• Journal of Information Technology Services
• /
• v.19 no.3
• /
• pp.117-137
• /
• 2020

The development of information technology leads to changes in various industries. In particular, the health care industry is more influenced so that it is focused on. With the widening of the health care market, the market of smart device based personal health care also draws attention. Since a variety of fitness applications for smartphone based exercise were introduced, more interest has been in the health care industry. But although an amount of use of mobile fitness applications increase, it fails to lead to a sustained use. It is necessary to find and understand what matters for mobile fitness application users. Therefore, this study analyze the reviews of mobile fitness application users, to draw key factors, and thereby to propose detailed strategies for promoting mobile fitness applications. We utilize text mining techniques - LDA topic modeling, term frequency analysis, and keyword extraction - to draw and analyze the issues related to mobile fitness applications. In particular, the key factors drawn by text mining techniques are explained through the concept of user experience. This study is academically meaningful in the point that the key factors of mobile fitness applications are drawn by the user experience based text mining techniques, and practically this study proposes detailed strategies for promoting mobile fitness applications in the health care area.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.1
• /
• pp.86-93
• /
• 2008

The construction of ontology defines as complicated semantic relations needs precise and expert skills. For the well defined ontology in real applications, plenty of information of instances for ontology classes is very critical. In this study, crawling algorithm which extracts the fittest topic from the Web overflowing over by a great number of documents has been focused and developed. Proposed crawling algorithm made a progress to gather documents at high speed by extracting topic-specific Link using URL patterns. And topic fitness of Link block text has been represented by fuzzy sets which will improve a precision of the focused crawler.

• KIEE International Transactions on Power Engineering
• /
• v.3A no.3
• /
• pp.155-160
• /
• 2003

Reliability assessment of power systems has been an important topic for the past several decades. It is becoming even more important nowadays as the power market moves toward a new competitive environment. This paper deals with two topics on reliability assessment. The first is how to select probability distributions and determine their parameters to model the probabilistic events in a power system. The second is how to consider restorative actions in the assessment, which directly influence reliability indices. This paper proposes simple but convincing alternative solutions on the two topics. In the case study, this paper shows the influences of the probability distributions that are used in power system modeling.

• International Commerce and Information Review
• /
• v.4 no.1
• /
• pp.107-128
• /
• 2002

In order to clarify the relationship between the Internet utilization of the Korean small export firms for export marketing and it's performance, first, a research model and hypothesis have been made on a theoretical basis. This research hypothesis has been examined through gathering survey data and a statistical analysis based on MANOVA. The conclusion, as well as the main point of this thesis, is that, in most cases, there was a significant statistical connection between the variables of export performances and Internet-utilized export marketing strategies, which are both based on the classical AIDA model of effective hierarchy of communication for sales and advertising. This conclusion brings forth a substantial topic as well as provides meaningful implications for the Korean small export firms when putting together a marketing strategy using the Internet. It is also hoped that this will come as a reference for other researchers henceforth. However, there is an issue of lack of representativeness when considering that the samples were chosen on the basis of the convenience sampling method. Another issue may be that a portion of the subjects were not related to actual trade, but were, in fact, Web masters who operated e-trade systems. Furthermore, there are also numerous problems of missing data, and 6 out of the 8 main hypotheses possessed the issue of fitness level with the data. Therefore, in order to enhance both the representative and fitness levels of the data of further research for this topic, future respondents will have to have special trade knowledge, as well as technical knowledge of e-trade. However, unless the Internet begins to greatly contribute to export marketing of corporations, this type of enterprising spirit by corporations in gathering suitable respondents will not surface. Although not used in examining the hypotheses of this particular thesis, the relationship between competitive and circumstantial variables, which were added to the upcoming survey, and export performances will soon be analyzed and announced in order to atone for some of the standards this thesis fell short of.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.4
• /
• pp.1426-1447
• /
• 2021

The payoff game between task publishers and users in the mobile crowdsensing environment is a hot topic of research. A optimal payoff selection model based on stochastic evolutionary game is proposed. Firstly, the process of payoff optimization selection is modeled as a task publisher-user stochastic evolutionary game model. Secondly, the low-quality data is identified by the data quality evaluation algorithm, which improves the fitness of perceptual task matching target users, so that task publishers and users can obtain the optimal payoff at the current moment. Finally, by solving the stability strategy and analyzing the stability of the model, the optimal payoff strategy is obtained under different intensity of random interference and different initial state. The simulation results show that, in the aspect of data quality evaluation, compared with BP detection method and SVM detection method, the accuracy of anomaly data detection of the proposed model is improved by 8.1% and 0.5% respectively, and the accuracy of data classification is improved by 59.2% and 32.2% respectively. In the aspect of the optimal payoff strategy selection, it is verified that the proposed model can reasonably select the payoff strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.9
• /
• pp.2976-2990
• /
• 2022

The adaptive adjustment of reference or weight vectors in decomposition-based methods has been a hot research topic in the evolutionary community over the past few years. Although various methods have been proposed regarding this issue, most of them aim to diversify solutions in the objective space to cover the true Pareto fronts as much as possible. Different from them, this paper proposes a knee point-based reference vector adaptive adjustment strategy to concurrently balance the convergence and diversity. To be specific, the knee point-based reference vector adaptive adjustment strategy firstly utilizes knee points to construct the adaptive reference vectors. After that, a new fitness function is defined mathematically. Then, this paper further designs a many-objective evolutionary algorithm with knee point-based reference vector adaptive adjustment strategy, where the mating operation and environmental selection are designed accordingly. The proposed method is extensively tested on the WFG test suite with 8, 10 and 12 objectives and MPDMP with state-of-the-art optimizers. Extensive experimental results demonstrate the superiority of the proposed method over state-of-the-art optimizers and the practicability of the proposed method in tackling practical many-objective optimization problems.

• Journal of Intelligence and Information Systems
• /
• v.20 no.4
• /
• pp.121-139
• /
• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Journal of Fashion Business
• /
• v.6 no.4
• /
• pp.17-31
• /
• 2002

This study is, to the special company with corporate brand, to investigate the effect of its reputation, credibility and the similarity of business area on consumer's attitude toward its clothing products. It is another topic of the study to reveal statistical significance in comsumer's attitude when naming new product with two different brand strategy. The major findings of this study can be summarized as follows. First, there was positive relationship between corporate reputation and consumer's attitude toward new brand and also between corporate credibility and consumer's that. Second, corporate credibility is proven to be grown in proportion to company's reputation. In other words, a corporate credibility is grown with a corporate reputation. Third, the influence of similarity or fitness of business area has a positive effect on a attitude toward new brand. The case of use the new brand name, the variable had low effect on attitude toward new brand, comparing to the case of use the existing brand name but seems to be statistically significant. Fourth, comparing to the inclination toward company's product based on brand name, strategy of supporting brand extension is more effective than that of new brand name on evaluating company's reputation, credibility and the similarity of business area. Fifth, the similarity of business area and the interaction of brand name on the basis of the strategy of use the existing brand name(;brand extension strategy) and new one have significant effect on the inclination to a brand. When participating a new business, it is more effective that a company is extending its business where the similarity of business area is growing. For the case of low similarity, the brand-new strategy is proven to be effective.