• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.4
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• pp.33-45
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• 2015

Currently, Internet is used an essential tool in the business area. Despite this importance, there is a risk of network attacks attempting collection of fraudulence, private information, and cyber terrorism. Firewalls and IDS(Intrusion Detection System) are tools against those attacks. IDS is used to determine whether a network data is a network attack. IDS analyzes the network data using various techniques including expert system, data mining, and state transition analysis. This paper tries to compare the performance of two data mining models in detecting network attacks. They are decision tree (C4.5), and neural network (FANN model). I trained and tested these models with data and measured the effectiveness in terms of detection accuracy, detection rate, and false alarm rate. This paper tries to find out which model is effective in intrusion detection. In the analysis, I used KDD Cup 99 data which is a benchmark data in intrusion detection research. I used an open source Weka software for C4.5 model, and C++ code available for FANN model.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.11
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• pp.4525-4530
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• 2015

Background: The MTHFR C677T polymorphism is a genetic alteration affecting an enzyme involved in folate metabolism, but its relationship to host susceptibility to prostate cancer remains uncertain. The aim of this study was to investigate the association between MTHFR C677T polymorphism and prostate cancer by performing a meta-analysis. Materials and Methods: Pubmed and Web of Science databases were searched for case-control studies investigating the association between MTHFR C677T polymorphism and prostate cancer. Odds ratios (OR) and 95% confidence intervals (95%CI) were used to assess any link. Results: A total of 22 independent studies were identified, including 10,832 cases and 11,993 controls. Meta-analysis showed that there was no obvious association between MTHFR C677T polymorphism and risk of prostate cancer under all five genetic models. There was also no obvious association between MTHFR C677T polymorphism and risk of prostate cancer in the subgroup analyses of Caucasians. In contrast, MTHFR C677T polymorphism was associated with increased risk for prostate cancer in Asians with the allele model (C vs G: OR=1.299, 95 %CI =1.121-1.506, P=0.001, $P_{heterogeneity}=0.120$, $I^2=45%$), additive genetic model (CC vs TT: OR =1.925, 95 % CI= 1.340-2.265, P=0.00, $P_{heterogeneity}=0.587$, $I^2=0.00%$), recessive model (CC vs TT+TC: OR= 1.708, 95 % CI=1.233-2.367, P=0.001, $P_{heterogeneity}=0.716$, $I^2=0.00%$), and heterozygote genetic model (CT vs TT: OR=2.193, 95 % CI =1.510-3.186, P=0.000, $P_{heterogeneity}=0.462$, $I^2=0.00%$). Conclusions: These results suggest that the MTHFR C677T polymorphism does not contribute to the risk of prostate cancer from currently available evidence in populations overall and Caucasians. However, the meta analysis indicates that it may play a role in prostate cancer development in Asians.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.3973-3980
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• 2014

Prostate adenocarcinoma is one of the leading causes of cancer related mortality in men but still limited knowledge is available about its associated functional SNPs including rs1042522 (Pro72Arg). The present study was undertaken to explore the association of this SNP with susceptibility to prostate adenocarcinoma along with its structural and functional impacts in the Pakistani population in a case-control study. Three-dimensional structure of human TP53 with Pro72Arg polymorphism was predicted through homology modeling, refined and validated for detailed structure-based assessment. We also carried out a HuGE review of the previous available data for this polymorphism. Different genetic models were used to evaluate the genotypes association with the increased risk of PCa (Allelic contrast: OR=0.0.34, 95%CI 0.24-0.50, p=0.000; GG vs CC: OR=0.17, 95%CI 0.08-0.38, p=0.000; Homozygous: OR=0.08, 95%CI 0.04-0.15, p=0.000; GC vs CC: OR=2.14, 95%CI 1.01-4.51, p=0.046; Recessive model: OR=0.10, 95%CI 0.05-0.18, p=0.000; Log Additive: OR=3.54, 95%CI 2.13-5.89, p=0.000) except the Dominant model (OR=0.77, 95%CI 0.39-1.52, p=0.46). Structure and functional analysis revealed that the SNP in the proline rich domain is responsible for interaction with HRMT1L2 and WWOX. In conclusion, it was observed that the Arg coding G allele is highly associated with increased risk of prostate adenocarcinoma in the Pakistani population (p=0.000).

• Environmental Analysis Health and Toxicology
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• v.26
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• pp.9.1-9.9
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• 2011

Objectives: In order to evaluate which temperature index is the best predictor for the health impact assessment of heat stress in Korea, several indexes were compared. Methods: We adopted temperature, perceived temperature (PT), and apparent temperature (AT), as a heat stress index, and changes in the risk of death for Seoul and Daegu were estimated with $^1{\circ}C$ increases in those temperature indexes using generalized additive model (GAM) adjusted for the non-temperature related factors: time trends, seasonality, and air pollution. The estimated excess mortality and Akaike's Information Criterion (AIC) due to the increased temperature indexes for the $75^{th}$ percentile in the summers from 2001 to 2008 were compared and analyzed to define the best predictor. Results: For Seoul, all-cause mortality presented the highest percent increase (2.99% [95% CI, 2.43 to 3.54%]) in maximum temperature while AIC showed the lowest value when the all-cause daily death counts were fitted with the maximum PT for the $75^{th}$ percentile of summer. For Daegu, all-cause mortality presented the greatest percent increase (3.52% [95% CI, 2.23 to 4.80%]) in minimum temperature and AIC showed the lowest value in maximum temperature. No lag effect was found in the association between temperature and mortality for Seoul, whereas for Daegu one-day lag effect was noted. Conclusions: There was no one temperature measure that was superior to the others in summer. To adopt an appropriate temperature index, regional meteorological characteristics and the disease status of population should be considered.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7243-7249
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• 2013

Numerous studies have been conducted regarding association between TNF-${\alpha}$ and oral cancer risk, but the results remain controversial. The present meta-analysis is performed to acquire a more precise estimation of relationships. Databases of Pubmed, the Cochrane library and the China National Knowledge Internet (CNKI) were retrieved until August 10, 2013. Pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated with fixed- or random-effect models. The heterogeneity assumption was assessed by I-squared test. Among the eight included case-control studies, all were focused on TNF-${\alpha}$-308G>A and four also concerned the TNF-${\alpha}$-238G>A polymorphism. It was found that oral cancer risk were significant decreased with the TNF-${\alpha}$-308G>A polymorphism in the additive genetic model (GG vs. AA, OR=0.19, 95% CI: [0.04, 1.00], P=0.05, I2=68.9%) and the dominant genetic model (GG+GA vs. AA, OR=0.22, 95% CI: [0.06, 0.82], P=0.03, I2=52.4%); however, no significant association was observed in allele contrast (G vs. A, OR=0.70, 95% CI: [0.23, 2.16], P=0.54, I2=95.9%) and recessive genetic models (GG vs. GA+AA, OR=0.72, 95% CI: [0.33, 1.57], P=0.41, I2=93.1%). For the TNF-${\alpha}$-238G>A polymorphism, significant associations with oral cancer risk were found in the allele contrast (G vs. A, OR=2.75, 95% CI: [1.25, 6.04], P=0.01, I2=0.0%) and recessive genetic models (GG vs. GA+AA, OR=2.23, 95%CI: [1.18, 4.23], P=0.01, I2=0.0%). Conclusively, this meta-analysis indicates that TNF-${\alpha}$ polymorphisms may contribute to the risk of oral cancer. Allele G and the GG+GA genotype of TNF-${\alpha}$-308G>A may decrease the risk of oral cancer, while allele G and the GG genotype of TNF-${\alpha}$-238G>A may cause an increase.

• Journal of Preventive Medicine and Public Health
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• v.37 no.4
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• pp.300-305
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• 2004

Objectives : The exposure to particulate air pollution during the pregnancy has reported to result in adverse pregnancy outcome such as low birth weight, preterm birth, still birth, and intrauterine growth retardation (IUGR). We aim to assess whether prenatal exposure of particulate matter less than 10 (m in diameter ($PM_{10}$) is associated with preterm birth in Seoul, South Korea. Methods : We included 382,100 women who delivered a singleton at 25-42 weeks of gestation between 1998 and 2000. We calculated the average PM10 exposures for each trimester period and month of pregnancy, from the first to the ninth months, based on the birth date and gestational age. We used three different models to evaluate the effect of air pollution on preterm birth; the logistic regression model, the generalized additive logistic regression model, and the proportional hazard model. Results : The monthly analysis using logistic regression model suggested that the risks of preterm birth increase with PM10 exposure between the sixth and ninth months of pregnancy and the highest risk was observed in the seventh month (adjusted odds ratio=1.07, 95% CI=1.01-1.14). We also found the similar results using generalized additive model. In the proportional hazard model, the adjusted odds ratio for preterm births due to PM10 exposure of third trimester was 1.04 (95% CI=0.96-1.13) and PM10 exposure between the seventh month and ninth months of pregnancy was associated with the preterm births. Conclusions : We found that there were consistent results when we applied the three different models. These findings suggest that air pollution exposure during the third trimester pregnancy has an adverse effect on preterm birth in South Korea.

• Journal of Environmental Health Sciences
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• v.40 no.5
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• pp.346-354
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• 2014

Objectives: Although a number of epidemiologic studies have examined the association between air pollution and mortality, data limitations have resulted in fewer studies of particulate matter with an aerodynamic diameter of ${\leq}2.5{\mu}m$ ($PM_{2.5}$). We conducted a time-series study of the acute effects of particulate matter with an aerodynamic diameter of ${\leq}10{\mu}m$($PM_{10}$) and $PM_{2.5}$ on the increased risk of death for all causes and cardiovascular mortality in Seoul, Korea from 2006 to 2010. Methods: We applied the generalized additive model (GAM) with penalized splines, adjusting for time, day of week, holiday, temperature, and relative humidity in order to investigate the association between risk of mortality and particulate matter. Results: We found that $PM_{10}$ and $PM_{2.5}$ were associated with an increased risk of mortality for all causes and of cardiovascular mortality in Seoul. A $10{\mu}g/m^3$ increase in the concentration of $PM_{10}$ corresponded to 0.44% (95% Confidence Interval [CI]: 0.25-0.63%), and 0.95% (95% CI: 0.16-1.73%) increase of all causes and of cardiovascular mortality. A $10{\mu}g/m^3$ increase in the concentration of $PM_{2.5}$ corresponded to 0.76% (95% CI: 0.40-1.12%), and 1.63% (95% CI: 0.89-2.37%) increase of all causes and cardiovascular mortality. Conclusion: We conclude that $PM_{10}$ and $PM_{2.5}$ have an adverse effect on population health and that this strengthens the rationale for further limiting levels of $PM_{10}$ and $PM_{2.5}$ in Seoul.

• Nutrition Research and Practice
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• v.17 no.1
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• pp.62-72
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• 2023

BACKGROUND/OBJECTIVES: Many studies have revealed an association between fat mass and the obesity-related gene (FTO) and obesity. On the other hand, no meta-analysis was conducted with data from only Koreans. Therefore, this study performed a meta-analysis using Korean data to provide evidence for the association between FTO single nucleotide polymorphisms (SNPs) and the risk of obesity among Korean adults. SUBJECT/METHODS: Meta-analysis was finally conducted with data extracted from seven datasets of four studies performed on Korean adults after the screening passed. Five kinds of FTO SNPs (rs9939609, rs7193144, rs9940128, rs8050136, and rs9926289) were included, and the relationship between FTO SNPs and body mass index (BMI) was investigated using linear regression with an additive model adjusted for covariants, such as age, sex, and area. RESULTS: The minor alleles of FTO SNPs were associated with increased BMI (odds ratio [OR], 1.31; 95% confidence interval [CI], 1.21-1.42). In sub-group analysis, FTO rs9939609 T>A was significantly associated with BMI (OR, 1.23; 95% CI, 1.06-1.42). The other FTO SNPs together were significantly associated with BMI (OR, 1.37; 95% CI, 1.25-1.49). The publication bias was not observed based on Egger's test. CONCLUSIONS: This meta-analysis showed that minor alleles in the FTO SNPs were significantly associated with an increased BMI among Korean adults. This meta-analysis is the first to demonstrate that minor alleles in the FTO SNPs contribute significantly to the increased risk of obesity among Korean adults using data from a Korean population.

• Journal of Preventive Medicine and Public Health
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• v.49 no.5
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• pp.329-341
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• 2016

Objectives: The goal of this study was to investigate the short-term effect of ambient air pollution on emergency department (ED) visits in Seoul for asthma according to patients' prior history of allergic diseases. Methods: Data on ED visits from 2005 to 2009 were obtained from the Health Insurance Review and Assessment Service. To evaluate the risk of ED visits for asthma related to ambient air pollutants (carbon monoxide [CO], nitrogen dioxide [$NO_2$], ozone [$O_3$], sulfur dioxide [$SO_2$], and particulate matter with an aerodynamic diameter <$10{\mu}m$ [$PM_{10}$]), a generalized additive model with a Poisson distribution was used; a single-lag model and a cumulative-effect model (average concentration over the previous 1-7 days) were also explored. The percent increase and 95% confidence interval (CI) were calculated for each interquartile range (IQR) increment in the concentration of each air pollutant. Subgroup analyses were done by age, gender, the presence of allergic disease, and season. Results: A total of 33 751 asthma attack cases were observed during the study period. The strongest association was a 9.6% increase (95% CI, 6.9% to 12.3%) in the risk of ED visits for asthma per IQR increase in $O_3$ concentration. IQR changes in $NO_2$ and $PM_{10}$ concentrations were also significantly associated with ED visits in the cumulative lag 7 model. Among patients with a prior history of allergic rhinitis or atopic dermatitis, the risk of ED visits for asthma per IQR increase in $PM_{10}$ concentration was higher (3.9%; 95% CI, 1.2% to 6.7%) than in patients with no such history. Conclusions: Ambient air pollutants were positively associated with ED visits for asthma, especially among subjects with a prior history of allergic rhinitis or atopic dermatitis.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.339-346
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• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).