• Journal of Gastric Cancer
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• v.24 no.4
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• pp.451-463
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• 2024

Purpose: Although laparoscopic distal gastrectomy has rapidly replaced open distal gastrectomy, laparoscopic total gastrectomy (LTG) is less frequently performed owing to technical difficulties. Robotic surgery could be an appropriate minimally invasive alternative to LTG because it alleviates the technical challenges posed by laparoscopic procedures. However, few studies have compared the oncological safety of robotic total gastrectomy (RTG) with that of LTG, especially for advanced gastric cancer (AGC). Herein, we aimed to assess the oncological outcomes of RTG for AGC and compare them with those of LTG. Materials and Methods: We retrospectively reviewed 147 and 204 patients who underwent RTG and LTG for AGC, respectively, between 2007 and 2020. Long-term outcomes were compared using inverse probability of treatment weighting (IPTW). Results: After IPTW, the 2 groups exhibited similar clinicopathological features. The 5-year overall survival was comparable between the 2 groups (88.5% [95% confidence interval {CI}, 79.4%-93.7%] after RTG and 87.3% [95% CI, 80.1%-92.0%]) after LTG; log-rank P=0.544). The hazard ratio (HR) for death after RTG compared with that after LTG was 0.73 (95% CI, 0.40-1.33; P=0.304). The 5-year relapse-free survival was also similar between the 2 groups (75.7% [95% CI, 65.2%-83.4%] after RTG and 76.4% [95% CI, 67.9%-83.0%] after LTG; log-rank P=0.850). The HR for recurrence after RTG compared with that after LTG was 0.93 (95% CI, 0.60-1.46; P=0.753). Conclusions: Our findings revealed that RTG and LTG for AGC had similar long-term outcomes. RTG is an oncologically safe alternative to LTG and has technical advantages.

• Journal of the Korean Institute of Gas
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• v.21 no.2
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• pp.41-49
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• 2017

Recently, as the number of facilities using chemicals is increasing, the amount of handling is rapidly increasing. However, chemical spills are occurring steadily, and if large quantities of chemicals are leaked in time, they are likely to cause major damage. These industrial complexes use information obtained from a number of sensors to detect and monitor leaking areas, and are used in industrial fields by applying existing fixed sensors to robots and drones. Therefore, it is necessary to propose a sensor placement method at the interface for rapid detection and response based on various leaking scenarios reflecting leaking conditions and environmental conditions of the chemical handling process. In this study, COMSOL was used to analyze the actual accident scenarios by applying the medium parameter to the case of chemical leaks. Based on the accident scenarios, the objective function is selected so that the velocity of each robot is calculated by attaching importance to each item of sensor detection probability, sensing time and sensing scenario number. We also confirmed the feasibility of this method of reliability analysis for unexpected leak accidents. Based on the above results, it is expected that it will be helpful to trace back the leakage source based on the concentration data of the portable sensor to be applied later.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.