• Imaging Science in Dentistry
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• v.43 no.3
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• pp.153-161
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• 2013

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. Materials and Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. Results: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Conclusion: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.88-90
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• 2018

There are many studies on IR-UWB Radar. A number of studies have been conducted on the Personnel count and measurement distance to person, mainly using IR-UWB. In this paper, however, we use IR-UWB Radar to distinguish objects. In order to distinguish these objects, in this paper, the IR-UWB radar is operated by positioning the object at a certain distance and the object is classified by using the size and shape of the wave reflected by the object. To distinguish objects using only the size and shape of these waveforms, SVM (Support Vector Machine) was used to classify objects by learning shape and size of waveforms. In this paper, we show that the size and shape of the waveform received by the IR-UWB Radar can be identified by SVM pattern learning.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.63-71
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• 2011

Cardiac disorder data are generally tested using the classifier and QRS-Complex and R-R interval which is used in this experiment are often extracted by ECG(Electrocardiogram) signals. The experimentation of ECG data with classifier is generally performed with SVM(Support Vector Machine) and MLP(Multilayer Perceptron) classifier, but this study experimented with Best-First Decision Tree(B-F Tree) derived from the Dicision Tree among Random Forest classifier algorithms to improve accuracy. To compare and analyze accuracy, experimentation of SVM, MLP, RBF(Radial Basic Function) Network and Decision Tree classifiers are performed and also compared the result of announced papers carried out under same interval and data. Comparing the accuracy of Random Forest classifier with above four ones, Random Forest is the best in accuracy. As though R-R interval was extracted using Band-pass filter in pre-processing of this experiment, in future, more filter study is needed to extract accurate interval.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.2
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• pp.311-318
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• 2014

As modern computer graphics technology has been developed, it is hard to discriminate computer graphics from photographic images with the naked eye. Advances in graphics technology has brought a lot of convenience to human, it has side effects such as image forgery, malicious edit and fraudulent means. In order to cope with such problems, studies of various algorithms using a feature that represents a characteristic of an image has been processed. In this paper, we verify directly the existing algorithm, and provide new features based a noise that represents the characteristics of the computer graphics well. And this paper introduces the method of using SVM(Support Vector Machine) with features proposed in previous research to improve the discrimination accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.5
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• pp.91-104
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• 2003

A masquerader is someone who pretends to be another user while invading the target user's accounts, directories, or files. The masquerade attack is the most serious computer misuse. Because, in most cases, after securing the other's password, the masquerader enters the computer system. The system such as IDS could not detect or response to the masquerader. The masquerade detection is the effort to find the masquerader automatically. This system will detect the activities of a masquerader by determining that user's activities violate a profile developed for that user with his audit data. From 1988, there are many efforts on this topic, but the success of the offers was limited and the performance was unsatisfactory. In this report we propose efficient masquerade detection system using SVM which create the user profile.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.843-847
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• 2023

In this paper, we propose a model for detecting efficient fire identification to prevent fires that can lead to various industrial accidents, farmland and large forest fires, with the widespread use of various chemicals and flammable substances as modern technology advances. This paper presents an algorithm that can detect fire smoke in a high-efficiency and short time using images, and an algorithm based on SVM(Support Vector Machine) and K fold cross-verification technologies. By analyzing images, fire and smoke detection algorithms have relatively superior detection performance compared to existing algorithms, and the analysis of fire and smoke characteristics detected in this paper is analyzed stably and efficiently and is expected to be used in various fields that may be exposed to fire risks in the future.

• Knowledge Management Research
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• v.23 no.1
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• pp.187-201
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• 2022

Online reviews written by tourists provide important information for the management and operation of the tourism industry. The star rating of online reviews is a simple quantitative evaluation of a product or service, but it is difficult to reflect the sincere attitude of tourists. There is also an issue; the star rating and review content are not matched. In this study, a star rating prediction model based on online review content was proposed to solve the discrepancy problem. We compared the differences in star ratings and sentiment by continent through sentiment analysis on tourist attractions and hotels written by foreign tourists who visited Korea. Variables were selected through TF-IDF vectorization and sentiment analysis results. Logit, artificial neural network, and SVM(Support Vector Machine) were used for the classification model, and artificial neural network and SVR(Support Vector regression) were applied for the rating prediction model. The online review rating prediction model proposed in this study could solve inconsistency problems and also could be applied even if when there is no star rating.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.9
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• pp.47-52
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• 2020

With the 4th industrial revolution, condition monitoring using machine learning techniques has become popular among researchers. An overload due to complex operations causes several irregularities in MOSFETs. This study investigated the acquired voltage to analyze the overcurrent effects on MOSFETs using a failure mode effect analysis (FMEA). The results indicated that the voltage pattern changes greatly when the current is beyond the threshold value. Several features were extracted from the collected voltage signals that indicate the health state of a switched-mode power supply (SMPS). Then, the data were reduced to a smaller sample space by using a principal component analysis (PCA). A robust machine learning algorithm, the support vector machine (SVM), was used to classify different health states of an SMPS, and the classification results are presented for different parameters. An SVM approach assisted by a PCA algorithm provides a strong fault diagnosis framework for an SMPS.

• Journal of Industrial Technology
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• v.33 no.A
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• pp.31-39
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• 2013

There are two methods to make a distinction of deterioration of high-speed railway track. One is that an administrator checks for each attribute value of track induction data represented in graph and determines whether maintenance is needed or not. The other is that an administrator checks for monthly trend of attribute value of the corresponding section and determines whether maintenance is needed or not. But these methods have a weak point that it takes longer times to make decisions as the amount of track induction data increases. As a field of artificial intelligence, the method that a computer makes a distinction of deterioration of high-speed railway track automatically is based on machine learning. Types of machine learning algorism are classified into four type: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This research uses supervised learning that analogizes a separating function form training data. The method suggested in this research uses SVM classifier which is a main type of supervised learning and shows higher efficiency binary classification problem. and it grasps the difference between two groups of data and makes a distinction of deterioration of high-speed railway track.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.9
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• pp.433-440
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• 2016

Railway point machines act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Since point failure can significantly affect railway operations with potentially disastrous consequences, early stress detection of point machine is critical for monitoring and managing the condition of rail infrastructure. In this paper, we propose a stress detection method for point machine in railway condition monitoring systems using sound data. The system enables extracting sound feature vector subset from audio data with reduced feature dimensions using feature subset selection, and employs support vector machines (SVMs) for early detection of stress anomalies. Experimental results show that the system enables cost-effective detection of stress using a low-cost microphone, with accuracy exceeding 98%.