• Journal of Digital Contents Society
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• v.19 no.4
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• pp.771-777
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• 2018

This study proposes a big data based risk analysis framework to analyze more comprehensive disaster risk and vulnerability. We introduce a distributed and parallel framework that allows large volumes of data to be processed in a short time by using open-source disaster risk assessment tool. A performance analysis of the proposed system presents that it achieves a more faster processing time than that of the existing system and it will be possible to respond promptly to precise prediction and contribute to providing guideline to disaster countermeasures. Proposed system is able to support accurate risk prediction and mitigate severe damage, therefore will be crucial to giving decision makers or experts to prepare for emergency or disaster situation, and minimizing large scale damage to a region.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.175-182
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• 2019

This study was conducted to test the assessment validity and examine the cut-off scores for driving risk as a part of the Self-report Assessment Forecasting Elderly Driving Risk (SAFE-DR) development project. The 132 senior drivers were categorized as either risky of 58 or safe of 74 drivers through the Drivers 65 Plus. Based on this initial assessment, we analyzed the risk prediction cut-offs. Furthermore, we tested the construct, content, and predictive validity. The cut-off score for the prediction of driving risk was found to be 74.5 points. The positive predictive value was 88.6%, and the negative predictive value was 86.3% about the cut-off score, signifying an excellent level of discrimination. Convergent validity, nomological validity, and content validity were found to be appropriate. Therefore, this study confirms that SAFE-DR is an appropriate assessment that can be used to screen dangerous elderly drivers.

• Electronics and Telecommunications Trends
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• v.35 no.1
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• pp.80-88
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• 2020

This paper describes the development trends and service provision examples of disaster occurrence and spread prediction technology for various disasters such as tsunamis, floods, and fires. In terms of fires, we introduce the WIFIRE system, which predicts the spread of large forest fires in the United States, and the Metro21: Smart Cities Institute project, which predicts the risk of building fires. This paper describes the development trends in tsunami prediction technology in the United States and Japan using artificial intelligence (AI) to predict the occurrence and size of tsunamis that cause great damage to coastal cities in Japan, Indonesia, and the United States. In addition, it introduces the NOAA big data platform built for natural disaster prediction, considering that the use of big data is very important for AI-based disaster prediction. In addition, Google's flood forecasting system, domestic and overseas earthquake early warning system development, and service delivery cases will be introduced.

• Genomics & Informatics
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• v.14 no.4
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• pp.149-159
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• 2016

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.183-190
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• 2021

In this paper, we implement a system for a fund recommendation based on the investment propensity and for a future fund price prediction. The investment propensity is classified by scoring user responses to series of questions. The proposed system recommends the funds with a suitable risk rating to the investment propensity of the user. The future fund prices are predicted by Prophet model which is one of the machine learning methods for time series data prediction. Prophet model predicts future fund prices by learning the parameters related to trend changes. The prediction by Prophet model is simple and fast because the temporal dependency for predicting the time-series data can be removed. We implement web pages for the fund recommendation and for the future fund price prediction.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• Journal of Korean Neurosurgical Society
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• v.58 no.4
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• pp.346-349
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• 2015

Objective : To investigate the value of lumbar bone mineral density (BMD) in fracture risk assessment (FRAX) tool. Methods : One hundred and ten patients aged over 60 years were enrolled and divided into 2 groups as non-osteoporotic vertebral fracture (OVF) and OVF groups. The 10-year-risk of major osteoporotic vertebral fracture of each group was calculated by FRAX tool with femoral and lumbar spine BMDs to compare the usefulness of lumbar spine BMD in prediction of OVF. The blood level of osteocalcin and C-terminal telopeptide (CTX) as markers of activities of osteoblast and osteoclast, respectively were analyzed using the institutional database. Results : In the OVF group, the ratio of patients with previous fracture history or use of glucocorticoid was higher than those in non-OVF group (p=0.000 and 0.030, respectively). The levels of T-score of femur neck and lumbar spine in OVF group were significantly lower than those in non-OVF group (p=0.001 and 0.000, respectively). The risk of OVF in FRAX using femur BMD in non-OVF and OVF groups was $6.7{\pm}6.13$ and $11.4{\pm}10.06$, respectively (p=0.007). The risk of using lumbar BMD in the 2 groups was $6.9{\pm}8.91$ and $15.1{\pm}15.08$, respectively (p=0.002). The areas under the receiver operator characteristic curve in the FRAX risk with lumbar and femur neck BMD were 0.726 and 0.684, respectively. The comparison of osteocalcin and CTX was not significant (p=0.162 and 0.369, respectively). Conclusion : In our study, the 10-year risk of major osteoporotic fracture in the OVF group of our study was lower than the recommended threshold of intervention for osteoporosis. Hence, a lower threshold for the treatment of osteoporosis may be set for the Korean population to prevent OVF. In the prediction of symptomatic OVF, FRAX tool using lumbar spine BMD may be more useful than that using femur neck BMD.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.313-322
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• 2020

There is always a risk of water disasters due to sudden storms in mountainous regions in Korea, which is more than 70% of the country's land. In this study, a radar-based risk prediction technique for sudden downpour is applied in the mountainous region and is evaluated for its applicability using Mt. Biseul rain radar. Eight local heavy rain events in mountain regions are selected and the information was calculated such as early detection of cumulonimbus convective cells, automatic detection of convective cells, and risk index of detected convective cells using the three-dimensional radar reflectivity, rainfall intensity, and doppler wind speed. As a result, it was possible to confirm the initial detection timing and location of convective cells that may develop as a localized heavy rain, and the magnitude and location of the risk determined according to whether or not vortices were generated. In particular, it was confirmed that the ground rain gauge network has limitations in detecting heavy rains that develop locally in a narrow area. Besides, it is possible to secure a time of at least 10 minutes to a maximum of 65 minutes until the maximum rainfall intensity occurs at the time of obtaining the risk information. Therefore, it would be useful as information to prevent flash flooding disaster and marooned accidents caused by heavy rain in the mountainous area using this technique.

• Korean Journal of Radiology
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• v.21 no.9
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• pp.1055-1064
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• 2020

Objective: To assess the incremental prognostic value of coronary computed tomography angiography (CCTA) in comparison to a clinical risk model (Framingham risk score, FRS) and coronary artery calcium score (CACS) for future cardiac events in ischemic stroke patients without chest pain. Materials and Methods: This retrospective study included 1418 patients with acute stroke who had no previous cardiac disease and underwent CCTA, including CACS. Stenosis degree and plaque types (high-risk, non-calcified, mixed, or calcified plaques) were assessed as CCTA variables. High-risk plaque was defined when at least two of the following characteristics were observed: low-density plaque, positive remodeling, spotty calcification, or napkin-ring sign. We compared the incremental prognostic value of CCTA for major adverse cardiovascular events (MACE) over CACS and FRS. Results: The prevalence of any plaque and obstructive coronary artery disease (CAD) (stenosis ≥ 50%) were 70.7% and 30.2%, respectively. During the median follow-up period of 48 months, 108 patients (7.6%) experienced MACE. Increasing FRS, CACS, and stenosis degree were positively associated with MACE (all p < 0.05). Patients with high-risk plaque type showed the highest incidence of MACE, followed by non-calcified, mixed, and calcified plaque, respectively (log-rank p < 0.001). Among the prediction models for MACE, adding stenosis degree to FRS showed better discrimination and risk reclassification compared to FRS or the FRS + CACS model (all p < 0.05). Furthermore, incorporating plaque type in the prediction model significantly improved reclassification (integrated discrimination improvement, 0.08; p = 0.023) and showed the highest discrimination index (C-statistics, 0.85). However, the addition of CACS on CCTA with FRS did not add to the prediction ability for MACE (p > 0.05). Conclusion: Assessment of stenosis degree and plaque type using CCTA provided additional prognostic value over CACS and FRS to risk stratify stroke patients without prior history of CAD better.

• Asian Journal of Atmospheric Environment
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• v.10 no.4
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• pp.197-207
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• 2016

Large quantities of air pollutants are released into the atmosphere and hence, must be monitored and routinely assessed for their health implications. This paper proposes a stochastic technique to predict unobserved hazardous air pollutants (HAPs), especially Benzo[a]pyrene (BaP), which can have negative effects on human health. The proposed approach constructs a nearest-neighbor structure by incorporating the linkage between BaP and meteorology and meteorological effects. This approach is adopted in order to predict unobserved BaP concentrations based on observed (or forecasted) meteorological conditions, including temperature, precipitation, wind speed, and air quality. The effects of BaP on human health are examined by characterizing the cancer risk. The efficient prediction provides useful information relating to the optimal monitoring period and projections of future BaP concentrations for both industrial and residential areas within Korea.