• 사상체질의학회지
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• 제22권1호
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• pp.17-25
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• 2010

1. Objectives: Voice diagnosis has been used to classify individuals into the Sasang constitution in SCM(Sasang Constitution Medicine) and to recognize his/her health condition in TKM(Traditional Korean Medicine). In this paper, we purposed a new speech classification algorithm for Sasang constitution. 2. Methods: This algorithm is based on the SVM(Support Vector Machine) technique, which is a classification method to classify two distinct groups by finding voluntary nonlinear boundary in vector space. It showed high performance in classification with a few numbers of trained data set. We designed for this algorithm using 3 SVM classifiers to classify into 4 groups, which are composed of 3 constitutional groups and additional indecision group. 3. Results: For the optimal performance, we found that 32.2% of the voice data were classified into three constitutional groups and 79.8% out of them were grouped correctly. 4. Conclusions: This new classification method including indecision group appears efficient compared to the standard classification algorithm which classifies only into 3 constitutional groups. We find that more thorough investigation on the voice features is required to improve the classification efficiency into Sasang constitution.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.406-412
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• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• 한국정보통신학회논문지
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• 제25권3호
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• pp.427-432
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• 2021

본 논문에서는 미상의 통신신호에 대한 자동 변조 인식을 위하여 심층신경망인 딥뉴럴네트워크를 적용하여 변조 형태를 식별하고 그 성능을 분석하였다. 신경망 입력 데이터는 변조된 신호의 시간영역 디지털샘플 데이터, FFT(Fast Fourier Transform)를 적용한 주파수영역 데이터, 시간 및 주파수영역 혼합데이터를 사용하여 각각의 변조인식 성능을 확인하였다. 아날로그 변조 및 디지털 변조 신호 11종에 대하여 -20~18 dB 까지 다양한 SNR(Signal to Noise Ratio) 환경에서 변조인식 성능을 확인하고 그 성능을 분석하였으며, 입력 데이터의 종류에 따른 학습 속도를 확인함으로써 제안한 방법이 실제적인 자동변조 인식 시스템 구축에 효과적인 방법임을 확인 하였다.

• 한국멀티미디어학회논문지
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• 제23권7호
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• pp.803-811
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• 2020

This paper proposes an image classification algorithm that transforms the number of convolution layers in the residual block of ResNet, CNN's representative method. The proposed method modified the structure of 34/50 layer of ResNet structure. First, we analyzed the performance of small and many convolution layers for the structure consisting of only shortcut and 3 × 3 convolution layers for 34 and 50 layers. And then the performance was analyzed in the case of small and many cases of convolutional layers for the bottleneck structure of 50 layers. By applying the results, the best classification method in the residual block was applied to construct a 34-layer simple structure and a 50-layer bottleneck image classification model. To evaluate the performance of the proposed image classification model, the results were analyzed by applying to the cifar10 dataset. The proposed 34-layer simple structure and 50-layer bottleneck showed improved performance over the ResNet-110 and Densnet-40 models.

• 한국군사과학기술학회지
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• 제10권4호
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• pp.88-96
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• 2007

This paper discusses the fast modulation classifiers capable of classifying both analog and digital modulation signals in wireless communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulated signals. In this paper, we investigate the performance of the two types of fast modulation classifiers (i.e. 2 nearest neighbor classifiers and 2 minimum distance classifiers) and compare the performance of these classifiers with that of the state of the art for the existing classification methods such as SVM Classifier. Computer simulations indicate good performance on an AWGN channel, even at low signal-to-noise ratios, in case of minimum distance classifiers (MDC for short) and k nearest neighbor classifiers (kNNC for short). Besides a good performance, these type classifiers are considered as ideal candidate to adapt real-time software radio because of their fast modulation classification capability.

• 산업경영시스템학회지
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• 제44권3호
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• 정보보호학회논문지
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• 제33권6호
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• pp.1067-1076
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• 2023

최근 변종 악성코드가 증가하면서 사이버 해킹 침해사고 규모가 확대되고 있다. 그리고 지능형 사이버 해킹 공격에 대응하기 위해 악성코드 패밀리를 효과적으로 분류하기 위한 기계학습 기반 연구가 활발히 진행되고 있다. 그러나 기존의 분류 모델은 데이터셋이 난독화되거나, 희소한 경우에 성능이 저하되는 문제가 있었다. 본 논문에서는 ASM 파일과 BYTES 파일에서 추출한 특징을 결합한 하이브리드 데이터셋을 제안하고, FNN을 사용하여 분류 성능을 평가한다. 실험 결과에 따르면 제안하는 방법은 단일 데이터셋에 비해 약 4% 향상된 성능을 보였으며, 특히 희소한 패밀리에 대해서는 약 30%의 성능 향상을 보였다.

• Communications for Statistical Applications and Methods
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• 제21권1호
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• pp.61-68
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• 2014

We propose binary classification methods by modifying logistic regression classification. We use variable selection procedures instead of original variables to select the principal components. We describe the resulting classifiers and discuss their properties. The performance of our proposals are illustrated numerically and compared with other existing classification methods using synthetic and real datasets.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(5)
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• pp.273-276
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• 2002

This paper has design of ECG pattern classification system using decision of fuzzy IF-THEN rules and neural network. each fuzzy IF-THEN rule in our classification system has antecedent lingustic values and a single consequent class. we use a fuzzy reasoning method based on a single winner rule in the classification phase. this paper in, the MIT/BIH arrhythmia database for the source of input signal is used in order to evaluate the performance of the proposed system. From the simulation results, we can effectively pattern classification by application of learned from neural networks.