• KIPS Transactions on Software and Data Engineering
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• v.9 no.7
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• pp.213-220
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• 2020

The convolutional neural network is composed of convolutional layers and fully connected layers. The nonlinear activation function is used in each layer of the convolutional layer and the fully connected layer. The activation function being used in a neural network is a function that simulates the method of transmitting information in a neuron that can transmit a signal and not send a signal if the input signal is above a certain criterion when transmitting a signal between neurons. The conventional activation function does not have a relationship with the loss function, so the process of finding the optimal solution is slow. In order to improve this, an agile activation function that generalizes the activation function is proposed. The agile activation function can improve the performance of the deep neural network in a way that selects the optimal agile parameter through the learning process using the primary differential coefficient of the loss function for the agile parameter in the backpropagation process. Through the MNIST classification problem, we have identified that agile activation functions have superior performance over conventional activation functions.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.319-328
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• 2021

Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTMKHA produces reasonable performance metrics when compared to the existing DDI prediction model.

• Journal of KIISE
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• v.42 no.12
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• pp.1535-1543
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• 2015

The brain-computer interface obtains a user's electroencephalogram as a replacement communication unit for the disabled such that the user is able to control machines by simply thinking instead of using hands or feet. In this paper, we propose a feature extraction method based on a non-selected filter by SBCSP to classify motor imagery EEG. First, we divide frequencies (4~40 Hz) into 4-Hz units and apply CSP to each Unit. Second, we obtain the FLD score vector by combining FLD results. Finally, the FLD score vector is projected onto the optimal plane for classification using PCA. We use BCI Competition III dataset IVa, and Extracted features are used as input for LS-SVM. The classification accuracy of the proposed method was evaluated using $10{\times}10$ fold cross-validation. For subjects 'aa', 'al', 'av', 'aw', and 'ay', results were $85.29{\pm}0.93%$, $95.43{\pm}0.57%$, $72.57{\pm}2.37%$, $91.82{\pm}1.38%$, and $93.50{\pm}0.69%$, respectively.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.87-95
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• 1996

A new design scheme for the fuzzy pattern matching classifier (FPMC) is proposed. in conventional design of FPMC, there are no exact information about the membership function of which shape and number critically affect the performance of classifier. So far, a trial and error or heuristic method is used to find membership functions for the input patterns. But each of them have limits in its application to the various types of pattern recognition problem. In this paper, a new method to find the appropriate shape and number of membership functions for the input patterns which minimize classification error is proposed using genetic algorithms(GAs). Genetic algorithms belong to a class of stochastic algorithms based on biological models of evolution. They have been applied to many function optimization problems and shown to find optimal or near optimal solutions. In this paper, GAs are used to find the appropriate shape and number of membership functions based on fitness function which is inversely proportional to classification error. The strings in GAs determine the membership functions and recognition results using these membership functions affect reproduction of next generation in GAs. The proposed design scheme is applied to the several patterns such as tire tread patterns and handwritten alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.309-320
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• 2007

The data imbalance problem which can be uncounted in data mining classification problems typically means that there are more or less instances in a class than those in other classes. It causes low prediction accuracy of the minority class because classifiers tend to assign instances to major classes and ignore the minor class to reduce overall misclassification rate. In order to solve the data imbalance problem, there has been proposed a number of techniques based on resampling with replacement, adjusting decision thresholds, and adjusting the cost of the different classes. In this paper, we study the feasibility of the combination usage of the techniques previously proposed to deal with the data imbalance problem, and suggest a combination method using genetic algorithm to find the optimal combination ratio of the techniques. To improve the prediction accuracy of a minority class, we determine the combination ratio based on the F-value of the minority class as the fitness function of genetic algorithm. To compare the performance with those of single techniques and the matrix-style combination of random percentage, we performed experiments using four public datasets which has been generally used to compare the performance of methods for the data imbalance problem. From the results of experiments, we can find the usefulness of the proposed method.

• Journal of KIISE
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• v.43 no.11
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• pp.1270-1274
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• 2016

Logistic regression is widely used for predicting and estimating the relationship among variables. We propose a new logistic regression model, the value weighted logistic regression, which comprises of a fine-grained weighting method, and assigns adapted weights to each feature value. This gradient approach obtains the optimal weights of feature values. Experiments were conducted on several data sets from the UCI machine learning repository, and the results revealed that the proposed method achieves meaningful improvement in the prediction accuracy.

• 한국지형공간정보학회:학술대회논문집
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• 1993.10a
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• pp.141-145
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• 1993

A new digital change detection algorithm, Euclidean Distance Analysis, was developed in an attempt to utilize the multi-band information in a selected band-comination, as an alternative to the conventional single-band analysis methods. To evaluate the relative performance of this new method, image differencing was applied. The better performance in change detection between the two algorithms investigated was provided by the Euclidean distance analysis. The new technique of Euclidean distance analysis holds promise for change detection, since it summarizes the multiple-band information on the cover-type changes and reduces the data dimensionality. It is suggested to further evaluate this new method, quantitatively, in the different environments. The use of different accuracy indices was also examined in the determining the optimal threshold level for each change image. As the standard measure for classification accuracy, the Kappa coefficient of agreement was used for evaluation.

• Journal of the Korean Geotechnical Society
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• v.19 no.5
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• pp.319-326
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• 2003

In site investigation for tunnel designs, nowadays, geophysical exploration such as seismic exploration and electric resistivity exploration as well as drilling logging is frequently carried out. A method which can systematically make the utmost use of all available data obtained from investigation, therefore, is strongly required for the optimal evaluation of ground conditions in terms of rock mass class, etc. Many researchers have proposed using qualitative data to cope with the lack of quantitative data. In this study, an evaluation technique of rock mass classes in undrilled region was proposed based upon multiple indicator kriging method which is a geostatistical technique. It was shown that two types of data with different degree of uncertainty, for example, drilling logging data and geophysical exploration data, could be simultaneously utilized in evaluating rock mass classes for a real tunnel design.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.1
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• pp.25-32
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• 2007

In this paper, a new method for first order plus dead time(FOPDT) model identification is proposed, which can identity multiple points on a process step response in terms of classification of time response. The process input and output to the test are decomposed into the transient part and the steady-state part. The steady-state part express one FOPDT model and the transient part express variously FOPDT model using least square estimation method. The optimum parameter tuning algorithm for PID controller of the Smith Predictor is proposed through ITAE as performance index. The Simulation results show the validity and improvement of performance for various processes.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.99-113
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• 2021

In these days, as cyberspace has been recognized as the fifth battlefield area following the land, sea, air, and space, attention has been focused on activities that view cyberspace as an operational and mission domain in earnest. Also, in the 21st century, cyber operations based on cyberspace are being developed as a 4th generation warfare method. In such an environment, the success of the operation is determined by the commander's decision. Therefore, in order to increase the rationality and objectivity of such decision-making, it is necessary to systematically establish and select a course of action (COA). In this study, COA is established by using the method of classifying operational elements necessary for cyber operation, and it is intended to suggest a direction for quantitative evaluation of COA. To this end, we propose a method of composing the COES (Cyber Operational Elements Set), which becomes the COA of operation, and classifying the cyber operational elements identified in the target development process based on the 5W1H Method. In addition, by applying the proposed classification method to the cyber operation elements used in the STUXNET attack case, the COES is formed to establish the attack COAs. Finally, after prioritizing the established COA, quantitative evaluation of the policy was performed to select the optimal COA.