• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.224-226
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• 2019

Currently, medical data is basically confidential and difficult to access because it is protected under the Medical Protection Act. For the practical education of the students who are in the process of education, the researcher or the faculty members create and upload simulated data (chart of the question bank) close to the actual data. In this paper, the maintenance and repair easier on the basis of node.js and Ajax, mysql, jquery to a web-based research and enables users to easily approach the problem of the chart and the easy to the difficult access to patient contact respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.605-608
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• 2009

It is necessary to estimate the Doppler spectrum for each range cell for the extraction of useful information from the return echoes in radar systems used for the remote sending purpose. However, The conventional spectrum estimation method, FFT(Fast Fourier Transform), called the Doppler filter bank, causes the frequency resolution problem if the dwell time is relatively short. This short acquisition time also spreads the side lobe levels of return echoes further, resulting in difficulties for the discrimination of weak target signals included in relatively strong target echoes. Therefore, in this paper, the efficient Doppler spectrum estimation methods are compared and investigated through the parameter spectrum estimation in the time domain to overcome these problems.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.171-180
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• 2021

The purpose of this paper is to estimate the impact of credit rationing on the amount of trade credit used by farmers in Vietnam. This study employs a survey data collected through direct interviews with heads of 1,065 rice households randomly selected out of provinces and city in the Mekong River Delta (MRD). In each province or city, the village with the largest area of land devoted to rice production from the district with the largest area of land devoted to rice production was picked up for survey. In each village, 200 rice farmers were randomly chosen for interview. Based on a probit model and a semi-parametric propensity score matching (PSM) estimator while controlling socio-demographic traits of rice farmers, the estimated results show that non-credit rationed farmers use less trade credit to finance production compared to their credit rationed counterparts. Moreover, the amount of trade credit used by farmers decreases as the degree of credit rationing drops. This paper provides evidence of the substitutive relationship between bank credit and trade credit. It also implicitly suggests that banks can drive trade creditors out of the market if they manage to solve the problem of information asymmetry and transaction cost.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1395-1403
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• 2021

The COVID-19 pandemic is having an adverse impact on Bangladesh's economy by affecting millions of people's life and hampering their income sources. The outbreak of COVID-19 has created more pressure on the labor market. The pandemic reduces employment opportunities as most of the companies have stopped their recruitment process to cut their operational costs, which increases the rate of graduate unemployment in Bangladesh. Hence, this study aims to investigate the impact of COVID-19 on graduate employability in Bangladesh that adversely affects the income of families and eventually the nation's economy. A literature review has been conducted from secondary sources to evaluate the impact, which shows that the rate of graduate unemployment increased from 47% to 58% in 2020 with an expected annual loss estimated at $53 million. Findings also reveal that the prime reasons for graduate employability are low demand and huge supply of graduates in the labor market, lack of professional skills of graduates, ineffective education system, etc. The study suggests that the government of Bangladesh should develop some policies to overcome this problem such as ensuring employment subsidies, implementing skills development programs, improving labor market flexibility, initiating credit programs for generating employment, and developing entrepreneurial ecosystems in Bangladesh.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.433-442
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• 2021

This research examines the financial performance of Village and Urban Community Funds (VFs). The study also explores the beneficial effects of the biggest microfinance programs in the world in the lower and lowest income provinces; specifically, whether VFs change household economic status or not. The data is collected uniquely from the village funds in four provinces of each region in Thailand which considerably reflect the government achievement. Accordingly, several financial ratios have been applied to evaluate the financial efficiency of the village funds, and the ordered logit model has been used to estimate the impact on economic variables of the poor. The findings show that the village funds do not improve the savings, income, consumption, and asset of VFs' members, although such funds have a higher financial performance. Furthermore, the VFs are a good substitute compared to the Bank for Agriculture and Agricultural Cooperatives (BAAC) credit because the cross-price elasticity of quantity of demand for such loans is positive. In particular, the loans from village funds are insignificantly correlated with the debt, income, asset, and economic status of VF members. This implies that Thai Village Funds do not alleviate definitely the serious problem about the financial situation in rural provinces. Thus, this microfinance does not change the economic well-being of the poor.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.311-324
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• 2021

Investors have increasingly become interested in macroeconomic antecedents in order to better understand the investment environment and estimate the scope of profitable investment in equity markets. This study endeavors to examine the interdependency between the macroeconomic antecedents (international oil price (COP), Domestic gold price (GP), Rupee-dollar exchange rates (ER), Real interest rates (RIR), consumer price indices (CPI)), and the BSE Sensex and Nifty 50 index return. The data is converted into a natural logarithm for keeping it normal as well as for reducing the problem of heteroscedasticity. Monthly time series data from January 1992 to July 2019 is extracted from the Reserve Bank of India database with the application of financial Econometrics. Breusch-Godfrey serial correlation LM test for removal of autocorrelation, Breusch-Pagan-Godfrey test for removal of heteroscedasticity, Cointegration test and VECM test for testing cointegration between macroeconomic factors and market returns,] are employed to fit regression model. The Indian market returns are stable and positive but show intense volatility. When the series is stationary after the first difference, heteroskedasticity and serial correlation are not present. Different forecast accuracy measures point out macroeconomics can forecast future market returns of the Indian stock market. The step-by-step econometric tests show the long-run affiliation among macroeconomic antecedents.

• The Journal of Industrial Distribution & Business
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• v.13 no.10
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• pp.1-8
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• 2022

Purpose: Support vector machines (SVMs) ensemble has been proposed to improve classification performance of Credit risk recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. To deal with this problem, this paper designs a support vector machines (SVMs) ensemble method based on fuzzy integral, which aggregates the outputs of separate component SVMs with importance of each component SVM. Research design, data, and methodology: This paper designs a personal credit risk evaluation index system including 16 indicators and discusses a support vector machines (SVMs) ensemble method based on fuzzy integral for designing a credit risk assessment system to discriminate good creditors from bad ones. This paper randomly selects 1500 sample data of personal loan customers of a commercial bank in China 2015-2020 for simulation experiments. Results: By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. Conclusions: The results show that the method proposed in this paper has higher classification accuracy than other classification methods, which confirms the feasibility and effectiveness of this method.

• Asia-Pacific Journal of Business
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• v.12 no.4
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• pp.13-40
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• 2021

Purpose - The purpose of this study is to predict corporate corruption in emerging markets such as Brazil, Russia, India, and China (BRIC) using different machine learning techniques. Since corruption is a significant problem that can affect corporate performance, particularly in emerging markets, it is important to correctly identify whether a company engages in corrupt practices. Design/methodology/approach - In order to address the research question, we employ predictive analytic techniques (machine learning methods). Using the World Bank Enterprise Survey Data, this study evaluates various predictive models generated by seven supervised learning algorithms: k-Nearest Neighbour (k-NN), Naïve Bayes (NB), Decision Tree (DT), Decision Rules (DR), Logistic Regression (LR), Support Vector Machines (SVM), and Artificial Neural Network (ANN). Findings - We find that DT, DR, SVM and ANN create highly accurate models (over 90% of accuracy). Among various factors, firm age is the most significant, while several other determinants such as source of working capital, top manager experience, and the number of permanent full-time employees also contribute to company corruption. Research implications or Originality - This research successfully demonstrates how machine learning can be applied to predict corporate corruption and also identifies the major causes of corporate corruption.

• Journal of Practical Agriculture & Fisheries Research
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• v.9 no.1
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• pp.3-12
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• 2007

It is our imminent project that we should train young and able manpower to strengthen the international competitiveness under the free trade of agricultural products, to solve the problem of decrease in farm population and of aging people in agriculture. The objective of this research is to suggest an alternative policy plan through the survey and analysis on the controversial issues in loans for starting agricultural business based on the survey of graduates of Korea National Agricultural College from 2002 to 2005. According to the survey, in case of graduates who are not available sufficient fanning capital such as land and agricultural facilities on it, they are not able to get loans from banks in that situation. The survey, as a result, points out that those who are legally required to do farming should be given several special aids by the government such as the improvement of Credit Guarantee Fund System for Farmers and Fishermen and the farming loans conditions for initial farm business, a long term lease of public land, giving a priority in lease-farmland project of farmland bank and allowing loan for working capital for farm management.

• Asia pacific journal of information systems
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• v.30 no.1
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• pp.21-30
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• 2020

Bitcoin (BTC) is a type of cryptocurrency that supports transaction/payment of virtual money between BTC users without the presence of a central authority or any third party like bank. It uses some cryptographic techniques namely public- and private-keys, digital signature and cryptographic-hash functions, and they are used for making secure transactions and maintaining distributed public ledger called blockchain. In BTC system, each transaction signed by sender is broadcasted over the P2P (Peer-to-Peer) Bitcoin network and a set of such transactions collected over a period is hashed together with the previous block/other values to form a block known as candidate block, where the first block known as genesis-block was created independently. Before a candidate block to be the part of existing blockchain (chaining of blocks), a computation-intensive hard problem needs to be solved. A number of miners try to solve it and a winner earns some BTCs as inspiration. The miners have high computing and hardware resources, and they play key roles in BTC for blockchain formation. This paper mainly analyses the underlying cryptographic techniques, identifies some weaknesses and proposes their enhancements. For these, two modifications of BTC are suggested ― (i) All BTC users must use digital certificates for their authentication and (ii) Winning miner must give signature on the compressed data of a block for authentication of public blocks/blockchain.