• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.81-101
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• 2003

The financial statements purpose to provide useful information to decision-making process of business managers. The value-relevant information, however, embedded in the financial statement has been often overlooked in Korea. In fact, the financial statements in Korea have been utilized for nothing but account reports to Security Supervision Boards (SSB). The objective of this study is to develop earnings forecasting models through financial statement analysis using artificial intelligence (AI). AI methods are employed in forecasting earnings: artificial neural networks (ANN) for manufacturing industry and case~based reasoning (CBR) for banking industry. The experimental results using such AI methods are as follows. Using ANN for manufacturing industry records 63.2% of hit ratio for out-of-sample, which outperforms the logistic regression by around 4%. The experiment through CBR for banking industry shows 65.0% of hit ratio that beats the statistical method by 13.2% in holdout sample. Finally, the prediction results for manufacturing industry are validated through monitoring the shift in cumulative returns of portfolios based on the earning prediction. The portfolio with the firms whose earnings are predicted to increase is designated as best portfolio and the portfolio with the earnings-decreasing firms as worst portfolio. The difference between two portfolios is about 3% of cumulative abnormal return on average. Consequently, this result showed that the financial statements in Korea contain the value-relevant information that is not reflected in stock prices.

• Journal of the Korean School Mathematics Society
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• v.21 no.2
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• pp.183-206
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• 2018

It arises that teachers' professional competence should be considered not only with a cognitive perspective but also with a situative perspective. In this study, we considered mathematics teacher noticing as situational professional competencies of a mathematics teacher, and explored how mathematics teachers noticing on children's development of reasoning about variability in a video club has changed with the situative perspective. Findings illustrate that the 'interpreting' component among the three components of noticing-attending, interpreting, and deciding how to respond-was critically decisive for the change of the participant teachers' noticing. We also discussed how the video club intervention(the framework of children's development of reasoning about variability) can support the development of teacher noticing as a professional competence. This study has implications on the design of a video club to improve mathematics teacher noticing.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.1 s.5
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• pp.115-121
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• 1995

Recently, spatial integration for mineral exploration is regarded as an important task of various geological applications of GIS. Therefore, theoretical bases of data representation and reasoning concerned with Dempster-Shafer theory and Fuzzy theory were systematically as the data-driven integration methodologies for raster-based geoinformation; they are distinguished from target-driven methodology based on statistical background. According to previous actual applications of these methods to mineral exploration, they have been proven to provide useful information related to hidden target mineral deposits, and it is thought that some suggestions in this study are helpful to further real applications including representation, reasoning, and interpretation stages in order to obtain a decision-supporting layer.

• Computers and Concrete
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• v.15 no.1
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• pp.89-101
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• 2015

In this paper, Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) models were discussed to determine the compressive strength of clinker mortars cured for 1, 2, 7 and 28 days. In the experimental stage, 1288 mortar samples were produced from 322 different clinker specimens and compressive strength tests were performed on these samples. Chemical properties of the clinker samples were also determined. In the modeling stage, these experimental results were used to construct the models. In the models tricalcium silicate ($C_3S$), dicalcium silicate ($C_2S$), tricalcium aluminate ($C_3A$), tetracalcium alumina ferrite ($C_4AF$), blaine values, specific gravity and age of samples were used as inputs and the compressive strength of clinker samples was used as output. The approximate reasoning ability of the models compared using some statistical parameters. As a result, ANN has shown satisfying relation with experimental results and suggests an alternative approach to evaluate compressive strength estimation of clinker mortars using related inputs. Furthermore MLR model showed a poor ability to predict.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.338-346
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• 2003

Information Technology (IT) and the internet have been major drivers the changes in all aspects of the business processes and activities. They have brought major changes to the financial statements audit environment as well, which in turn has required modifications in audit procedures. There exist, however, certain difficulties with current audit procedures especially for the assessment of the level of control risk. This assessment is primarily based on the auditors' professional judgment and experiences, not based on the objective hies or criteria. To overcome these difficulties, this paper proposes a prototype decision support model named CRAS-CBR using case based reasoning (CBR) to support auditors in making their professional judgment on the assessment of the level of control risk of the general accounting system in the manufacturing industry. To validate the performance, we compare our proposed model with benchmark performances in terms of classification accuracy for the level of control risk. Our experimental results showed CRAS-CBR outperforms a statistical model (MDA) and staff auditor performance in average hit ratio.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.159-166
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• 2019

The collection and manipulation of Internet of Things (IoT) data is increasing at a fast pace and its importance is recognized in every sector of our society. For efficient utilization of IoT data, the vast and varied IoT data needs to be reliable and meaningful. In this paper, we propose an IoT framework to realize this need. The IoT framework is based on a four layer IoT architecture onto which context aware computing technology is applied. If the collected IoT data is unreliable it cannot be used for its intended purpose and the whole service using the data must be abandoned. In this paper, we include techniques to remove uncertainty in the early stage of IoT data capture and collection resulting in reliable data. Since the data coming out of the various IoT devices have different formats, it is important to convert them into a standard format before further processing, We propose the RDF format to be the standard format for all IoT data. In addition, it is not feasible to process all captured Iot data from the sensor devices. In order to decide which data to process and understand, we propose to use contexts and reasoning based on these contexts. For reasoning, we propose to use standard AI and statistical techniques. We also propose an experiment environment that can be used to develop an IoT application to realize the IoT framework.

• Journal of Educational Research in Mathematics
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• v.22 no.1
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• pp.101-115
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• 2012

Analogy, a plausible reasoning on the basis of similarity, is one of the thinking strategy for concept formation, problem solving, and new discovery in many disciplines. Statistics educators argue that analogy can be used as an useful thinking strategy in statistics as well. This study investigated the characteristics of students' analogical thinking in statistics. The mathematically gifted were asked to construct similar problems to a base problem which is a statistical problem having a statistical context. From the analysis of the problems, students' new problems were classified into five types on the basis of the preservation of the statistical context and that of the basic structure of the base problem. From the result, researchers provide some implications. In statistics, the problems, which failed to preserve the statistical context of base problem, have no meaning in statistics. However, the problems which preserved the statistical context can give possibilities for reconceptualization of the statistical concept even though the basic structure of the problem were changed.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of the Korean Home Economics Association
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• v.41 no.12
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• pp.53-68
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• 2003

The purpose of this study was to develop lesson plans and teaching materials applying practical reasoning instruction for the 7th home economics curriculum content, and to test the effect of practical reasoning instruction on morality of middle school students. This study is a quasi-experimental research with a pretest-posttest design. Practical reasoning instruction for experimental group and traditionally lecture oriented instruction for comparison group were input, and tested the statistical differences between two groups before and after the treatment. The subjects for this study were 8th grade students of a middle school located in Kwangju city. Two classes of 76 students homogeneous in characteristics and academic record for each experimental and comparison group were assigned. Instrument used for this study was a revised moral indicator, that was developed by KEDI(2001). Spss/win for 10.0 statistics program was used for analysis of data. ANCOVA was done for testing statistical difference between pretest and posttest of experiment group and comparison group. Result of study which showed statistically significant difference between groups were：1. Virtue of "responsibility for words and deeds"(from 3.22 to 3.61 for experimental group and from 3.27 to 3.26 for comparison group) in domain of responsibility and cooperation, and virtue of "punctuality"(from 3.59 to 3.76 for experimental group and from 3.41 to 3.28 for comparison group) in domain of trustworthiness, 2. Virtue of "conversation etiquette"(from 3.47 to 3.67 for experimental group and from 3.28 to 3.31 for comparison group) in domain of caring for others, 3. Virtue of "forgiveness other′s mistakes"(from 3.32 to 3.65 for experimental group and from 3.33 to 3.25 for comparison group) in domain of kindness, concession, forgiveness, and virtue of "volunteering activity"(from 2.89 to 3.71 for experimental group and from 3.36 to 3.45 for comparison group) in domain of compassion and service, 4. Virtue of "equip the convenient facility for handicapped"(from 4.19 to 4.29 for experimental group and from 4.17 to 3.91 for comparison group) in domain of equality and human rights, virtue of "recovering selfness for own community"(from 2.34 to 2.79 for experimental group and from 2.14 to 2.29 for comparison group), virtue of "opposing way of accomplishing purpose by an means"(from 3.27 to 3.31 for experimental group and from 3.47 to 3.05 for comparison group), virtue of "opposing election of considering acquaintance"(from 3.35 to 3.56 for experimental group and from 3.12 to 3.14 for comparison group) in domain of fairness, and virtue of "eradication of military force or violence among countries"(from 3.49 to 3.57 for experimental group and from 3.38 to 3.05 for comparison group) in domain of love for humanity. The morality of experimental group was improved more than that of comparison group in all of above items. From the results of this study, following conclusion was drawn. Practical reasoning instruction in home economics is effective in raising students′ virtue and value of responsibility in words and deeds, trustworthiness in punctuality, courtesy of not interrupting conversation, forgiveness of other′s mistakes, volunteering activity, equity for handicapped, fairness opposing selfness for own community, fairness opposing way of accomplishing purpose by all means, fairness opposing election of considering acquaintance, and love for humanity opposing war.

• School Mathematics
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• v.13 no.4
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• pp.599-614
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• 2011

According to prior research studies, comparison of two data sets promote informal and formal statistical reasoning, which may mediate descriptive and inferential statistics. However, there has been relatively little attention given to the mediation of both descriptive and inferential statistics. We attempted to identify which statistical concepts or factors students used and how they applied concepts or factors to make decisions when they compared data sets. We also investigated the characteristics and changes of the view of concepts and factors. As a result, we identified that students paid attention to data value, center, spread, and sample, which are important factors of inferential statistics. Students' understanding of each factors were sometimes appropriate for inferential statistics, but sometimes not. From the results, we suggest instructional ideas for a task which can connect descriptive and inferential statistics.