• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.331-340
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• 2008

Feature Selection is one of methods to improve the classification accuracy of data in the field of machine learning. Many feature selection algorithms have been proposed and discussed for years. However, the problem of finding the optimal feature subset from full data still remains to be a difficult problem. Bio-inspired algorithms are well-known evolutionary algorithms based on the principles of behavior of organisms, and very useful methods to find the optimal solution in optimization problems. Bio-inspired algorithms are also used in the field of feature selection problems. So in this paper we proposed new improved bio-inspired algorithms for feature selection. We used well-known bio-inspired algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), to find the optimal subset of features that shows the best performance in classification accuracy. In addition, we modified the bio-inspired algorithms considering the prior importance (prior relevance) of each feature. We chose the mRMR method, which can measure the goodness of single feature, to set the prior importance of each feature. We modified the evolution operators of GA and PSO by using the prior importance of each feature. We verified the performance of the proposed methods by experiment with datasets. Feature selection methods using GA and PSO produced better performances in terms of the classification accuracy. The modified method with the prior importance demonstrated improved performances in terms of the evolution speed and the classification accuracy.

• Journal of Dairy Science and Biotechnology
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• v.18 no.1
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• pp.47-60
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• 2000

Over decades of work, the probiotic research has grown rapidly with a number of new cultures, which is claimed a variety of benefit. However, many of the specific effects attributed to the ingestion of probiotics remain convoluted and scientifically unsubstantiated. Accordingly, the scientific community faces a greater challenge and must objectively seek cause and effect relationships for many potential and currently investigated probiotic species. Rational selection and design of probiotics remains an important challenge and will require a solid information about the physiology and genetics of candidate strains relevant to their intestinal roles, functional activities, and interaction of with other resident micro flora. As far as beneficial culture of lactic acid bacteria (LAB) is concerned, simple, cost-effective, and exact identification of candidate strains is of foremost importance among others. Until recently, the relatedness of bacterial isolates has been determined sorely by testing for one or several phenotyphic markers, using methods such as serotyping, phage-typing, biotyping, and so forth. However, there are problems in the use of many of these phenotype-based methods. In contrast, some of newer molecular typing methods involving the analysis of DNA offer many advantages over traditional techniques. These DNA-based methods have the greater discriminatory power than that of phenotypic procedures. This review focuses on the importance and the basis of molecular typing methods along with some considerations on de-sign and selection of probiotic culture for human consumption.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.52-56
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• 1996

In trainir~ga back-propagation neural network, the learning speed of the network is greatly affected by its learning rate. Most of off-line fashioned learning-rate selection methods, however, are empirical except for some deterministic methods. It is very tedious and difficult to find a good learning rate using the empirical methods. The deterministic methods cannot guarantee the quality of the quality of the learning rate. This paper proposes a new learning-rate selection method. Our off-line fashioned method selects a good learning rate through stochastically searching process using evolutionary programming. The simulation results show that the learning speed achieved by our method is superior to that of deterministic and empirical methods.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.149-159
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• 2019

Six sigma is an innovative management movement which provides improved business process by adapting the paradigm and the trend of market and customers. Suitable selection of six sigma project could highly reduce the costs, improve the quality, and enhance the customer satisfaction. There are existing studies on the selection of Six Sigma projects, but few studies have been conducted to select the correct project under an incomplete information environment. The purpose of this study is to propose the application of integrated MCDM techniques for correct project selection under incomplete information. The project selection process of six sigma involves four steps as follows: 1) determination of project selection criteria 2) calculation of relative importance of team member's competencies 3) assessment with project preference scale 4) finalization of ranking the projects. This study proposes the combination methods by applying group fuzzy Analytical Hierarchy Process (AHP), an easy defuzzified number of Trapezoidal Fuzzy Number (TrFN) and Grey Relational Analysis (GRA). Both of the weight of project selection criteria and the relative importance of team member's competencies can be evaluated by group fuzzy AHP. Project preferences are assessed by easy defuzzified scale of TrFN in case of incomplete information.)

• The Journal of Advanced Prosthodontics
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• v.16 no.1
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• pp.38-47
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• 2024

PURPOSE. This study aims to evaluate the accuracy of different shade selection techniques and determine the matching success of crown restorations fabricated using digital shade selection techniques. MATERIALS AND METHODS. Teeth numbers 11 and 21 were prepared on a typodont model. For the #11 tooth, six different crowns were fabricated with randomly selected colors and set as the target crowns. The following four test groups were established: Group C, where the visual shade selection was performed using the Vita 3D Master Shade Guide and the group served as the control; Group Ph, where the shade selection was performed under the guidance of dental photography; Group S, where the shade selection was performed by measuring the target tooth color using a spectrophotometer; and Group I, where the shade selection was performed by scanning the test specimens and target crowns using an intraoral scanner. Based on the test groups, 24 crowns were fabricated using different shade selection techniques. The ΔE values were calculated according to the CIEDE2000 (2:1:1) formula. The collected data were analyzed by means of a one-way analysis of variance. RESULTS. For the four test groups (Groups C, Ph, S, and I), the following mean ΔE values were obtained: 2.74, 3.62, 2.13, and 3.5, respectively. No significant differences were found among the test groups. CONCLUSION. Although there was no statistically significant difference among the shade selection techniques, Group S had relatively lower ΔE values. Moreover, according to the test results, the spectrophotometer shade selection technique may provide more successful clinical results.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.197-204
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• 2014

RIP, EIGRP, OSPF are the interior gateway protocol for sending and receiving routing information among routers in AS(Autonomous System). Various path selection methods using the metric in regard to them have been studied recently but there are few examples that the contents learners understand theoretically are verified by the practice. The Best Path is determined by calculating the Cost value based on the relevant topology of each routing protocol. After implementing the virtual network, it is certain that the results tracking and verifying the relevant path selection of each routing protocol are consistent with the Best Path. If methods suggested in this paper are applied properly, the relevant path selection process of routing protocol can be understood systematically. And it is expected that the outstanding results of learning will be able to be achieved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5631-5652
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• 2019

The existing defense strategy selection methods based on game theory basically select the optimal defense strategy in the form of mixed strategy. However, it is hard for network managers to understand and implement the defense strategy in this way. To address this problem, we constructed the incomplete information stochastic game model for the dynamic analysis to predict multi-stage attack-defense process by combining Bayesian game theory and the Markov decision-making method. In addition, the payoffs are quantified from the impact value of attack-defense actions. Based on previous statements, we designed an optimal defense strategy selection method. The optimal defense strategy is selected, which regards defense effectiveness as the criterion. The proposed method is feasibly verified via a representative experiment. Compared to the classical strategy selection methods based on the game theory, the proposed method can select the optimal strategy of the multi-stage attack-defense process in the form of pure strategy, which has been proved more operable than the compared ones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11C
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• pp.1092-1099
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• 2006

Future wireless communication systems will employ spatial multiplexing with multiple antennas at both transmitter and receiver to take advantage of larger capacity gains as compared to the systems that use a single antenna. However, in order to reduce higher hardware costs and computational burden, it will require an efficient transmit-receive antenna selection algorithm, which we propose in this paper. Through simulation and comparative analysis of various existing methods and the one we propose in this paper, the algorithm we propose was validated as nearer to the optimal selection technique than existing nearly optimal antenna selection schemes.

• Journal of Environmental Impact Assessment
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• v.8 no.1
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• pp.71-79
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• 1999

The comparative evaluation is the most effective method for site selection because the selection of waste treatment facility is to determine the optimum site out of limited candidate sites. This study adopted the ordinal scale evaluation, one of methods of comparative evaluation. The ordinal scale evaluation aims to determine the investigating items referring to the character of sites, to determine the importance factors for investigating items, and to determine the optimum site according to the quantitative evaluation. As a result of this study, the defects of the former reports on the environmental characteristics, such as obscurity of meaning and subjective statement, were reduced by the ordinal scale evaluation which is one of the quantitative evaluation methods. This ordinal scale evaluation method has some valuable advantages, such as, to be able to consider the cost-effect efficiency, to consider the objectiveness and the clearness of the reports on the environmental characteristics. Therefore the reducement of social complications about site selection of the indisposed facilities could be expected by this study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.647-669
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• 2016

Multiple attribute for decision making including user preference will increase the complexity of route selection process. Various approaches have been proposed to solve the optimal route selection problem. In this paper, multi attribute decision making (MADM) algorithms such as Simple Additive Weighting (SAW), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP) method and Total Order Preference by Similarity to the Ideal Solution (TOPSIS) methods have been proposed for acoustic signature based optimal route selection to facilitate user with better quality of service. The traffic density state conditions (very low, low, below medium, medium, above medium, high and very high) on the road segment is the occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) is considered as one of the attribute in decision making process. The short-term spectral envelope features of the cumulative acoustic signals are extracted using Mel-Frequency Cepstral Coefficients (MFCC) and Adaptive Neuro-Fuzzy Classifier (ANFC) is used to model seven traffic density states. Simple point method and AHP has been used for calculation of weights of decision parameters. Numerical results show that WPM, AHP and TOPSIS provide similar performance.