• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• 한국사회정책
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• v.25 no.4
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• pp.233-263
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• 2018

This study sought to identify gender-specific mechanisms of increased suicide rates during economic crises in South Korea. In order to address research aims, we focused on two international economic crises: IMF financial crisis in 1997, and international recession in 2008. This study provides three main findings. First, different mechanisms increased suicide rates during the two economic crises. Particularly, the high level of unemployment raised suicide rates during the 1997 IMF while the high level of working poor in the 2008 recession. Second, suicidal risk patterns for men and women differed at each period. The 1997 crisis which mostly affected full-time permanent workers had had relatively greater impacts on men suicide, whereas the 2008 crisis which affected precarious workers had done on women suicide. Finally, our finding indicated that these gender-specific risk patterns had been derived from the gendered labour market and male-friendly social policy. Placing women at the periphery of the labor market and using them as a buffer in times of crisis, governments failed to protect them from their economic difficulties. Suicide is fundamental and important public health and social problems. These findings suggest that the national suicide prevention strategy should pay attention to the social determinants of suicide through gendered as well as population health perspectives.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.6
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• pp.71-88
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• 2022

The purpose of this study is to develop the competencies to help micro entrepreneurs who have experienced failure to successfully re-challenge. To this end, relevant literature published from 1977 to 2022 was analyzed, behavioral event interviews (BEI) were conducted with 7 successful micro entrepreneurs, and focus group interviews (FGI) were conducted three times by inviting competency development and HRD experts. Based on these research activities, the draft about competencies for micro entrepreneurs who had have failure was derived. And then inviting 12 experts in related field for Delphi Analysis, the final competency model that helps micro entrepreneurs successfully recover were developed as follows : Competency Groups(small business owners, recovery from failure), 8 detailed competencies(seize business opportunities, business planning, business differentiation, operation management, market exploration, research and development of products and services, positive self-regulation, overcoming and coping with failure experiences), 22 competency factors, and 72 behavioral indicators. This study has an academic significance in that it developed the competencies required for micro entrepreneurs recovering from failure. In addition, the results of this study can be used to develop a competency-based education program for micro entrepreneurs and to select suitable candidates for support programs.

• Journal of the Korean Society of Radiology
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• v.83 no.3
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• pp.473-485
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• 2022

The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important biomarker for the diagnosis of neurodegenerative disease. Objective and reliable assessment and precise detection are important for the early diagnosis of neurodegenerative diseases. Artificial intelligence (AI) using brain MRI applied to the study of neurodegenerative diseases could promote early diagnosis and optimal decisions for treatment plans. MRI-based AI software have been developed and studied worldwide. Representatively, there are MRI-based volumetry and segmentation software. In this review, we present the development process of brain volumetry analysis software in neurodegenerative diseases, currently used and developed AI software for neurodegenerative disease in the Republic of Korea, probable uses of AI in the future, and AI software limitations.

• Applied Biological Chemistry
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• v.15 no.1
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• pp.85-92
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• 1972

The field experiment was performed to investigate the effects of various kinds of phosphorus fertilizers such as double superphosphate, fused magnesium phosphate and Simagcarin (both the Kyun-gi Chemical Co, products) on the physiological roles in development of root system, growth and yield compositions of rice plant. Radioactive phosphoric acid $(H_3\;^{32}PO_4)$ was applied to measure the root activity. 1. The number of total tillers was significantly increased in double superphosphate plots, but the rate of fruitful tillers was more numerous in the fused magnesium phosphate and the Simagcarin plots than that of the other plots. 2. The grain yield was much more obtained in the fused magnesium phosphate and Simagcarin plots (no significant difference were found between both of plots) than the double superphosphate and control plots. It seemed due to the increasing of seedbearing rate and number of fruitful tillers. 3. In double superphosphate plots, root system was mostly developed near topsoil areas, but fused magnesium phosphate and the Simagcarin plots, root system was uniformly distributed from topsoil to subsoil areas. 4. As the results of those experiments, fused magnesium phosphate and Simagcarin was demonstrated to be soil amendmentical materials rather than the phosphorus fertilizers, especially in low productive paddy soils which lack the special mineral nutritions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.384-398
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• 2015

In this paper, the high-resolution Weather Research and Forecasting/Noah-MultiParameterization (WRF/Noah-MP) modeling system is configured for the Cheongmicheon Farmland site in Korea (CFK), and its performance in land and atmospheric simulation is evaluated using the observed data at CFK during the 2014 special observation period (21 August-10 September). In order to explore the usefulness of turning on Noah-MP dynamic vegetation in midterm simulations of surface and atmospheric variables, two numerical experiments are conducted without dynamic vegetation and with dynamic vegetation (referred to as CTL and DVG experiments, respectively). The main results are as following. 1) CTL showed a tendency of overestimating daytime net shortwave radiation, thereby surface heat fluxes and Bowen ratio. The CTL experiment showed reasonable magnitudes and timing of air temperature at 2 m and 10 m; especially the small error in simulating minimum air temperature showed high potential for predicting frost and leaf wetness duration. The CTL experiment overestimated 10-m wind and precipitation, but the beginning and ending time of precipitation were well captured. 2) When the dynamic vegetation was turned on, the WRF/Noah-MP system showed more realistic values of leaf area index (LAI), net shortwave radiation, surface heat fluxes, Bowen ratio, air temperature, wind and precipitation. The DVG experiment, where LAI is a prognostic variable, produced larger LAI than CTL, and the larger LAI showed better agreement with the observed. The simulated Bowen ratio got closer to the observed ratio, indicating reasonable surface energy partition. The DVG experiment showed patterns similar to CTL, with differences for maximum air temperature. Both experiments showed faster rising of 10-m air temperature during the morning growth hours, presumably due to the rapid growth of daytime mixed layers in the Yonsei University (YSU) boundary layer scheme. The DVG experiment decreased errors in simulating 10-m wind and precipitation. 3) As horizontal resolution increases, the models did not show practical improvement in simulation performance for surface fluxes, air temperature, wind and precipitation, and required three-dimensional observation for more agricultural land spots as well as consistency in model topography and land cover data.

• Knowledge Management Research
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• v.22 no.4
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• pp.71-85
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• 2021

The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.