• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.161-169
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• 2021

The two-dimensional confusion matrix used in credit assessment, biostatistics, and many other fields consists of true positive, true negative, false positive, and false negative. Their rates, such as the true positive rate (TPR), true negative rate (TNR), false positive rate, and false negative rate, can be applied to measure its accuracy. In this study, we propose the TPR-TNR plot, a graphical method that can geometrically describe and explain these rates based on the confusion matrix. The proposed TPR-TNR plot consists of two right-angled triangles. We obtain that the TPR and TNR describe the acute angles of right-angled triangles in the plot. These acute angles can be used to determine optimal thresholds corresponding to lots of accuracy measures.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.215-217
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• 2008

The most widely used method for searching contents of mot ion picture compares contents by extracted cuts. The cut extract ion methods, such as CHD(Color Histogram Difference) or ECR(Edge Change Ratio), are very weak at modifications such as cropping, resizing and low bit rate. The suggested method uses audio contents for indexing and searching to make search be robust against these modification. Scenes of audio contents are extracted for modification-robust search. And based on these scenes, make spectral powers binary on each frequency bin. in the time-frequency domain. The suggested method shows failure rate less than 1% on the false positive error and the true negative error to the modified(using cropping, clipping, row bit rate, addtive frame) contents.

• Journal of information and communication convergence engineering
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• v.7 no.1
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• pp.7-12
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• 2009

The advanced computer network and Internet technology enables connectivity of computers through an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and can not detect new hacking patterns, making it vulnerable to previously unidentified attack patterns and variations in attack and increasing false negatives. Intrusion detection and prevention technologies are thus required. We proposed a network based hybrid Probe Intrusion Detection model using Fuzzy cognitive maps (PIDuF) that detects intrusion by DoS (DDoS and PDoS) attack detection using packet analysis. A DoS attack typically appears as a probe and SYN flooding attack. SYN flooding using FCM model captures and analyzes packet information to detect SYN flooding attacks. Using the result of decision module analysis, which used FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. For the performance evaluation, the "IDS Evaluation Data Set" created by MIT was used. From the simulation we obtained the max-average true positive rate of 97.064% and the max-average false negative rate of 2.936%. The true positive error rate of the PIDuF is similar to that of Bernhard's true positive error rate.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.255-260
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• 2015

Among many optimal threshold criteria from ROC curve, the closest-to-(0,1) and amended closest-to-(0,1) criteria are considered. An ROC curve that passes close to the (0,1) point indicates that two models are well classified. In this case, the ROC curve is located far from the (1,0) point. Hence we propose two criteria: the farthest-to-(1,0) and amended farthest-to-(1,0) criteria. These criteria are found to have a relationship with the KolmogorovSmirnov statistic as well as some optimal threshold criteria. Moreover, we derive that a definition for the proposed criteria with more than two dimensions and with relations to multi-dimensional optimal threshold criteria.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.3
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• pp.113-120
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• 2020

The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection. The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000 validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120 pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6 performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting concrete cracks.

• Journal of Acupuncture Research
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• v.24 no.6
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• pp.187-193
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• 2007

Objectives : The objectives of this study were to investigate the relationship between electrical detection of ear acupuncture points and musculoskeletal pain. Methods : 18 adults who have musculoskeletal pain without trauma factorsparticipated in this study. They answered the questionnaire and their ear acupuncture points were examined with electrical detectors. We analyzed the relationship between electrical detection of ear acupuncture points and musculoskeletal pain with concordance rate and validity. Results : Total concordance rates of the head region was 68.00%(questionnaire) 32.08%(investigation), that of vertebral region was 67.86%, 59.38%, that of both upper limbs was 86.67%, 39.69%, and that of both lower limbs was 50.00%, 23.46%. The true positive rate was 0.704, the true negative rate was 0.492, the false positive rate was 0.508, and the false negative rate was 0.296 in the validity test. In the head, two concordance rates of the temporal and occipital regions were relatively higher than those of the parietal and frontal regions. In the vertebral region, two concordance rates of the cervical and lumbar regions were relatively higher than those of the thoracic and sacrum regions. In the upper limb, two concordance rates of the shoulder and shoulder joints were relatively higher than those of the others. In the lower limb, concordance rates of investigation were relatively low at all areas. The right lower limb was relatively higher than the left in concordance rates of the questionnaire. Conclusions : The results suggest that electrical detection of ear acupuncture points can be used in the diagnosis and treatment of musculoskeletal pain.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.4
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• pp.811-817
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• 2009

The rapid growth of network based IT systems has resulted in continuous research of security issues. Probe intrusion detection is an area of increasing concerns in the internet community. Recently, a number of probe intrusion detection schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems, are useful only for the existing patterns of probe intrusion. They can not detect new patterns of probe intrusion. Therefore, it is necessary to develop a new Probe Intrusion Detection technology that can find new patterns of probe intrusion. In this paper, we proposed a new network based probe intrusion detector(NePID) using anomaly traffic analysis and fuzzy cognitive maps that can detect intrusion by the denial of services attack detection method utilizing the packet analyses. The probe intrusion detection using fuzzy cognitive maps capture and analyze the packet information to detect syn flooding attack. Using the result of the analysis of decision module, which adopts the fuzzy cognitive maps, the decision module measures the degree of risk of denial of service attack and trains the response module to deal with attacks. For the performance evaluation, the "IDS Evaluation Data Set" created by MIT was used. From the simulation we obtained the max-average true positive rate of 97.094% and the max-average false negative rate of 2.936%. The true positive error rate of the NePID is similar to that of Bernhard's true positive error rate.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.314-333
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• 2014

When analyzing default predictions in real estate companies, the number of non-defaulted cases always greatly exceeds the defaulted ones, which creates the two-class imbalance problem. This lowers the ability of prediction models to distinguish the default sample. In order to avoid this sample selection bias and to improve the prediction model, this paper applies a minority sample generation approach to create new minority samples. The logistic regression, support vector machine (SVM) classification, and neural network (NN) classification use an imbalanced dataset. They were used as benchmarks with a single prediction model that used a balanced dataset corrected by the minority samples generation approach. Instead of using prediction-oriented tests and the overall accuracy, the true positive rate (TPR), the true negative rate (TNR), G-mean, and F-score are used to measure the performance of default prediction models for imbalanced dataset. In this paper, we describe an empirical experiment that used a sampling of 14 default and 315 non-default listed real estate companies in China and report that most results using single prediction models with a balanced dataset generated better results than an imbalanced dataset.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.2
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• pp.1-14
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• 2022

The implications of bank-specific risks and macroeconomic risks on the growth, profitability, and stability of Islamic and conventional banks are examined and compared in this article. The study also investigates whether corporate governance mitigates the effects of both bank-specific and macroeconomic risks on Islamic and conventional banks' development, profitability, and stability. For the period 2007-2019, we examined a panel data set of 22 banks in Pakistan, including both Islamic and conventional banks. We discovered considerable evidence that both bank-specific risks and macroeconomic risks have negative effects on the growth, profitability, and stability of Pakistani banks using a dynamic panel data estimator, the two-step Generalized Method of Moments (GMM) approach. Furthermore, the findings show that bank-specific and macroeconomic risks have different consequences in both types of banking. The impacts of liquidity risk, operational risk, capital risk, inflation risk, and exchange rate risk are higher for Islamic banks than for conventional banks. Conventional banks, on the other hand, are more vulnerable to credit risk and interest rate risk. Finally, the findings show that good corporate governance reduces the negative consequences of both categories of risks on bank development, profitability, and stability. This is true for Islamic and conventional banks alike.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.50-59
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• 2017

To evaluate the capability of integrated PET-MRI, which has recently been utilized in the clinical practices, on the diagnosis of liver cancer, its utility was assessed by $2{\times}2$ decision matrix. The numbers of abnormal and normal decisions on the liver cancer were 98 and 51 cases, respectively, upon PET-MRI scan results of the subjects, and the numbers of positive and negative decisions were 103 and 62, respectively, upon cytopathologic results. Out of the two tests, 95 cases were shown as true-positive and 3 were false positive, while 62 were true negative and 5 were false negative. Upon the results of PET-MRI test, its sensitivity, specificity, false negative rate, and false positive rate were 95.00%, 95.38%, 0.05%, and 95.15%, respectively. Therefore, it is considered to have the high potential to use the determination of the stage before the surgery, detections of recurrence and remote metastasis, assessment of uncertain remote lymph node metastasis, and so on in the diagnosis of the liver cancer, and also for the clinical utility of PET-MRI to be sufficient by integrated diagnosis and follow up scan with pathological studies.