• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.218-223
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• 2011

Various research studies are underway to explore music classification by genre. Because sound professionals define the criterion of music to categorize differently each other, those classification is not easy to come up clear result. When a new genre is appeared, there is onerousness to renew the criterion of music to categorize. Therefore, music is classified by emotional adjectives, not genre. We classified music by light and shade in precedent study. In this paper, we propose the music classification system that is based on emotional adjectives to suitable search for atmosphere, and the classification criteria is three kinds; light and shade in precedent study, intense and placid, and grandeur and trivial. Variance Considered Machines that is an improved algorithm for Support Vector Machine was used as classification algorithm, and it represented 85% classification accuracy with the result that we tried to classify 525 songs.

• ETRI Journal
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• v.43 no.2
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• pp.332-343
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• 2021

With cyberattack techniques on the rise, there have been increasing developments in the detection techniques that defend against such attacks. However, cyber attackers are now developing fileless malware to bypass existing detection techniques. To combat this trend, security vendors are publishing analysis reports to help manage and better understand fileless malware. However, only fragmentary analysis reports for specific fileless cyberattacks exist, and there have been no comprehensive analyses on the variety of fileless cyberattacks that can be encountered. In this study, we analyze 10 selected cyberattacks that have occurred over the past five years in which fileless techniques were utilized. We also propose a methodology for classification based on the attack techniques and characteristics used in fileless cyberattacks. Finally, we describe how the response time can be improved during a fileless attack using our quick and effective classification technique.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.89-93
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• 2009

Embedded systems are becoming more popular as many embedded platforms have become more affordable. It offers a compact solution for many different problems including computer vision applications. Texture classification can be used to solve various problems, and implementing it in embedded platforms will help in deploying these applications into the market. This paper proposes to deploy the texture classification algorithms onto the embedded computer vision (ECV) platform. Two algorithms are compared; grey level co-occurrence matrices (GLCM) and Gabor filters. Experimental results show that raw GLCM on MATLAB could achieves 50ms, being the fastest algorithm on the PC platform. Classification speed achieved on PC and ECV platform, in C, is 43ms and 3708ms respectively. Raw GLCM could achieve only 90.86% accuracy compared to the combination feature (GLCM and Gabor filters) at 91.06% accuracy. Overall, evaluating all results in terms of classification speed and accuracy, raw GLCM is more suitable to be implemented onto the ECV platform.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.1-5
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• 2008

Generally the analytical tools of data mining have two learning types which are supervised and unsupervised learning algorithms. Classification and prediction are main analysis tools for supervised learning. In this paper, we perform a comparison study of classification algorithms in data mining. We make comparative studies between popular classification algorithms which are LDA, QDA, kernel method, K-nearest neighbor, naive Bayesian, SVM, and CART. Also, we use almost all classification data sets of UCI machine learning repository for our experiments. According to our results, we are able to select proper algorithms for given classification data sets.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.238-243
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• 2009

This paper present same thodology for the classification of power system voltage stability, the trajectory of which to instability is monotonic, using an interior point method based support vector machine(IPMSVM). The SVM based voltage stability classifier canp rovide real-time stability identification only using the local measurement data, without the topological information conventionally used.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.609-615
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• 1998

In this paper, we propose efficient total recognition system of handwritten and printed numerals for enhancing the classification time. The proposed system consist two step neuroclassifier: Printed numerals classifier and Handwritten numerals classifier. The performance of the propose classifier was tested on 5000 handwritten numerals database of NIST and 100 printed numerals database. In case of handwritten classifier, the overall classification times were 11 second. And in case of proposed system, the overall classification times were reduced by...

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.145-152
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• 2015

We consider an online selective-sample learning problem for sequence classification, where the goal is to learn a predictive model using a stream of data samples whose class labels can be selectively queried by the algorithm. Given that there is a limit to the total number of queries permitted, the key issue is choosing the most informative and salient samples for their class labels to be queried. Recently, several aggressive selective-sample algorithms have been proposed under a linear model for static (non-sequential) binary classification. We extend the idea to hidden Markov models for multi-class sequence classification by introducing reasonable measures for the novelty and prediction confidence of the incoming sample with respect to the current model, on which the query decision is based. For several sequence classification datasets/tasks in online learning setups, we demonstrate the effectiveness of the proposed approach.

• Advances in nano research
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• v.12 no.5
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• pp.489-499
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• 2022

Nanofluids have recently triggered a substantial scientific interest as cooling media. However, their stability is challenging for successful engagement in industrial applications. Different factors, including temperature, nanoparticles and base fluids characteristics, pH, ultrasonic power and frequency, agitation time, and surfactant type and concentration, determine the nanofluid stability regime. Indeed, it is often too complicated and even impossible to accurately find the conditions resulting in a stabilized nanofluid. Furthermore, there are no empirical, semi-empirical, and even intelligent scenarios for anticipating the stability of nanofluids. Therefore, this study introduces a straightforward and reliable intelligent classifier for discriminating among the stability regimes of alumina-water nanofluids based on the Zeta potential margins. In this regard, various intelligent classifiers (i.e., deep learning and multilayer perceptron neural network, decision tree, GoogleNet, and multi-output least squares support vector regression) have been designed, and their classification accuracy was compared. This comparison approved that the multilayer perceptron neural network (MLPNN) with the SoftMax activation function trained by the Bayesian regularization algorithm is the best classifier for the considered task. This intelligent classifier accurately detects the stability regimes of more than 90% of 345 different nanofluid samples. The overall classification accuracy and misclassification percent of 90.1% and 9.9% have been achieved by this model. This research is the first try toward anticipting the stability of water-alumin nanofluids from some easily measured independent variables.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.629-634
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• 2009

In real-time system application, the feature extraction and scoring algorithm for classification of the passive sonar target has the following problems: it requires an accurate and efficient feature extraction method because it is very difficult to distinguish the features of the propeller shaft rate (PSR) and the blade rate (BR) from the frequency spectrum in real-time, it requires a robust and effective feature scoring method because the classification database (DB) composed of extracted features is noised and incomplete, and further, it requires an easy design procedure in terms of structures and parameters. To solve these problems, an intelligent feature extraction and scoring algorithm using the evolution strategy (ES) and the fuzzy theory is proposed here. To verify the performance of the proposed algorithm, a passive sonar target classification is performed in real-time. Simulation results show that the proposed algorithm effectively solves sonar classification problems in real-time.