• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.81-87
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• 2023

Intrusion detection systems that learn metadata of network packets have been proposed recently. However these approaches require time to analyze packets to generate metadata for model learning, and time to pre-process metadata before learning. In addition, models that have learned specific metadata cannot detect intrusion by using original packets flowing into the network as they are. To address the problem, this paper propose a natural language processing-based intrusion detection system that detects intrusions by learning the packet payload as a single sentence without an additional conversion process. To verify the performance of our approach, we utilized the UNSW-NB15 and Transformer models. First, the PCAP files of the dataset were labeled, and then two Transformer (BERT, DistilBERT) models were trained directly in the form of sentences to analyze the detection performance. The experimental results showed that the binary classification accuracy was 99.03% and 99.05%, respectively, which is similar or superior to the detection performance of the techniques proposed in previous studies. Multi-class classification showed better performance with 86.63% and 86.36%, respectively.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.389-396
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• 2024

Generative AI has recently been utilized across all fields, achieving expert-level advancements in deep data analysis. However, identifying regional names in scientific literature remains a challenge due to insufficient training data and limited AI application. This study developed a standardized dataset for effectively classifying regional names using address data from Korean institution-affiliated authors listed in the Web of Science. It tested and evaluated the applicability of machine learning and deep learning models in real-world problems. The BERT model showed superior performance, with a precision of 98.41%, recall of 98.2%, and F1 score of 98.31% for metropolitan areas, and a precision of 91.79%, recall of 88.32%, and F1 score of 89.54% for city classifications. These findings offer a valuable data foundation for future research on regional R&D status, researcher mobility, collaboration status, and so on.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.448-461
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• 2024

Purpose: The purpose of this study is to establish a life zone class suitable for Korean characteristics in the post-COVID-19 era and to classify the types for the installation and operation of living SOC facilities. Method: The concept of the life zone was established through policies and previous studies related to the life zone, and data in various fields such as population, employment, transportation, economy, and education were classified using the z-score technique. Result: Korea's life zones can be classified into metropolitan life zones, regional life zones, urban life zones, village life zones, and neighborhood life zones, and depending on their roles, they can be classified into central life zones, workplace-residential balanced life zones, residential life zones, industrial life zones, and low-density life zones. Conclusion: The results of this study show that proper life zone establishment and proper living SOC supply can prevent the decline of underdeveloped areas and contribute to balanced regional development

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.186-191
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• 2023

Data processing techniques play a critical role in decision-making, including handling missing and outlier data, prediction, and recommendation models. This requires a clear explanation of the validity, reliability, and accuracy of all processes and results. In addition, it is necessary to solve data problems through explainable models using decision trees, inference, etc., and proceed with model lightweight by considering various types of learning. The multi-layer mining classification method that applies the sixth principle is a method that discovers multidimensional relationships between variables and attributes that occur frequently in transactions after data preprocessing. This explains how to discover significant relationships using mining on transactions and model the data through regression analysis. It develops scalable models and logistic regression models and proposes mining techniques to generate class labels through data cleansing, relevance analysis, data transformation, and data augmentation to make explanatory decisions.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.51-59
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• 2024

Collecting a dataset with a corresponding labeled gaze vector requires a high cost in the gaze estimation field. In this paper, we suggest a data augmentation of manipulating the gaze of an original image, which improves the accuracy of the gaze estimation model when the number of given gaze labels is restricted. By conducting multi-class gaze bin classification as an auxiliary task and adjusting the latent variable of the diffusion model, the model semantically edits the gaze from the original image. We manipulate a non-binary attribute, pitch and yaw of gaze vector to a desired range and uses the edited image as an augmented train data. The improved gaze accuracy of the gaze estimation network in the semi-supervised learning validates the effectiveness of our data augmentation, especially when the number of gaze labels is 50k or less.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.25-41
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• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.

• Journal of Sasang Constitutional Medicine
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• v.11 no.1
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• pp.137-157
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• 1999

The purpose of this study is to help classifying Sasang Constitution through correlation with sound characteristic. This study was done it under the suppose that Sasang Constitution has correlation with sound spectrogram. The following result were obtained about correlation between sound spectrogram and Sasang Constitution by comparison and analysis 1. Soeumin answered his voice low tone, smooth and quiet in the survey. Soyangin answered his voice high, clear, fast and speaking random. Taeumin answered his voice low, thick and muddy. 2. Taeyangin was significantly slow compared with the others in the time of reading composition. Taeyangin was significantly slow compared with the others in Formant frequency 1. Taeyangin was significantly discriminated from Soeumin in Formant frequency 5. Taeyangin was significantly low compared with the others in Bandwidth 2. Soeumln was significantly low compared with Taeyangin in Pitch Maximum and Pitch Maximum-Pitch Minimum. Taeyangin was significantly high compared with the others in Energy mean. 3. In list of specification, the discrimination rate was higher than that by lists of 13 in the results of Multi-dimensional 4-class minimum-distance. The discrimination rate of three disposition except Soyangin was higher than that of four disposition in the results of One way ANOVA and Analysis of dis crimination in SPSS/PC+. In CART, the estimate rate of Sasang Constitution discrimination was higher than any other method. It is considered that there is a correlation between sound spectrogram and Sasang constitution according to the results. And method of Sasang constitution classification through sound spectrogram analysis can be one method as assistant for the objectification of Sasang constitution classification.

• Restorative Dentistry and Endodontics
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• v.31 no.4
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• pp.300-311
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• 2006

The purpose of this study is to evaluate prospectively the effect of different bonding systems and retention grooves on the clinical performance of resin restorations in non-carious cervical lesions (NCCLs). Thirty-nine healthy adults who had at least 2 NCCLs in their premolar areas were included in this study. One hundred and fifty teeth were equally assigned to six groups: (A) Scotchbond Multi-Purpose (SBMP, 3M ESPE, St. Paul, MN, USA, 4th generation bonding system) without retention grooves; (B) SBMP with retention grooves; (C) BC Plus (Vericom Co., Anyang, Gyeonggido, Korea, 5th generation bonding system) without retention grooves; (D) BC Plus with retention grooves; (E) Adper Prompt (3M ESPE, Seefeld, Germany, 6th generation bonding system) without retention grooves; (F) Adper Prompt with retention grooves. All cavities were filled with a hybrid composite resin. Denfil (Vericom Co., Anyang, Gyeonggido, Korea) by one operator. Restorations were evaluated at baseline and at 6-month recall, according to the modified USPHS (United States Public Health Service) criteria. Additionally, clinical photographs were taken and epoxy resin replicas were made for SEM evaluation. At 6-month recall, there were some differences in the number of alpha ratings among the experimental groups. But, despite the differences in the number of alpha ratings, there was no significant difference among the 3 adhesive systems (p < 0.05). There was also no significant difference between the groups with or without mechanical retention (p < 0.05). Follow-ups for longer periods than 6 months are needed to verify the clinical performance of different bonding systems and retention grooves.

• Journal of Food Hygiene and Safety
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• v.32 no.6
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• pp.493-499
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• 2017

This study was conducted to monitor the current status of pesticide residues and sulfur dioxide in a total of 91 functional rice products from February to October 2016. Multi class pesticide multiresidue methods of 220 pesticides was performed by the GC/ECD, GC/NPD, GC/TOF/MS, LC/PDA, and LC/FLD. As a result of analysis, the pesticides were detected in 3 samples, representing a detection rate of 3.3%. The kind of pesticides was propiconazole and isoprothiolane used for germicide in agriculture or plant growth regulator purposes. The detected levels were 0.0340~0.0566 mg/kg, which were under the MRL (Maximum Residues Limits). The contents of sulfur dioxide in 91 samples by the Monier-Williams method were not detected. Risk assessment of pesticides evaluated using human health exposure with the ratio of EDI (Estimated daily intake) to ADI (Acceptable daily intake). %ADI (the ratios of EDI to ADI) were 0.24~1.25% with safety level.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.