• 한국멀티미디어학회논문지
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• 제24권10호
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• 아태비즈니스연구
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• 제14권4호
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• pp.233-254
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• 2023

Purpose - This study aims to verify whether the effect of tax avoidance on corporate value is non-linear in the Korean financial markets. Design/methodology/approach - This study believes that the cause of the inconsistent empirical analysis results of previous studies that verified the relationship between tax avoidance and firm value may be an error in assuming linearity, and verifies whether a nonlinear relationship exists. The sample company in this study is a December settlement corporation listed on the Korean stock market, and the analysis period is from 2000 to 2021. In the empirical analysis model, Tobin's Q is used as a proxy for corporate value, tax avoidance is used as the main independent variable, and a regression model is designed with corporate size, growth rate, and debt ratio set as control variables. Findings - As a result of the empirical analysis, it can be confirmed that there is an inverted U-shaped nonlinear relationship between tax avoidance and corporate value. In the additional analysis using Ohlson (1995) firm valuation model for the robustness of the results of the empirical analysis, the same nonlinear value relationship between tax avoidance can be confirmed. Research implications or Originality - This study is considered to be meaningful in that it verifies the non-linear relationship of tax avoidance, which has not been attempted in previous studies. The meaning of the inverted U-shaped nonlinear relationship presented in this study is that corporate tax avoidance acts as a factor that increases corporate value up to a certain level, but rather becomes a factor that decreases corporate value when it exceeds a critical point. These results are expected to provide new perspectives and perspectives on tax avoidance to companies belonging to the Korean capital market.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.49-59
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• 2021

This study empirically analyzes the effect of firm overinvestment propensity on the value relevance of capital investment. In order to verify this point, this study attempts to analyze the value relevance of overinvestment firms' capital investments. The analysis was performed according to the model of Biddle et al. (2009) and McNichols and Stubben (2008) on overinvestment propensity for analysis, and the results are as follows. First, in terms of overinvestment, corporate capital investment shows negative value relevance, so the excessive investments above reasonable levels have reduced firm's value. In contrast, the value relevance for capital investment showed a positive value for firms whose managerial propensity changed, that is, from under-investment in the previous year, it shifted to overinvestment in the current year. Second, as a result of analyzing the value relevance of the investment increase according to the investment propensity, the overinvestment firms showed negative values and the underinvested firms showed positive values; thus, the value relevance of the increase in investment was opposite to the investment propensity of the firm. These findings confirm that the stock market differentially evaluates investment efficiency according to investment propensity, continuity, and investment alterations, and reflects it appropriately in the firm's value.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.1083-1094
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• 2021

This paper explores the effect of IFRS on the financial performance of Iraqi commercial banks. It also investigates the value significance of financial performance statements using the Ohlson model, which has been used for the stock value relevance test in a number of studies. Using a sample of 66 listed banks on the Iraq Stock Exchange over three years of IFRS pre-adoption (2011-2013) and three years of IFRS post-adoption (2016-2018), we find financial performance components EPS and BVS value relevant to the stock returns. The findings also indicate that the implementation of IFRS has a major positive effect on the value relevance of the BVS, while the adoption of IFRS does not have a significant impact on the value relevance of the EPS reported by Iraqi banks. Our results indicate that the market value of the bank rises dramatically with enhanced financial performance reporting. In addition, the implementation of IFRS has a major effect on the financial performance measures and the value relevance of financial reporting in the Iraqi banking sector. This paper adds to previous value relevance literature and IFRS by throwing light on the banking sector in a developing country that has recently moved from applying local accounting standards to IFRS.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2009년도 추계학술발표대회
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• pp.335-336
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• 2009

In this paper, I propose an automatic approach to annotating images dynamically based on MBRM(Multiple Bernoulli Relevance Models) using GLCM(Grey Level Co-occurrence Matrix). MBRM is more appropriate to annotate images compare with multinomial distribution. The model is used in limited test set, MSRC-v2 (Microsoft Research Cambridge Image Database). The results show that this model is significantly outperforms previously reported results on the task of image annotation and retrieval.

• 한국IT서비스학회지
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• 제8권3호
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• pp.67-81
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• 2009

This study aims to test the valur relevance of development cost particularly focusing on IT firms of KOSDAQ. Test period is from 2005 to 2007 and the samples are 2,271 year-firms including 1,692 firms that reported development cost in financial statements. The basic test model is a modified Ohlson(1995)'s linear model. The empirical results show that there is the negative relation between stock price and development cost reported as asset. It means that development costs reported as asset is considered as expense in the market. It implies that development activities of KOSDAQ IT firms is not related to market-leading technologies or goods. Otherwise it might reflect the conservative valuation of market on the unstability of KOSDAQ market itself.

• 정보관리학회지
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• 제10권2호
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• pp.3-22
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• 1993

최근 시소러스를 기반으로 하는 불리안 검색 시스템에서 문서의 순위 결정에 사용 될 수 있는 Relevance, R-distance, K-distance와 같은 방법들이 개발되었다. 이러한 방법들은 색인어들 사이의 연관성 정보를 이용하여 문서들의 순위를 결정함으로써 많은 경우에 높은 검색 효율을 제공할 지라도, 불리안 연산자 AND, OR, NOT에 대한 연산 방법이 문제점으로 지적되어왔다. 본 논문에서는 개선된 퍼지 집합 모델과 확장된 불리안 모델을 시소러스가 제공하는 색인어들 사이의 연관성 정보를 효율적으로 이용할 수 있도록 확장함으로써, 기존 방법들의 문제점을 극복하는 새로운 순위 결정 방법 KB-FSM과 KB-EBM을 제안한다. 또한 KB-FSM과 KB-EBM이 Relevance, R-distance, K-distance보다 문서들의 순위를 보다 정확하게 결정함을 성능 비교를 통하여 입증한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권3호
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• pp.157-162
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• 2016

In this paper, a yield prediction model based on relevance vector machine (RVM) and a granular computing model (quotient space theory) is presented. With a granular computing model, massive and complex meteorological data can be analyzed at different layers of different grain sizes, and new meteorological feature data sets can be formed in this way. In order to forecast the crop yield, a grey model is introduced to label the training sample data sets, which also can be used for computing the tendency yield. An RVM algorithm is introduced as the classification model for meteorological data mining. Experiments on data sets from the real world using this model show an advantage in terms of yield prediction compared with other models.

• Journal of Information Technology Applications and Management
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• 제18권4호
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• pp.1-20
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• 2011

The recent development of mobile devices, including smartphones has maximized the user's convenience. This phenomenon has triggered the specification and activation of mobile cloud service and transformed the user's information usage and communication means. Despite such development of new technology, there is little understanding of mobile cloud. Therefor, in order to help the understanding of the usage of mobile cloud computing technology, this study developed the research model based on the UTAUT (the Unified Theory of Acceptance and Usage of Technology) model. The proposed research model included five external determinants, such as Interoperability, Information Organization, Adequacy, Information Security and Context Awareness, along with the moderating effects of Job Relevance, and empirically proved their influence on the user's job performance. This study proved the research model by analyzing the data collected from 287 copies of survey with the structural equation modeling approach. The result showed that all determinants, except Information Security have positive impact on the usage of mobile cloud computing technology. Thus, this study can compliment the limitations of previous research, suggest a new direction to analyze the user's perspective in the related research on mobile cloud and build a theoretical foundation for the further development of mobile cloud.

• Structural Engineering and Mechanics
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• 제72권1호
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• pp.31-41
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• 2019

Structural integrity assessment of piping components is of paramount important for remaining life prediction, residual strength evaluation and for in-service inspection planning. For accurate prediction of these, a reliable fracture parameter is essential. One of the fracture parameters is stress intensity factor (SIF), which is generally preferred for high strength materials, can be evaluated by using linear elastic fracture mechanics principles. To employ available analytical and numerical procedures for fracture analysis of piping components, it takes considerable amount of time and effort. In view of this, an alternative approach to analytical and finite element analysis, a model based on relevance vector machine (RVM) is developed to predict SIF of part through crack of a piping component under fatigue loading. RVM is based on probabilistic approach and regression and it is established based on Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Model for SIF prediction is developed by using MATLAB software wherein 70% of the data has been used for the development of RVM model and rest of the data is used for validation. The predicted SIF is found to be in good agreement with the corresponding analytical solution, and can be used for damage tolerant analysis of structural components.