• Phonetics and Speech Sciences
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• v.8 no.2
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• pp.17-22
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• 2016

This paper devises a model to diagnose physical fatigue using speech features. This paper presents a machine learning method through an SVM algorithm using the various feature parameters. The parameters used include the significant speech parameters, questionnaire responses, and bio-signal parameters obtained before and after the experiment imposing the fatigue. The results showed that performance rates of 95%, 100%, and 90%, respectively, were observed from the proposed model using three types of the parameters relevant to the fatigue. These results suggest that the method proposed in this study can be used as the physical fatigue diagnostic model, and that fatigue can be easily diagnosed by speech technology.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.41-47
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• 2012

MANET has vulnerable structure on security owing to structural characteristics as follows. MANET consisted of moving nodes is that every nodes have to perform function of router. Every node has to provide reliable routing service in cooperation each other. These properties are caused by expose to various attacks. But, it is difficult that position of environment intrusion detection system is established, information is collected, and particularly attack is detected because of moving of nodes in MANET environment. It is not easy that important profile is constructed also. In this paper, conformal predictor - support vector machine(CP-SVM) based intrusion detection technique was proposed in order to do more accurate and efficient intrusion detection. In this study, IDS-agents calculate p value from collected packet and transmit to cluster head, and then other all cluster head have same value and detect abnormal behavior using the value. Cluster form of hierarchical structure was used to reduce consumption of nodes also. Effectiveness of proposed method was confirmed through experiment.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.458-460
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• 2000

본 논문에서는 유전자 알고리즘(GA)과 Support Vector Machine(SVM)을 결합하여 사용한 얼굴 인식 시스템을 제안한다. 기존의 SVM을 이용한 얼굴 인식 연구에서는 얼굴 전체 영상을 SVM의 입력벡터로 사용하는데 반해, 본 연구에서는 GA를 이용하여 얼굴 영상 중에서 개인별로 식별 능력이 우수한 특징들만을 선택하여 이를 SVM의 입력벡터로 사용한다. 조명, 표정, 안경 착용 등 다양한 변화가 있는 Yale 얼굴 데이터베이스를 사용하여 실험한 결과, 얼굴 전체 영상을 사용한 경우보다 더 좋은 인식률을 보였다. 또한 제안된 방법에 의한 얼굴 인식 시스템은 각 개인별로 식별력이 우수한 특징들만을 저장하므로, 얼굴인식 시스템을 구성하기 위해 저장될 정보의 양이 현저하게 감소하게 된다.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1648-1649
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• 2007

This paper proposes a very novel method which makes it possible that state feedback controller can be designed for unknown dynamic system with measurable states. The RLS algorithm is used for the identification of input-output relationship. A virtual state space representation is derived from the relationship and the SVM(Support Vector Machines) makes the relationship between actual states and virtual states. A state feedback controller can be designed based on the virtual system and the SVM makes the controller be with actual states. The results of this paper can give many opportunities that the state feedback control can be applied for unknown dynamic systems

• Geomechanics and Engineering
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• v.13 no.3
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• pp.447-458
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• 2017

This study proposes a sensitivity analysis method for slope stability based on the least squares support vector machine (LS-SVM) to examine the influencing factors of slope stability. The method uses LS-SVM as an algorithm for machine learning. An appropriate training dataset is established according to the slope characteristics, and a testing dataset is designed orthogonally. Results of the testing data in the experiment design are calculated after training using the LS-SVM model. The sensitivity of the slope stability of each factor is examined via gray correlation analysis. The results are consistent with those of the traditional Bishop analysis and can be used as a reference for optimizing slope design.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.112-119
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• 2021

Social media has become a global means of communication in people's lives. Most people are using Twitter for communication purposes and its inappropriate use, which has negative effects on people's lives. One of the widely common misuses of Twitter is cyberbullying. As the resources of dialectal Arabic are rare, so for cyberbullying most people are using dialectal Arabic. For this reason, the ultimate goal of this study is to detect and classify cyberbullying on Twitter in the Arabic context in Saudi Arabia. To help in the detection and classification of tweets, Pointwise Mutual Information (PMI) to generate a lexicon, and Support Vector Machine (SVM) algorithms are used. The evaluation is performed on both methods in terms of the F1-score. However, the F1-score after applying the PMI is 50%, while after the SVM application on the resampling data it is 82%. The analysis of the results shows that the SVM algorithm outperforms better.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.93-96
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• 2022

Natural language processing (NLP) is utilized to understand a natural text. Text analysis systems use natural language algorithms to find the meaning of large amounts of text. Text classification represents a basic task of NLP with a wide range of applications such as topic labeling, sentiment analysis, spam detection, and intent detection. The algorithm can transform user's unstructured thoughts into more structured data. In this work, a text classifier has been developed that uses academic admission and registration texts as input, analyzes its content, and then automatically assigns relevant tags such as admission, graduate school, and registration. In this work, the well-known algorithms support vector machine SVM and K-nearest neighbor (kNN) algorithms are used to develop the above-mentioned classifier. The obtained results showed that the SVM classifier outperformed the kNN classifier with an overall accuracy of 98.9%. in addition, the mean absolute error of SVM was 0.0064 while it was 0.0098 for kNN classifier. Based on the obtained results, the SVM is used to implement the academic text classification in this work.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.706-714
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• 2012

The Korea Meteorological Administration provided the recent four-years records of weather dataset for our very short-term heavy rainfall prediction. We divided the dataset into three parts: train, validation and test set. Through feature selection, we select only important features among 72 features to avoid significant increase of solution space that arises when growing exponentially with the dimensionality. We used a differential evolution algorithm and two classifiers as the fitness function of evolutionary computation to select more accurate feature subset. One of the classifiers is Support Vector Machine (SVM) that shows high performance, and the other is k-Nearest Neighbor (k-NN) that is fast in general. The test results of SVM were more prominent than those of k-NN in our experiments. Also we processed the weather data using undersampling and normalization techniques. The test results of our differential evolution algorithm performed about five times better than those using all features and about 1.36 times better than those using a genetic algorithm, which is the best known. Running times when using a genetic algorithm were about twenty times longer than those when using a differential evolution algorithm.

• Journal of IKEEE
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• v.22 no.1
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• pp.46-52
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• 2018

The problem of traffic light detection and recognition has recently become one of the most important topics in various researches on autonomous driving. Most algorithms are based on colors to detect and recognize traffic light signals. These methods have disadvantage in that the recognition rate is lowered due to the change of the color of the traffic light, the influence of the angle, distance, and surrounding illumination environment of the image. In this paper, we propose machine learning based detection and recognition algorithm using shape information to solve these problems. Unlike the existing algorithms, the proposed algorithm detects and recognizes the traffic signals based on the morphological characteristics of the traffic lights, which is advantageous in that it is robust against the influence from the surrounding environments. Experimental results show that the recognition rate of the signal is higher than those of other color-based algorithms.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.725-732
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• 2020

This paper uses variables following as : to follow me well(0-9), it takes a lot of time to make a decision (0-9), lethargy(0-9) during physical activity in the exercise learning program of the children in the marginalized class. This paper classifies 'gender', 'physical education classroom', and 'upper, middle and lower' of age, and observe changes in ego-resiliency and self-control through sports rehabilitation therapy to find out changes in mental health. To achieve this, the data acquired was merged and the characteristics of large and small numbers were removed using the Label encoder and One-hot encoding. Then, to evaluate the performance by applying each algorithm of MLP, SVM, Dicesion tree, RNN, and LSTM, the train and test data were divided by 75% and 25%, and then the algorithm was learned with train data and the accuracy of the algorithm was measured with the Test data. As a result of the measurement, LSTM was the most effective in sex, MLP and LSTM in physical education classroom, and SVM was the most effective in age.