• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.7
• /
• pp.49-55
• /
• 2016

In digital forensic images, there is a serious problem that is distributed with various image types. For the problem solution, this paper proposes a classification algorithm of the forensic image types. The proposed algorithm extracts the 21-dim. feature vector with the contrast and energy from GLCM (Gray Level Co-occurrence Matrix), and the entropy of each image type. The classification test of the forensic images is performed with an exhaustive combination of the image types. Through the experiments, TP (True Positive) and FN (False Negative) is detected respectively. While it is confirmed that performed class evaluation of the proposed algorithm is rated as 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic Curve) is 0.9980 by the sensitivity and the 1-specificity. Also, the minimum average decision error is 0.1349. Also, at the minimum average decision error is 0.0179, the whole forensic image types which are involved then, our classification effectiveness is high.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.3
• /
• pp.75-81
• /
• 2014

In a distribution of the digital image, there is a serious problem that is distributed an illegal forgery image by pirates. For the problem solution, this paper proposes an image forensic decision algorithm using an edge energy information of forgery image. The algorithm uses SA (Streaking Artifacts) and SPAM (Subtractive Pixel Adjacency Matrix) to extract the edge energy informations of original image according to JPEG compression rate(QF=90, 70, 50 and 30) and the query image. And then it decides the forge whether or not by comparing the edge informations between the original and query image each other. According to each threshold in TCJCR (Threshold by Combination of JPEG Compression Ratios), the matching of the edge informations of original and query image is excused. Through the matching experiments, TP (True Positive) and FN (False Negative) is 87.2% and 13.8% respectively. Thus, the minimum average decision error is 0.1349. Also, it is confirmed that the performed class evaluation of the proposed algorithm is 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic) curve is 0.9388 by sensitivity and 1-specificity.

• Journal of Korean Academy of Nursing
• /
• v.35 no.1
• /
• pp.47-55
• /
• 2005

Purpose: This study was to evaluate the validity of the Pediatric Index of Mortality Ⅱ(PIM Ⅱ). Method: The first values on PIM Ⅱ variables following ICU admission were collected from the patient's charts of 548 admissions retrospectively in three ICUs(medical, surgical, and neurosurgical) at P University Hospital and a cardiac ICU at D University Hospital in Busan from January 1, 2002 to December 31, 2003. Data was analyzed with the SPSSWIN 10.0 program for the descriptive statistics, correlation coefficient, standardized mortality ratio(SMR), validity index(sensitivity, specificity, positive predictive value, negative predictive value), and AUC of ROC curve. Result: The mortality rate was 10.9% (60 cases) and the predicted death rate was 9.5%. The correlation coefficient(r) between observed and expected death rates was .929(p<.01) and SMR was 1.15. Se, Sp, pPv, nPv, and the correct classification rate were .80, .96, .70, .98, and 94.0% respectively. In addition, areas under the curve (AUC) of the receiver operating characteristic(ROC) was 0.954 (95% CI=0.919～0.989). According to demographic characteristics, mortality was underestimated in the medical group and overestimated in the surgical group. In addition, the AUCs of ROC curve were generally high in all subgroups. Conclusion: The PIM Ⅱ showed a good, so it can be utilized for the subject hospital. better.

• Communications for Statistical Applications and Methods
• /
• v.16 no.6
• /
• pp.1005-1012
• /
• 2009

The area under the curve(AUC) is commonly used as a measure of the receiver operating characteristic(ROC) curve which displays the performance of a set of binary classifiers for all feasible ratios of the costs associated with true positive rate(TPR) and false positive rate(FPR). In the bipartite ranking problem where one has to compare two different observations and decide which one is "better", the AUC measures the quantity that ranking score of a randomly chosen sample in one class is larger than that of a randomly chosen sample in the other class and hence, the function which maximizes an AUC of bipartite ranking problem is different to the function which maximizes (minimizes) accuracy (misclassification error rate) of binary classification problem. In this paper, we develop a way to construct the unified framework for AUC maximizer including support vector machines based on maximizing large margin and logistic regression based on estimating posterior probability. Moreover, we develop an efficient algorithm for the proposed unified framework. Numerical results show that the propose unified framework can treat various methodologies successfully.

• Economic and Environmental Geology
• /
• v.46 no.4
• /
• pp.301-312
• /
• 2013

Landslides are caused by complex interaction among a large number of interrelated factors such as topography, geology, forest and soils. In this study, a comparative study was carried out using fuzzy relationship method and artificial neural network to evaluate landslide susceptibility. For landslide susceptibility mapping, maps of the landslide occurrence locations, slope angle, aspect, curvature, lithology, soil drainage, soil depth, soil texture, forest type, forest age, forest diameter and forest density were constructed from the spatial data sets. In fuzzy relation analysis, the membership values for each category of thematic layers have been determined using the cosine amplitude method. Then the integration of different thematic layers to produce landslide susceptibility map was performed by Cartesian product operation. In artificial neural network analysis, the relative weight values for causative factors were determined by back propagation algorithm. Landslide susceptibility maps prepared by two approaches were validated by ROC(Receiver Operating Characteristic) curve and AUC(Area Under the Curve). Based on the validation results, both approaches show excellent performance to predict the landslide susceptibility but the performance of the artificial neural network was superior in this study area.

• Clinical and Experimental Reproductive Medicine
• /
• v.36 no.4
• /
• pp.255-263
• /
• 2009

Objective: The objective of the study was to determine the diagnostic performance of transabdominal ultrasound by receiver operating characteristic (ROC) curve analysis, in order to evaluate the usefulness in establishing the diagnosis of polycystic ovary syndrome (PCOS). Methods: Questionnaires were given to 8,793 reproductive women reviewed at Ewha Womans University Mokdong hospital. Ultrasound examinations were performed in 701 women with a transabdominal transducer. Transabdominal ultrasounds were performed in 185 normal control women (normal menstruation without hyperandrogenism or PCO morphology) and 248 PCOS patients according to National Institutes of Health (NIH) PCOS diagnosis criteria. ROC curves were calculated for ovarian volume and follicle number. Results: In normal control group, the mean age were $23.64{\pm}4.26$ years old and the mean ovarian volume and follicle number were $6.03{\pm}1.89\;cm^3$ and $6.49{\pm}1.93$, respectively. The ovarian volume showed an area under the ROC curve (AURC) of 0.761. A ovarian volume decision threshold >$9\;cm^3$ had a sensitivity of 51.0% and a specificity of 91.4% for the diagnosis of PCOS. The follicle number showed an AURC of 0.733. A follicle number decision threshold ${\geq}9$ had a sensitivity of 54.9% and a specificity of 87.0% for the diagnosis of PCOS. A follicle number decision threshold ${\geq}10$ had a sensitivity of 53.2% and a specificity of 90.4%. A follicle number and a ovarian volume did not have a high diagnostic power for screening for PCOS. Conclusion: Our results suggest that transabdominal ultrasound assessment is not effective for the detection of PCOS in young women of reproductive age.

• Journal of Microbiology and Biotechnology
• /
• v.24 no.8
• /
• pp.1044-1050
• /
• 2014

Since there is no consensus about the most reliable assays to detect invasive aspergillosis from samples obtained by minimally invasive or noninvasive methods, we compared the efficacy of an enzyme-linked immunosorbent assay (ELISA) for galactomannan (GM) detection and quantitative real-time PCR assay (qRT-PCR) for the diagnosis of invasive pulmonary aspergillosis. Neutropenic, male Sprague-Dawley rats (specific pathogen free; 8 weeks old; weight, $200{\pm}20g$) were immunosuppressed with cyclophosphamide and infected with Aspergillus fumigatus intratracheally. Tissue and whole blood samples were harvested on days 1, 3, 5, and 7 post-infection and examined with GM ELISA and qRT-PCR. The A. fumigatus DNA detection sequence was detected in the following number of samples from 12 immunosuppressed, infected rats examined on the scheduled days: day 1 (0/12), day 3 (0/12), day 5 (6/12), and day 7 (8/12) post-infection. The sensitivity and specificity of the qRT-PCR assay was 29.2% and 100%, respectively. Receiver operating characteristic curve (ROC) analysis indicated a Ct (cycle threshold) cut-off value of 15.35, and the area under the curve (AUC) was 0.627. The GM assay detected antigen in sera obtained on day 1 (5/12), day 3 (9/12), day 5 (12/12), and day 7 (12/12) post-infection, and thus had a sensitivity of 79.2% and a specificity of 100%. The ROC of the GM assay indicated that the optimal Ct cut-off value was 1.40 (AUC, 0.919). The GM assay was more sensitive than the qRT-PCR assay in diagnosing invasive pulmonary aspergillosis in rats.

• Journal of the Korea Convergence Society
• /
• v.13 no.1
• /
• pp.399-408
• /
• 2022

The purpose of this study was to compare the usefulness of the lipid ratio indicators for the diagnosis of metabolic syndrome in the elderly aged 65 years or older. From January 2018 to December 2020, 1,464 people aged 65 years or older who underwent a health checkup at a general hospital in Seoul were included. Lipid ratio indicators were measured through blood tests. The prevalence of metabolic syndrome according to the quartiles of the lipid ratio index was confirmed by logistic regression analysis. In addition, the metabolic syndrome predictive ability and cutoff value of the lipid ratio indices were estimated with the receiver operating characteristic(ROC) curve. The correlation between atherogenic index of plasma(AIP) and waist circumference was the highest in both men and women(r=0.278, p<0.001 vs r=0.252, p<0.001). As for the lipid ratio indices, the incidence of metabolic syndrome was higher in the fourth quartile than in the first quartile. The area under the ROC curve(AUC) value of AIP was higher at 0.826(95% CI=0.799-0.850) and 0.852(95% CI=0.820-0.881) for men and women, respectively, compared to other lipid ratio indicators, and the optimal cutoff values for both men and women was 0.44(p<0.001). Therefore, the AIP among the lipid ratio indicators was found to be the most useful index for diagnosing metabolic syndrome in the elderly aged 65 years or older.

• Journal of the Korean Society of Radiology
• /
• v.85 no.1
• /
• pp.184-196
• /
• 2024

Purpose We investigated potentially promising imaging findings and their combinations in the evaluation of cognitive decline. Materials and Methods This retrospective study included 138 patients with subjective cognitive impairments, who underwent brain MRI. We classified the same group of patients into Alzheimer's disease (AD) and non-AD groups, based on the neuropsychiatric evaluation. We analyzed imaging findings, including white matter hyperintensity (WMH) and cerebral microbleeds (CMBs), using the Kruskal-Wallis test for group comparison, and receiver operating characteristic (ROC) curve analysis for assessing the diagnostic performance of imaging findings. Results CMBs in the lobar or deep locations demonstrated higher prevalence in the patients with AD compared to those in the non-AD group. The presence of lobar CMBs combined with periventricular WMH (area under the ROC curve [AUC] = 0.702 [95% confidence interval: 0.599-0.806], p < 0.001) showed the highest performance in differentiation of AD from non-AD group. Conclusion Combinations of imaging findings can serve as useful additive diagnostic tools in the assessment of cognitive decline.

• Clinical and Experimental Pediatrics
• /
• v.60 no.9
• /
• pp.290-295
• /
• 2017

Purpose: Airway pathology in children with atopic asthma can be reflected by the concave shape of the maximal expiratory flow-volume (MEFV) curve and high fractional exhaled nitric oxide (FeNO) values. We evaluated the capacity of the curvilinearity of the MEFV curve, FeNO, and their combination to distinguish subjects with atopic asthma from healthy individuals. Methods: FeNO and angle ${\beta}$, which characterizes the general configuration of the MEFV curve, were determined in 119 steroid-naïve individuals with atopic asthma aged 8 to 16 years, and in 92 age-matched healthy controls. Receiver operating characteristic (ROC) curve analyses were performed to determine the cutoff points of FeNO and angle ${\beta}$ that provided the best combination of sensitivity and specificity for asthma detection. Results: Asthmatic patients had a significantly smaller angle ${\beta}$ and higher FeNO compared with healthy controls (both, P<0.001). For asthma detection, the best cutoff values of angle ${\beta}$ and FeNO were observed at $189.3^{\circ}$ and 22 parts per billion, respectively. The area under the ROC curve for the combination of angle ${\beta}$ and FeNO improved to 0.91 (95% confidence interval [CI], 0.87-0.95) from 0.80 (95% CI, 0.75-0.86; P<0.001) for angle ${\beta}$ alone and 0.86 (95% CI, 0.82-0.91; P=0.002) for FeNO alone. In addition, the combination enhanced sensitivity with no significant decrease in specificity. Conclusion: These data suggest that the combined use of the curvilinearity of the MEFV curve and FeNO is a useful tool to differentiate between children with and without atopic asthma.