• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1521-1530
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• 2014

The ROC analysis is considered for multiple class diagnosis. There exist many criteria to find optimal thresholds and measure the accuracy of diagnostic tests for k dimensional ROC analysis. In this paper, we proposed a diagnostic accuracy measure called the correct classification simple rate, which is defined as the summation of true rates for each classification distribution and expressed as a function of summation of sequential true rates for two consecutive distributions. This measure does not weight accuracy across categories by the category prevalence and is comparable across populations for multiple class diagnosis. It is found that this accuracy measure does not only have a relationship with Kolmogorov - Smirnov statistics, but also can be represented as a linear function of some optimal threshold criteria. With these facts, the suggested measure could be applied to test for comparing multiple distributions.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.99-104
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.6
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• pp.311-322
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• 2018

As a method of seismic-design for pile-supported wharves, equivalent static analysis, response spectrum analysis, and time history analysis method are applied. Among them, the response spectrum analysis is widely used to obtain the maximum response of a structure. Because the ground is not modeled in the response spectrum analysis of pile-supported wharves, the amplified input ground acceleration should be calculated by ground classification or seismic response analysis. However, it is difficult to calculate the input ground acceleration through ground classification because the pile-supported wharf is build on inclined ground, the methods to calculate the input ground acceleration proposed in the standards are different. Therefore, in this study, the dynamic centrifuge model tests and the response spectrum analysis were carried out to calculate the appropriate input ground acceleration. The pile moment in response spectrum analysis and the dynamic centrifuge model tests were compared. As a result of comparison, it was shown that the response spectrum analysis results using the amplified acceleration in the ground surface were appropriate.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.928-935
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• 2009

Ontology schema reasoning is used to maintain consistency of concepts and build concept hierarchy automatically. For the purpose, the search of concepts must be inevitably performed. Ontology schema reasoning performs the test of subsumption relationships of all the concepts delivered in the test set. The result of subsumption tests is determined based on the creation of complete graphs, which seriously weighs with the performance of reasoning. In general, the process of creating complete graph has been known as expressive procedure. This process is essential in improving the leading performance. In this paper, we propose a method enhancing the classification performance by identifying unnecessary subsumption test supported by optimized searching method on subsumption relationship test among concepts. It is achieved by propagating subsumption tests results into other concept.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.147-157
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• 2018

There are increasing demands to provide early warning against intruding drones and cope with potential threats. Commercial anti-drone systems are mostly based on simple target detection by radar reflections. In real scenario, however, it becomes essential to obtain drone radar signatures so that hostile targets are recognized in advance. We present experimental test results that micro-Doppler radar signature delivers partial information on multi-rotor platforms and exhibits limited performance in drone recognition and classification. Afterward, we attempt to generate high resolution profile of flying drone targets. To this purpose, wide bands radar signals are employed to carry out inverse synthetic aperture radar(ISAR) imaging against moving drones. Following theoretical analysis, experimental field tests are carried out to acquire real target signals. Our preliminary tests demonstrate that high resolution ISAR imaging provides effective measures to detect and classify multiple drone targets in air.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1263-1269
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• 2016

Hearing impaired persons are exposed to the danger since they can't be aware of many dangerous situations like fire alarms, car hones and so on. Therefore they need haptic or visual informations when they meet dangerous situations. In this paper, we design a dangerous sound detection engine for hearing impaired. We consider four dangerous indoor situations such as a boiled sound of kettle, a fire alarm, a door bell and a phone ringing. For outdoor, two dangerous situations such as a car horn and a siren of emergency vehicle are considered. For a test, 6 data sets are collected from those six situations. we extract LPC, LPCC and MFCC as feature vectors from the collected data and compare the vectors for feasibility. Finally we design a matching engine using an artificial neural network and perform classification tests. We perform classification tests for 3 times considering the use outdoors and indoors. The test result shows the feasibility for the dangerous sound detection.

• Journal of Information Technology Services
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• v.20 no.3
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• pp.75-86
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• 2021

As artificial intelligence is actively studied, chatbot systems are being applied to various fields. In particular, many chatbot systems for psychological counseling have been studied that can comfort modern people. However, while most psychological counseling chatbots are studied as rule-base and deep learning-based chatbots, there are large limitations for each chatbot. To overcome the limitations of psychological counseling using such chatbots, we proposes a novel psychological counseling AI chatbot system. The proposed system consists of a GPT-2 model that generates output sentence for Korean input sentences and an Electra model that serves as sentiment analysis and anxiety cause classification, which can be provided with psychological tests and collective intelligence functions. At the same time as deep learning-based chatbots and conversations take place, sentiment analysis of input sentences simultaneously recognizes user's emotions and presents psychological tests and collective intelligence solutions to solve the limitations of psychological counseling that can only be done with chatbots. Since the role of sentiment analysis and anxiety cause classification, which are the links of each function, is important for the progression of the proposed system, we experiment the performance of those parts. We verify the novelty and accuracy of the proposed system. It also shows that the AI chatbot system can perform counseling excellently.

• Journal of Korean Critical Care Nursing
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• v.5 no.2
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• pp.49-60
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• 2012

Purpose: The purpose of this study was to explore health care workers awareness and performance of standard precautions. Methods: Participants were 296 health care workers including nurses, physicians, and medical technicians. Awareness and performance of standard precautions were measured with 4-point Likert scales. The data were analyzed with t-tests and one-way ANOVA by using SPSS 18.0. Results: The mean scores of awareness were 3.72 in nurses, 3.62 in physicians, and 3.47 in medical technicians. There was a significant difference of awareness by occupational classification (F=12.39, p<.001). The mean scores of performance of standard precautions were 3.45 in nurses, 3.19 in physicians, and 3.23 in medical technicians. There was a significant difference of performance by occupational classification (F=10.98, p<.001). In addition, the score of performance of standard precautions was significantly lower than that of awareness (t=11.89, p<.001). Conclusion: The results of this study indicated that awareness and performance of standard precautions were different by occupational classification. To improve performance of standard precautions in hospitals, it is necessary to provide a distinct infection control program by occupational classification.

• Molecules and Cells
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• v.22 no.1
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• pp.78-88
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• 2006

The phylogeny of the subfamily Tephritinae (Diptera: Tephritidae) was reconstructed from mitochondrial 16S ribosomal RNA gene sequences using 53 species representing 11 currently recognized tribes of the Tephritinae and 10 outgroup species. The minimum evolution and Bayesian trees suggested the following phylogenetic relationships: (1) monophyly of the Tephritinae was strongly supported; (2) a sister group relationship between the Tephritinae and Plioreocepta was supported by the Bayesian tree; (3) the tribes Tephrellini, Myopitini, and Terelliini (excluding Neaspilota) were supported as monophyletic groups; (4) the non-monophyletic nature of the tribes Dithrycini, Eutretini, Noeetini, Tephritini, Cecidocharini, and Xyphosiini; and (5) recognition of 10 putative tribal groups, most of which were supported strongly by the statistical tests of the interior branches. Our results, therefore, convincingly suggest that an extensive rearrangement of the tribal classification of the Tephritinae is necessary. Since our sampling of taxa heavily relied on the current accepted classification, some lineages identified by the present study were severely under-sampled and other possible major lineages of the Tephritinae were probably not even represented in our dataset. We believe that our results provide baseline information for a more rigorous sampling of additional taxa representing all possible major lineages of the subfamily, which is essential for a comprehensive revision of the tephritine tribal classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12B
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• pp.2447-2454
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• 1999

This paper proposes a N-APPEM(Nonlinear A Posteriori Probability Estimation Method) with a frame normalization method to conventional classification network to increase speech recognition ability. It also tests the recognition ability of the classification and prediction neural networks for the Korean isolated digits. From the experimental results, the prediction network with MLP(Multi-Layer Perceptron) achieves the highest recognition ability of 98.0%. The prediction requires very complicated networks increased linearly with the number of incoming speech categories. However, the classification network with the N-APPEM and the normalization improves the recognition ability up to 85.5% with a sin81e network, which is almost 12.0% improvement.