• Journal of Soil and Groundwater Environment
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• v.13 no.4
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• pp.40-53
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• 2008

Groundwater pollution prediction methods have been developed to plan the sustainable groundwater usage and protection from potential pollution in many countries. DRASTIC established by US EPA is the most widely used groundwater vulnerability mapping method. However, the DRASTIC showed limitation in predicting the groundwater contamination because the DRASTIC method is designed to embrace only hydrogeologic factors. Therefore, in this study, three different methods were applied to improve a groundwater pollution prediction method: US EPA DRASTIC, Modified-DRASTIC suggested by Panagopoulos et al. (2006), and LSDG (Land use, Soil drainage, Depth to water, Geology) proposed by Rupert (1999). The Modified-DRASTIC is the modified version of the DRASTIC in terms of the rating scales and the weighting coefficients. The rating scales of each factor were calculated by the statistical comparison of nitrate concentrations in each class using the Wilcoxon rank-sum test; while the weighting coefficients were modified by the statistical correlation of each parameter to nitrate concentrations using the Spearman's rho test. The LSDG is a simple rating method using four factors such as Land use, Soil drainage, Depth to water, and Geology. Classes in each factor are compared by the Wilcoxon rank-sum test which gives a different rating to each class if the nitrate concentration in the class is significantly different. A database of nitrate concentrations in groundwaters from 149 wells was built in Keumsan area. Application of three different methods for assessing the groundwater pollution potential resulted that the prediction which was represented by a correlation (r) between each index and nitrate was improved from the EPA DRASTIC (r = 0.058) to the modified rating (r = 0.245), to the modified rating and weights (r = 0.400), and to the LSDG (r = 0.415), respectively. The LSDG seemed appropriate to predict the groundwater pollution in that it contained land use as a factor of the groundwater pollution sources and the rating of each class was defined by a real pollution nitrate concentration.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.185-205
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• 2023

As the tourism industry recovers post the COVID-19 pandemic, an increasing number of tourists are utilizing various platforms to leave reviews. However, amidst the vast amount of data, finding useful information remains challenging, often leading to time and cost inefficiencies in selecting travel destinations. Despite ongoing research, there are limitations due to the absence of ratings or the presence of different rating formats across platforms. Moreover, inconsistencies between ratings and the content of reviews pose challenges in developing recommendation models. To address these issues, this study utilized 7,104 reviews of tourist spots in Jeju Island to develop a specialized satisfaction index for Jeju tourist attractions and employed this index to construct a 'Rating Prediction Model.' To validate the model's performance, we predicted the ratings of 700 experimental data points using both the developed model and an LSTM approach. The proposed model demonstrated superior performance with a weighted accuracy of 73.87%, which is approximately 4.67% higher than that of the LSTM. The results of this study are expected to resolve the discrepancies between ratings and review contents, standardize ratings in reviews without ratings or in various formats, and provide reliable rating indicators applicable across all areas of travel in different domains.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.43-58
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• 2011

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.273-290
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• 2006

This study suggests a frequency matrix technique to predict personal credit rate more efficiently using incomplete data sets. At first this study test on multiple discriminant analysis and logistic regression analysis for predicting personal credit rate with incomplete data sets. Missing values are predicted with mean imputation method and regression imputation method here. An artificial neural network and frequency matrix technique are also tested on their performance in predicting personal credit rating. A data set of 8,234 customers in 2004 on personal credit information of Bank A are collected for the test. The performance of frequency matrix technique is compared with that of other methods. The results from the experiments show that the performance of frequency matrix technique is superior to that of all other models such as MDA-mean, Logit-mean, MDA-regression, Logit-regression, and artificial neural networks.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.185-194
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• 2010

In recommender systems which are used widely at e-commerce, collaborative filtering needs the information of user-ratings and neighbor user-ratings. These are an important value for recommendation in recommender systems. We investigate the in-formation of rating in NBCFA (neighbor Based Collaborative Filtering Algorithm), we suggest new algorithm that improve prediction accuracy of recommender system. After we analyze relations between two variable and Error Value (EV), we suggest new algorithm and apply it to fitted line. This fitted line uses Least Squares Method (LSM) in Exploratory Data Analysis (EDA). To compute the prediction value of new algorithm, the fitted line is applied to experimental data with fitted function. In order to confirm prediction accuracy of new algorithm, we applied new algorithm to increased sparsity data and total data. As a result of study, the prediction accuracy of recommender system in the new algorithm was more improved than current algorithm.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.183-190
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• 2021

In this paper, we implement a system for a fund recommendation based on the investment propensity and for a future fund price prediction. The investment propensity is classified by scoring user responses to series of questions. The proposed system recommends the funds with a suitable risk rating to the investment propensity of the user. The future fund prices are predicted by Prophet model which is one of the machine learning methods for time series data prediction. Prophet model predicts future fund prices by learning the parameters related to trend changes. The prediction by Prophet model is simple and fast because the temporal dependency for predicting the time-series data can be removed. We implement web pages for the fund recommendation and for the future fund price prediction.

• Journal of the Korean Society of Safety
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• v.18 no.1
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• pp.108-115
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• 2003

Bridges are rated at two levels by either Load Factor Design (LFD) or Allowable Stress Design (ASD). The lower level rating is called Inventory Rating and the upper level rating is called Operating Rating. To maintain bridges effectively, there is an urgent need to assess actual bridge loading carrying capacity and to predict their remaining life from a system reliability viewpoint. The lifetime functions are introduced and explained to predict the time-dependent failure probability. The bridge studied in this paper was built 30 years ago in rural area. For this bridge, the load test and rehabilitation were conducted. The time-dependent system failure probability is predicted with or without rehabilitation. As a case study, an optional rehabilitation is suggested, and fir this rehabilitation, load rating is computed and the time-dependent system failure probability is predicted. Based on rehabilitation costs and extended service lifes, the optimal rehabilitation is suggested.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.462-472
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• 2021

With an increase in the scale of recommender systems, users' rating data tend to be extremely sparse. Some methods have been utilized to alleviate this problem; nevertheless, it has not been satisfactorily solved yet. Therefore, we propose an effective pre-rating method based on users' dichotomous preferences and average ratings fusion. First, based on a user-item ratings matrix, a new user-item preference matrix was constructed to analyze and model user preferences. The items were then divided into two categories based on a parameterized dynamic threshold. The missing ratings for items that the user was not interested in were directly filled with the lowest user rating; otherwise, fusion ratings were utilized to fill the missing ratings. Further, an optimized parameter λ was introduced to adjust their weights. Finally, we verified our method on a standard dataset. The experimental results show that our method can effectively reduce the prediction error and improve the recommendation quality. As for its application, our method is effective, but not complicated.

• The Journal of Industrial Distribution & Business
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• v.13 no.1
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• pp.23-32
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• 2022

Purpose: This study used ESG grade, but defined AESG, adjusted to the size of a company and examines whether it can be used as an investment strategy. Research design, data and methodology: The analysis sample in this study is a company that has given an ESG rating among companies listed on the Korea Stock Exchange. We examine the results through portfolio analysis and Fama-macbeth regression analysis. Results: As result of examining the long-only performance and the long-short performance by constructing quintile portfolios, it was observed that a significant positive return was shown. It was observed that there was an alpha that could not be explained in asset pricing models. Also, AESG had a return prediction effect in the result of a Fama-Macbeth regression that controlled corporate characteristic variables in individual stocks. Next, we confirmed AESG's usage through various portfolio composition. In the portfolio optimization, the Risk Efficient method was the most superior in terms of sharpe ratio and the construct multi-factor model with Value, Momentum and Low Vol showed statistically significant performance improvement. Conclusions: The results of this study suggest that it can be helpful in ESG investment to reflect the ESG rating of relatively small companies more through the scale adjustment of the ESG rating (i.e.AESG).

• Proceedings of the KSR Conference
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• 2003.10b
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• pp.538-543
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• 2003

This study is the developed prediction of residual effective prestress force of prestressed concrete beam bridges. Developed prediction method is based on the center camber of prestressed concrete beam, structural design. report of various PSC beams, construction reference materials of PSC beams. Evaluation of residual effective prestress force by developed method is compared with evaluation by structural design. This comparison results shows that this developed method is very effective method. Therefore prediction of residual effective prestress force by this developed method will be used for evaluation of the rating of various PSC beam bridges(road bridges and railway bridges).