• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.187-199
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• 2022

The purpose of this study was to offer a new conceptual framework based on a combination of the TPB model, the TAM model, and two additional constructs consisting of eWOM and pricing value called the E-P-TAM-TPB model, and to assess the model's implications on hotel selection behavior. This study empirically examines the E-P-TAM-TPB model to evaluate and validate domestic tourists' online hotel booking intentions by using the partial least squares structural equation modeling (PLS-SEM) approach. The data was collected from 355 domestic tourists who booked the room via the hotel website. The major findings of this study indicated that the E-P-TAM-TPB model has a positive significant influence on online hotel selection behavior. The results revealed that all proposed hypotheses were declared supported. Future studies should build on the framework by incorporating potential moderators to better understand how different groups of customers behave online in different segments of the hospitality industry. Managers must not only develop an easy booking process but also provide price value information to attract or impress clients. Tourists can compare room rates with other hotel websites and OTAs.

• Journal of the Chungcheong Mathematical Society
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• v.27 no.2
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• pp.219-225
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• 2014

I use the dynamic programming approach to study the optimal consumption and investment problem with regime-switching and constant labor income. I derive the optimal solutions in closed-form with constant absolute risk aversion (CARA) utility and constant disutility.

• 한국ITS학회:학술대회논문집
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• 2010.05a
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• pp.157-162
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• 2010

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.259-269
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• 1997

Bayesian inference for a record value statistics(RVS) model of nonhomogeneous Poisson process is considered. We seal with Bayesian inference for double exponential, Gamma, Rayleigh, Gumble RVS models using Gibbs sampling and Metropolis algorithm and also explore Bayesian computation and model selection.

• Journal of the Chungcheong Mathematical Society
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• v.31 no.1
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• pp.65-73
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• 2018

We investigate the optimal consumption and investment problem of working agent who faces tax system on consumption, labor income, savings and investment. By applying martingale method, we obtain the closed-form solutions so it is possible to verify the effect of tax system analytically.

• Journal of Applied Biological Chemistry
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• v.43 no.4
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• pp.207-210
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• 2000

As a typical example of the screening for a microbial biocatalyst from nature, isolation of nitrilesynthesizing microorganisms, characterization of a new enzyme aldoxime dehydratase, and its function in the aldoxime-nitrile pathway are introduced. Catalytic properties of some of our enzymes were improved through a direct evolutionary approach.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.267-272
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• 2006

A procedure for selecting regressors in the linear regression model is suggested using local influence approach. Under an appropriate perturbation scheme, the effect of perturbation of regressors on the profile log-likelihood displacement is assessed for variable selection. A numerical example is provided for illustration.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.325-338
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• 2004

A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored In the form of materialized views, which represent pre-computed portions of frequently asked queries. One of the most important tasks of designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views so that the total query response time over all queries can be minimized while a limited amount of time for maintaining the views is given(maintenance-cost view selection problem). In this paper, we propose an efficient solution to the maintenance-cost view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the maintenance-cost view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in terms of time complexity over existing search-based approaches that use heuristics. Our analysis shows that the algorithm consistently yields a solution that only has an additional 10% of query cost of over the optimal query cost while at the same time exhibits an impressive performance of only a linear increase in execution time. We have implemented a prototype version of our algorithm that is used to evaluate our approach.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.325-345
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• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.

• Journal of the Korean Society for Nondestructive Testing
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• v.15 no.2
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• pp.395-406
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• 1995

Classification of flaws in weldments from their ultrasonic scattering signals is very important in quantitative nondestructive evaluation. This problem is ideally suited to a modern ultrasonic pattern recognition technique. Here brief discussion on systematic approach to this methodology is presented including ultrasonic feature extraction, feature selection and classification. A stronger emphasis is placed on probabilistic neural networks as efficient classifiers for many practical classification problems. In an example probabilistic neural networks are applied to classify flaws in weldments into 3 classes such as cracks, porosity and slag inclusions. Probabilistic nets are shown to be able to exhibit high performance of other classifiers without any training time overhead. In addition, forward selection scheme for sensitive features is addressed to enhance network performance.