• Journal of Information Technology Services
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• v.11 no.4
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• pp.233-250
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• 2012

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.38-44
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• 2013

Smart phones have brought rapid changes in this competitive world. Smart phone application developers are trying their best to consider the customer requirements in the most efficient way while considering all its attributes. However smart phone service quality has been given less consideration comparatively during the last few years. This paper proposes a measurement method for improving service quality of smart phone application. This method combines the service quality performance model (SQPM) and process capability index (PCI). The service quality performance model is used to identify service items that require improvement. Process capability index is used as a measure for prioritization of those improvements. Case study was carried out to search out important communication application service attributes. customer satisfaction level data was collected for users who used the application service. A total of twenty four service attributes were found during this survey. Using the joint approach of SQPM and PCI, five significant service attributes were prioritized for service quality improvement.

• Hwankyungkyoyuk
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• v.21 no.4
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• pp.94-110
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• 2008

Recently environmental problem becomes such a big issue all over the world that the necessity and importance of the environmental education in school has been simultaneously emphasized. While diverse methods for the environmental education have been researched, this paper, especially focused on a teaching-learning model called GBS (Goal-based scenario), aims to provide a new learner-centered approach for the environmental education. For this purpose, this paper first briefly presents two theoretical backgrounds of GBS (i.e., constructivism and Schank's dynamic memory theory), which is followed by specific and concrete strategies and methods of how to apply GBS in class for the teacher. GBS(Goal-Based Scenario) is a learner-centered model in which learners are presented with a reality-based scenario (or task or problem) and go through several stages of 'missions' to get to a final solution of the given scenario. GBS, while completely resonant with other constructivist learning models in terms of learner-centered approaches, is distinctive from others, when it supplies more specific, structured guides of learning, called 'missions', to the students throughout the whole learning process. In a words, GBS ought to be recognized as an unique learner-centered model compromising the contradictory concepts of 'learner control' and 'structure and specifics' in learning environments still without any damage of constructivist learning principles.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.69
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• pp.453-472
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• 2016

As globalization is widely expanded in Islamic world as well as huge capital like oil-money is looking for new investment areas, our government should keep eyes on the current situation of Muslim market. This study will focus on the research of operation model of Islamic insurance. It will analysis on the institutional aspects of Islamic insurance(Takaful) system, which is a step further on the basis of these previous researches about Islamic finance. Takaful is conducted with various machanism such as Wakala, Mudarabah, Waqf. The mechanism can also be intermingled with one another to form other diverse contracts. However most of them are focused on basic conceptual elements of Islamic insurance system. As public interests in Islamic insurance are increasing these days, many Islam related literatures are issued accordingly. But most of them were focused on basic aspect of Islamic financial system or on the study of business management structure. The conceptual approach to evaluate the Islamic insurance market shall become the foundation of operation in conventional business law and regulation penetrate to the Islamic business environment. Therefore, the research of the operation model in Islamic insurance system as well as the search of trade engineering basis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.109-134
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• 2018

Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two public real-world datasets show that our approach not only achieves high recommendation accuracy in relatively short runtime, but also is able to reduce the impact of malicious ratings.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.155-158
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• 2008

The needs for digital models of real environment such as 3D terrain or cyber city model are increasing. Most of applications related with modeling and simulation require virtual environment constructed from geospatial information of real world in order to guarantee reliability and accuracy of the simulation. The most fundamental data for building virtual environment, terrain elevation and orthogonal imagery is acquired from optical sensor of satellite or airplane. Providing interoperable and reusable digital model is important to promote practical application of high-resolution satellite imagery. This paper presents the new research regarding representation of geospatial information, especially for 3D shape and appearance of virtual terrain, and describe framework for constructing real-time 3D model of large terrain based on high-resolution satellite imagery. It provides infrastructure of 3D simulation with geographical context. Details of standard-based approach for providing infrastructure of real-time 3D simulation using high-resolution satellite imagery are also presented. This work would facilitate interchange and interoperability across diverse systems and be usable by governments, industry scientists and general public.

• Journal of Environmental Science International
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• v.25 no.10
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• pp.1433-1444
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• 2016

South Korea is a maritime nation, surrounded by water on three sides; hence, it is important to preserve in a sustainable manner. Most areas, especially those bordering the East Sea, have been suffering from severe coastal erosion. Information on the sediment yield of a river basin is an important requirement for water resources development and management. In Korea, data on suspended sediment yield are limited owing to a lack of logistic support for systematic sediment sampling activities. This paper presents an integrated approach to estimate the sediment yield for ungauged coastal basins by using a soil erosion model and a sediment delivery rate model in a geographic information system (GIS)-based platform. For applying the sediment yield model, a basin specific parameter was validated on the basis of field data, that, ranging from 0.6 to 1.2 for the 19 gauging stations. The calculated specific sediment yield ranged from 17 to $181t/km^2.yr$ in the various basin sizes of Korea. We obtained reasonable sediment yield values when comparing the measured data trends around the world with those in Korean basins.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.497-506
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• 2008

The needs for digital models of real environment such as 3D terrain or cyber city model are increasing. Most of applications related with modeling and simulation require virtual environment constructed from geospatial information of real world in order to guarantee reliability and accuracy of the simulation. The most fundamental data for building virtual environment, terrain elevation and orthogonal imagery is acquired from optical sensor of satellite or airplane. Providing interoperable and reusable digital model is important to promote practical application of high-resolution satellite imagery. This paper presents the new research regarding representation of geospatial information, especially for 3D shape and appearance of virtual terrain. and describe framework for constructing real-time 3D model of large terrain based on high-resolution satellite imagery. It provides infrastructure of 3D simulation with geographical context. Web architecture, XML language and open protocols to build a standard based 3D terrain are presented. Details of standard-based approach for providing infrastructure of real-time 3D simulation using high-resolution satellite imagery are also presented. This work would facilitate interchange and interoperability across diverse systems and be usable by governments, industry scientists and general public.

• Journal of Smart Tourism
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• v.1 no.1
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• pp.53-64
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• 2021

Upselling is often a critical factor in revenue generation for businesses in the tourism and travel industry. Utilizing passenger data from a major international airline company, we develop the PAX (Passenger, Airline, eXternal) model to predict passengers that are most likely to accept an upgrade offer from economy to premium. Formulating the problem as an extremely unbalanced, cost-sensitive, supervised binary classification, we predict if a customer will take an upgrade offer. We use a feature vector created from the historical data of 3 million passenger records from 2017 to 2019, in which passengers received approximately 635,000 upgrade offers worth more than $422,000,000 U.S. dollars. The model has an F1-score of 0.75, outperforming the airline's current rule-based approach. Findings have several practical applications, including identifying promising customers for upselling and minimizing the number of indiscriminate emails sent to customers. Accurately identifying the few customers who will react positively to upgrade offers is of paramount importance given the airline 'industry's razor-thin margins. Research results have significant real-world impacts because there is the potential to improve targeted upselling to customers in the airline and related industries.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.895-904
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• 2021

This study aims to investigate the impact of innovation on firm efficiency. Panel data of fourteen finance companies and nine technology companies from 2011 to 2019 on the Vietnam Stock Exchange Market is derived from audited financial statements, annual reports, and other crucial reports that are provided by Vietstock; macroeconomic variables are collected from the World Bank Database. A two-stage approach is used. First, use of the Data Envelopment Analysis methodology to measure firm efficiency. Second, use of the Pooled ordinary least squares, the Fixed effects model, and the Random effects model to investigate the impact of innovation on firm efficiency. Furthermore, the Generalized Method of Moments and the Tobit model are used to validate the impact of innovation on firm efficiency, and the t-test is used to confirm the difference in efficiency with and without the impact of innovation between two industries. The results show that there is a significant impact of innovation on efficiency, and innovation plays a more important in increasing the efficiency of the finance industry than the technology industry. Moreover, the relation between age and efficiency is like the U-shaped, and between size and efficiency is like the inverted U-shaped, whereas efficiency is not associated with inflation.