for HSDPA subscription are less likely to adopt DMB service. This implies that continuing development of killer application and the analysis of substitutes or complements of other media are crucial for the increase of DMB adoption intentions. It is found that the more consumers prefer sports, movies and entertainment/game and put higher values in the quality of the contents, the more likely they adopt satellite DMB service. Meanwhile, the more consumers prefer TV, drama and news contents, and are sensitive to the subscription fees, they are more likely to adopt terrestrial DMB service. Therefore, it seem that consumers' DMB adoption between terrestrial and satellite services is crucially related with types and characteristics of contents offered.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.83-93
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• 2001

Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.

• Health Policy and Management
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• v.20 no.1
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• pp.87-102
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• 2010

The objective of this study were to examine the impact of medicaid coverage expansion policy aimed at improving access to primary care. The case-control study was conducted to compare preventable hospitalization(PH) rate in new medicaid recipients versus national health insurance(NHI) enrollees form 1996 to 2001. Rates of preventable hospitalization associated with ambulatory care sensitive conditions(ACSC) were calculated and standardized by age and sex. Multinomial logit regression model was used to control the confounding factors such as age, gender and charlson comorbidity index Annual PH rates in the new medicaid increased 1.64 times after medicaid expansion, with controling confounding factors. Meanwhile, annual PH rate in the NHI increased 1.68 times during the same period, with adjusting confounding factors. Current findings suggest that the new medicaid PH rate was less likely to rise than NHI PH rate after implementing medicaid expansion. This study is expected to provide policy-relevant evidence of medicaid expansion to include population with low income.

• Journal of Distribution Science
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• v.15 no.5
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• pp.29-38
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• 2017

Purpose - Prior theories predict a negative correlation between stock liquidity and dividend payout propensity. We test this hypothesis by examining the sample Korean retail firms. Research design, data, and methodology - We construct four different types of stock liquidity measures and investigate how these stock liquidity variables affect dividend payout propensity by employing the logit regression model. The retail firms listed in the KOSPI and KOSDAQ markets are analyzed from 1990 to 2015. Results - Our estimation results support the liquidity hypothesis if we adopt the stock turnover rate as the stock liquidity measure, particularly for the retail firms listed in the KOSPI markets and for non-conglomerate firms. Yet, our estimation results adopting the illiquidity measure of Amihud (2002), the proportion of non-trading day, and the volume of trading do not support the liquidity hypothesis. Conclusions - Our findings provide mixed results for the validity of stock liquidity hypothesis, which enriches the existing literature. In terms of turnover rate, the stock liquidity hypothesis holds robustly. Yet, we are not able to find any empirical evidence supporting the hypothesis if we use the other three measures of stock liquidity.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup1
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• pp.55-63
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• 2016

Smoking and harmful drinking dramatically increase health risks but little is known about their cooccurrence and factors that influence this co-habit, limiting development and implementation of appropriately targeted prevention interventions. This study was conducted among youth aged 10-24 years old in the Chi Linh Demographic - Epidemiological Surveillance System (CHILILAB DESS). The total numbers in the first, second and third rounds in 2006, 2009 and 2013 were 12,406, 10,211, and 7,654, respectively. A random-effects logit model controlling for both time-variant and time-invariant variables was applied to explore factors associated with current smoking, harmful drinking, and occurrence of smoking and harmful drinking together. We found dramatically increasing trends in current smoking, harmful drinking and co-occurrence among youth. Our results indicate similar health problems among youth in peri-urban areas in Vietnam. Demographic characteristics (older age, being male, being unmarried, and having informal work) appeared to be predictors for smoking and drinking behaviour. Besides, peer and family members had significant influence on smoking, whereas having a close-friend who was smoking was the most important variable. The results suggested that smoking and harmful drinking should not be solved with separate, stand-alone interventions but rather with integrated efforts.

• Journal of Forest and Environmental Science
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• v.36 no.1
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• pp.7-16
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• 2020

Tree size distribution modelling is an integral part of forest management. Most distribution yield systems rely on some flexible probability models. In this study, a simple finite mixture of two components two-parameter Weibull distribution was compared with complex four-parameter distributions in terms of their fitness to predict tree size distribution of teak (Tectona grandis Linn f) plantations. Also, a system of equation was developed using Seemingly Unrelated Regression wherein the size distributions of the stand were predicted. Generalized beta, Johnson's SB, Logit-Logistic and generalized Weibull distributions were the four-parameter distributions considered. The Kolmogorov-Smirnov test and negative log-likelihood value were used to assess the distributions. The results show that the simple finite mixture outperformed the four-parameter distributions especially in stands that are bimodal and heavily skewed. Twelve models were developed in the system of equation-one for predicting mean diameter, seven for predicting percentiles and four for predicting the parameters of the finite mixture distribution. Predictions from the system of equation are reasonable and compare well with observed distributions of the stand. This simplified mixture would allow for wider application in distribution modelling and can also be integrated as component model in stand density management diagram.