• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.23-30
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• 2020

In this study, the relationship between natural gas (NG) data and gas-related environmental elements was performed using machine learning algorithms to predict the level of gas leakage risk without directly measuring gas leakage data. The study was based on open data provided by the server using the IoT-based remote control Picarro gas sensor specification. The naturel gas leaks into the air, it is a big problem for air pollution, environment and the health. The proposed method is multivariate outlier removing method based Random Forest (RF) classification for predicting risk of NG leak. After, unsupervised k-means clustering, the experimental dataset has done imbalanced data. Therefore, we focusing our proposed models can predict medium and high risk so best. In this case, we compared the receiver operating characteristic (ROC) curve, accuracy, area under the ROC curve (AUC), and mean standard error (MSE) for each classification model. As a result of our experiments, the evaluation measurements include accuracy, area under the ROC curve (AUC), and MSE; 99.71%, 99.57%, and 0.0016 for MOL_RF respectively.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.223-232
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• 2015

The area under the ROC curve (AUC), the volume under the ROC surface (VUS) and the hypervolume under the ROC manifold (HUM) are defined and interpreted with probability that measures the discriminant power of classification models. AUC, VUS and HUM are expressed with the summation and integration notations for discrete and continuous random variables, respectively. AUC for discrete two random samples is represented as the nonparametric Mann-Whitney statistic. In this work, we define conditional Mann-Whitney statistics to compare more than two discrete random samples as well as propose that VUS and HUM are represented as functions of the conditional Mann-Whitney statistics. Three and four discrete random samples with some tie values are generated. Values of VUS and HUM are obtained using the proposed statistic. The values of VUS and HUM are identical with those obtained by definition; therefore, both VUS and HUM could be represented with conditional Mann-Whitney statistics proposed in this paper.

• YAKHAK HOEJI
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• v.48 no.4
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• pp.226-230
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• 2004

The pharmacokinetics of orally administered paclitlxel (50 mg/kg) was studied in six rabbits after 1hr pretreatment (2.0 mg/kg and 10 mg/kg) of tetramethoxyflavone or coadministration of (2.0 mg/kg, 10 mg/kg and 20 mg/kg) tetramethoxyflavone. The area under the plasma concentration-tine curve (AUC) and plasma concentration of paclitaxe1 coadministered with tetramethoxyflavone (10 mglkg) were increased significantly (p<0.05) compared with control. However, coadministration of tetramethoxyflavone (2 and 20 mg/kg) showed no significant effect on the pharmacokinetic parameters of paclitaxel. Pretreatment with tetramethoxyflavone significantly (p<0.05) increased the plasma concentration of paclitaxel. The area under the plasma concentration-time curve (AUC) and the peak concentration (C$_{max}$) of paclitaxel pretreated with tetramethoxyflavone were increased significantly (p<0.01, p<0.05) compared with control. The terminal half. life of paclitaxel pretreated with tetramethoxyflavone (2 mg/kg and 10 mg/kg) was significantly (p<0.05) prolonged compared with control. Pretreatment with tetramethoxyflavone (2.0 mg/kg, 10 mg/kg) significantly (p<0.01, p<0.05) increased the absolute bioavailability of paclitaxel compared with the control (154∼179%). On the basis of the results, it might be considered that tetramethoxyflavone may inhibit cytochrome P450 or P-glycoprotein efflux pump which are engaged in paclitaxel metabolism, result in increased AUC and t$_{1}$2/ of paclitaxel. However, further study should be conducted to clarify the roles of cytochrome P450 and P-glycoprotein on paclitaxel bioavailability with/or without tetramethoxyflavone. P-glycoprotein on paclitaxel bioavailability with/or without tetramethoxyflavone.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.46 no.4
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• pp.371-382
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• 2020

There have been continuous attempts to quantify sensory attributes of cosmetic products by measuring relevant physical properties. The most representative method to evaluate stickiness is to measure axial force using texture analyzer. Stickiness is known to correlate with AUC which abbreviates area under curve in the obtained axial force curve as a function of time. Recently, Normandie University research group developed in vivo stickiness evaluation method considering the characteristics of skin along with established evaluation method[8]. Based on the study, we tried to optimize in vivo stickiness evaluation method especially for cosmetic creams. The experiment was carried out on 5 different facial creams products by changing the amount and the times of rolling of creams, and the shape and material of probes. Based on the results of the sensory evaluation, the most consistent conditions were established as the optimal evaluation method. As a result, applying 70 μL of cream and rubbing 10 times for 7 s inside the 3.4 cm circle were judged to be suitable. As for the probes, spherical metallic probe was more proper due to its reproducibility. We conducted the settled method on 10 subjects to check its validity. Although the absolute values of AUC differed depending on the individuals, the AUC values were all ranked the same. Finally, for the standardization of stickiness of AUC, polyvinylpyrrolidone (PVP) was set as a reference material and we measured AUC of its aqueous solution by changing concentration. Then, the degree of stickiness recognition for 5 different creams was surveyed to check the correlation between AUC and stickiness.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.203-212
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• 2024

The area under the ROC curve (AUC) is one of the most common criteria used to measure the overall performance of binary classifiers for a wide range of machine learning problems. In this article, we propose a L₁-penalized AUC-optimization classifier that directly maximizes the AUC for high-dimensional data. Toward this, we employ the AUC-consistent surrogate loss function and combine the L₁-norm penalty which enables us to estimate coefficients and select informative variables simultaneously. In addition, we develop an efficient optimization algorithm by adopting k-means clustering and proximal gradient descent which enjoys computational advantages to obtain solutions for the proposed method. Numerical simulation studies demonstrate that the proposed method shows promising performance in terms of prediction accuracy, variable selectivity, and computational costs.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.721-738
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• 2018

The purpose of this study is to create landslide vulnerability using frequency ratio (FR) and evidential belief functions (EBF) model which are two methods of probability model and to select appropriate model for each region through comparison of results in Sacheon-myeon and Jumunjin-eup of Gangneung. 762 locations in Sacheon-myeon and 548 landscapes in Jeonju-eup were constructed based on the interpretation of aerial photographs. Half of each landslide point was randomly selected for modeling and remaining landslides were used for verification purposes. Twenty landslide-inducing factors classified into five categories such as topographic elements, hydrological elements, soil maps (1:5,000), forest maps (1:5,000), and geological maps (1:25,000) were considered for the preparation of landslide vulnerability in the study. The relationship between landslide occurrence and landslide inducing factors was analyzed using FR and EBF models. The two models were then verified using the AUC (curve under area) method. According to the results of verification, the FR model (AUC = 81.2%) was more accurate than the EBF model (AUC = 78.9%) at Jeonjun-eup. In the Sacheon-myeon, the EBF model (AUC = 83.6%) was more accurate than the FR model (AUC = 81.6%). Verification results show that FR model and EBF model have high accuracy with accuracy of around 80%.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.593-605
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• 2019

Significant literature exists on the area under the ROC curve (AUC) and the volume under the ROC surface (VUS) which are statistical measures of the discriminant power of classification models. Whereas the partial AUC is restricted on the false positive rate, the two-way partial AUC is restricted on both the false positive rate and true positive rate, which could be more efficient and accurate than partial AUC. The two-way partial AUC was suggested as more efficient and accurate than the partial AUC. Partial VUS as well as the three-way partial VUS were also developed for the ROC surface. A proposed AUC is expressed in this paper with probability and integration using two truncated distribution functions restricted on both the false positive rate and true positive rate. It is also found that this AUC has a relation with the two-way partial AUC. The three-way partial VUS for the ROC surface is also related to the VUS using truncated distribution functions. These AUC and VUS are represented and estimated in terms of Mann-Whitney statistics. Their parametric and non-parametric estimation methods are explored based on normal distributions and random samples.

• YAKHAK HOEJI
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• v.29 no.4
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• pp.216-219
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• 1985

The phormacokinetics of acetaminophen were investigated in rabbits with folate-induced renal failure. The blood level, the area under the blood concentraction curve(AUC) and the biological half-life were increased significantly, and the urinary excretion was decreased significantly as compared with those of normal rabbits. Serum creatinine concentration and AUC, creatinine clearance and renal clearance have linear relationship respectively. Dosage regimen of acetaminophen was considered to be adjusted in renal failure.

• Journal of Pharmaceutical Investigation
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• v.16 no.4
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• pp.152-157
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• 1986

The pharmacokinetics of sulfamethoxazole were investigated in rabbits with folate-induced renal failure. The blood level, area under the blood concentration curve (AUC) and biological half-life were increased significantly, and the urinary excretion was decreased significantly compared with those of normal rabbits. Correlation of serum creatinine concentration and AUC, biological half-life, and correlation of creatinine clearance and renal clearance have linear relationship respectively. From these results, dosage regimen of sulfamethoxazole is considered to be adjusted for effective and safe therapy in renal failure.

• The Korean Journal of Pain
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• v.33 no.1
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• pp.54-59
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• 2020

Background: The median nerve cross-sectional area (MNCSA) is a useful morphological parameter for the evaluation of carpal tunnel syndrome (CTS). However, there have been limited studies investigating the anatomical basis of median nerve flattening. Thus, to evaluate the connection between median nerve flattening and CTS, we carried out a measurement of the median nerve thickness (MNT). Methods: Both MNCSA and MNT measurement tools were collected from 20 patients with CTS, and from 20 control individuals who underwent carpal tunnel magnetic resonance imaging (CTMRI). We measured the MNCSA and MNT at the level of the hook of hamate on CTMRI. The MNCSA was measured on the transverse angled sections through the whole area. The MNT was measured based on the most compressed MNT. Results: The mean MNCSA was 9.01 ± 1.94 ㎟ in the control group and 6.58 ± 1.75 ㎟ in the CTS group. The mean MNT was 2.18 ± 0.39 mm in the control group and 1.43 ± 0.28 mm in the CTS group. Receiver operating characteristics curve analysis demonstrated that the optimal cut-off value for the MNCSA was 7.72 ㎟, with 75.0% sensitivity, 75.0% specificity, and an area under the curve (AUC) of 0.82 (95% confidence interval [CI], 0.69-0.95). The best cut off-threshold of the MNT was 1.76 mm, with 85% sensitivity, 85% specificity, and an AUC of 0.94 (95% CI, 0.87-1.00). Conclusions: Even though both MNCSA and MNT were significantly associated with CTS, MNT was identified as a more suitable measurement parameter.