• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Tuberculosis and Respiratory Diseases
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• v.45 no.5
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• pp.962-974
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• 1998

Background: Alteration of p53 tumor suppressor genes is most frequently identified in human neoplasms, including lung carcinoma. It is well known that bcl-2 oncoprotein protects cells from apoptosis. Recent studies have demonstrated that bcl-2 expression is associated with favorable prognosis for patients with non-small cell lung carcinoma. However, the precise biologic role of bcl-2 in the development of these tumors is still obscure. p53 and bcl-2 have important regulatory influence in the apoptotic pathway and thus their relationship is of interest in tumorigenesis, especially lung cancer. Purpose: The author investigated to know the prognostic significance of the expression of p53 and bcl-2 in radically resected non-small cell lung cancer. Method: 84 cases of formalin-fixed paraffin-embedded blocks from resected primary non-small cell lung cancer from 1980 to 1994 at Hanyang University Hospital were available for both clinical follow-up and immunohistochemical staining using monoclonal antibodies for p53 and bcl-2. Results : The histologic classification of the tumor was based on WHO criteria., and the specimens included 45 squamous cell carcinomas(53.6%), 28 adeonocarcinomas(33.3%) and 11 large cell carcinomas(13.1 %). p53 immunoreactivity was noted in 47 cases of 84 cases(56.0%). bcl-2 immunoreactivity was noted in 15 cases of 84 cases(17.9%). The mean survival duration was $64.23{\pm}10.73$ months in bcl-2 positive group and $35.28{\pm}4$. 39 months in bcl-2 negative group. The bcl-2 expression was significantly correlated with survival in radically resected non-small cell lung cancer patients(p=0.03). The mean survival duration was $34.71{\pm}6.12$ months in p53 positive group and $45.35{\pm}6.30$ months in p53 negative group(p=0.21). The p53 expression was not predictive for survival. There was no correlation between combination of the different status of p53 and bcl-2 expression in our study. Conclusions : The interaction and the regulation of new biologic markers, such as those involved in the apoptotic pathway, are complex. bcl-2 overexpression is a good prognostic factor in non-small cell lung cancer and p53 expression is not significantly associated with the prognostic factor in non-small cell lung cancer.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Tuberculosis and Respiratory Diseases
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• v.47 no.3
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• pp.356-364
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• 1999

Backgrounds: Previous reports have revealed a high morbidity and mortality in fatal asthma patients, especially those treated in the medical intensive care unit(MICU). But it has not been well known about the predictable factors for the mortality of fatal asthma(F A) with acute respiratory failure. In order to define the predictable factors for the mortality of FA at the admission to MICU, we analyzed the relationship between the clinical parameters and the prognosis of FA patients. Methods: A retrospective analysis of all medical records of 59 patients who had admitted for FA to MICU at a tertiary care MICU from January 1992 to March 1997 was performed. Results: Over all mortality rate was 32.2% and 43 patients were mechanically ventilated. In uni-variate analysis, the death group had significantly older age ($66.2{\pm}10.5$ vs. $51.0{\pm}18.8$ year), lower FVC($59.2{\pm}21.1$ vs. $77.6{\pm}23.3%$) and lower $FEV_1$($41.4{\pm}18.8$ vs. $61.l{\pm}23.30%$), and longer total ventilation time ($255.0{\pm}236.3$ vs. $98.1{\pm}120.4$ hour) (p<0.05) compared with the survival group (PFT: best value of recent 1 year). At MICU admission, there were no significant differences in vital signs, $PaCO_2$, $PaO_2/FiO_2$, and $AaDO_2$, in both groups. However, on the second day of MICU, the death group had significantly more rapid pulse rate ($121.6{\pm}22.3$ vs. $105.2{\pm}19.4$ rate/min), elevated $PaCO_2$ ($50.1{\pm}16.5$ vs. $41.8{\pm}12.2 mm Hg$), lower $PaO_2/FiO_2$, ($160.8{\pm}59.8$ vs. $256.6{\pm}78.3 mm Hg$), higher $AaDO_2$ ($181.5{\pm}79.7$ vs. $98.6{\pm}47.9 mm Hg$), and higher APACHE III score ($57.6{\pm}21.1$ vs. $20.3{\pm}13.2$) than survival group (p<0.05). The death group had more frequently associated with pneumonia and anoxic brain damage at admission, and had more frequently developed sepsis during disease progression than the survival group (p<0.05). Multi-variate analysis using APACHE III score and $PaO_2/FiO_2$, ratio on first and second day, age, sex, and pneumonia combined at admission revealed that APACHE III score (40) and $PaO_2/FiO_2$ ratio (<200) on second day were regarded as predictive factors for the mortality of fatal asthma (p<0.05). Conclusions: APACHE III score ($\geq$40) and $PaO_2/FiO_2$ ratio (<200) on the second day of MICU, which might reflect the response of treatment, rather than initially presented clinical parameters would be more important predictable factors of mortality in patients with FA.