• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.453-458
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• 2003

The anomaly-detection algorithm based on negative selection of T cells is representative model among self-recognition methods and it has been applied to computer immune systems in recent years. In immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigen, or the nonself cell. In this paper, we propose a novel self-recognition algorithm based on the positive selection of T cells. We indicate the effectiveness of the proposed algorithm by change-detection simulation of some infected data obtained from cell changes and string changes in the self-file. We also compare the self-recognition algorithm based on positive selection with the anomaly-detection algorithm.

• Korea Journal of Hospital Management
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• v.27 no.3
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• pp.1-14
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• 2022

Purposes: This purpose of this study was to check how much the online word of mouth influences on customer's hospital selection according to Kano's model. Methodology: Kano classified the attributes that affect customer's satisfaction into attractive, one-dimensional, indifferent, must-be, and reverse attributes. Among them, attractive and one-dimensional attributes make up the largest portion in hospital selection. Based on this, the influence of positive or negative online reviews on the selection of hospitals was investigated. Differentiated service was selected as the attractive attributes, and a kind, sufficient explanation was selected as the one-dimensional attributes. Then a questionnaire was conducted how much the positive or negative online reviews influence on hospital selection, respectively. It was conducted from August 7 to September 7, 2021 for medical consumers in their 20s and older who have used medical services for the past 3 years, and the final 142 questionnaires were analyzed. All data was analyzed by chi-square and two-way ANOVA using SPSS ver 25.0. Findings: The results showed that, in one-dimensional attributes, the difference between positive and negative reviews was not statistically significant, but in attractive attributes, positive and negative reviews showed a statistically significant difference. It suggests that positive reviews on attractive attributes had a greater influence on hospital selection. In terms of hospital selection, when the experimental participants were exposed to the positive reviews, the hospital selection ratio did not differ by Kano's attributes, but to the negative reviews it differed. The hospital selection ratio, even after they were exposed to negative reviews, was higher in the attractive attributes than in the one-dimensional attributes. Practical Implication: This study confirmed that hospital selection is influenced differently depending on the Kano's attributes and the direction of the reviews, and suggests that marketers should respond differently to each Kano's attributes when they deal with online reviews of hospitals.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.190-195
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• 2004

We propose a pattern classification algorithm using self-nonself discrimination principle of immune cells and apply it to DNA pattern classification problem. Pattern classification problem in bioinformatics is very important and frequent one. In this paper, we propose a classification algorithm based on the negative selection of the immune system to classify DNA patterns. The negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes ${\eta}$ groups of antigenic receptor for ${\eta}$ different patterns, these receptor groups can classify into ${\eta}$ patterns. We propose a pattern classification algorithm based on the negative selection in nucleotide base level and amino acid level. Also to show the validity of our algorithm, experimental results of RNA group classification are presented.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.231-235
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• 2004

According to revealing the DNA sequence of human and living things, it increases that a demand on a new computational processing method which utilizes DNA sequence information. In this paper we propose a classification algorithm based on negative selection of the immune system to classify DNA patterns. Negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes n group of antigenic receptor for n different patterns, they can classify into n patterns. In this paper we propose a pattern classification algorithm based on negative selection in nucleotide base level and amino acid level.

• Culinary science and hospitality research
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• v.22 no.8
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• pp.109-121
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• 2016

This study analyzed the effect of five sub-variables' attribute of HMR: features of information, diversity, promptness, price and convenience, on the consumer purchasing intention. In addition, the regulation effect of positive reviews and negative reviews of consumers' online reviews between HMR selection attribute and purchasing intention was also tested. Results are following. First, convenience feature (B=.577, p<.001) and diversity feature (B=.093, p<.01) among the effect of HMR selection attribute had a positive (+) effect on purchasing intention. On the other hand, promptness feature (B=.235, p<.001) and price feature (B=.161, p<.001), and information feature (B=.288, p<.001) were not significant effect on purchasing intention. Second, result of regulation effect of the positive reviews of consumer's online review between the selection attribute of the HMR product and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product is input as an independent variable, there was a significant positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In addition, there was significant positive (+) main effect (B=.472, p<.001) in the second step model in which the consumers' positive reviews, that is a regulation variable. Furthermore, the feature of price (B=.068, p<.05) had a significant positive (+) effect in the third stage in which the selection attribute of the HMR product that is an independent variable and the interaction of the positive review. However, the feature of information (B=-.063, p<.05) showed negative (-) effect, and there was no effect on the features of convenience, diversity, and promptness. Third, as a result of testing the regulation effect of the negative reviews of consumers' online reviews between HMR product selection attribute and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product was a positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In the second-stage model in which consumers' negative reviews (B=-.113, p<.001) had negative (-) effect. In the third-stage in which the selection attribute of the HMR product and the interactions of the negative reviews was a positive (+) effect with the feature of price (B=.113, p<.01). Last, there was no effect at all on the features of convenience, promptness, and information.

• Journal of Microbiology and Biotechnology
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• v.30 no.10
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• pp.1583-1591
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• 2020

CRISPR/Cpf1 has emerged as a new CRISPR-based genome editing tool because, in comparison with CRIPSR/Cas9, it has a different T-rich PAM sequence to expand the target DNA sequence. Single-base editing in the microbial genome can be facilitated by oligonucleotide-directed mutagenesis (ODM) followed by negative selection with the CRISPR/Cpf1 system. However, single point mutations aided by Cpf1 negative selection have been rarely reported in Corynebacterium glutamicum. This study aimed to introduce an amber stop codon in crtEb encoding lycopene hydratase, through ODM and Cpf1-mediated negative selection; deficiency of this enzyme causes pink coloration due to lycopene accumulation in C. glutamicum. Consequently, on using double-, triple-, and quadruple-base-mutagenic oligonucleotides, 91.5-95.3% pink cells were obtained among the total live C. glutamicum cells. However, among the negatively selected live cells, 0.6% pink cells were obtained using single-base-mutagenic oligonucleotides, indicating that very few single-base mutations were introduced, possibly owing to mismatch tolerance. This led to the consideration of various target-mismatched crRNAs to prevent the death of single-base-edited cells. Consequently, we obtained 99.7% pink colonies after CRISPR/Cpf1-mediated negative selection using an appropriate single-mismatched crRNA. Furthermore, Sanger sequencing revealed that single-base mutations were successfully edited in the 99.7% of pink cells, while only two of nine among 0.6% of pink cells were correctly edited. The results indicate that the target-mismatched Cpf1 negative selection can assist in efficient and accurate single-base genome editing methods in C. glutamicum.

• IE interfaces
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• v.20 no.4
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• pp.458-468
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• 2007

Artificial immune systems (AIS) are one of natural computing inspired by the natural immune system. The fault detection, the pattern recognition, the system control and the optimization are major application area of artificial immune systems. This paper gives a concept of artificial immune systems and useful techniques as like the clonal selection, the immune network theory and the negative selection. A concise survey on the optimization problem based on artificial immune systems is generated. The overall performance of artificial immune systems for the optimization problem is discussed.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.527-538
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• 2021

The purpose of the study is to determine the efficiency, position, and partner selection of securities companies via the negative super-SBM model used in data envelopment analysis (DEA). This model utilizes a variety of inputs, including current assets, non-current assets, fixed assets, liabilities, owner's equity and charter capital, and outputs including net revenue, gross profit, operating profit, and net profit after tax collected from the financial reports (Vietstock, 2020) of 32 securities companies, operating during the period from 2016 to 2019, negative data are collected as well. Empirical results determined both efficient and inefficient terms, and then further determined the position of each securities firm under consideration of every term. The overall score arrived at discovered a large performance change realizing a maximum score able to reach 20.791. In the next stage, alliancing inefficient companies was carried out based on the 2019 scores to seek out optimal partners for the inefficient companies. The tested result indicated that AAS was the best partner selection when its partners received a good result after alliancing, as with FTS (11.04469). The partner selection is deemed as a solution helpful to inefficient securities companies in order to improve their future efficiency scores.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.9
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• pp.460-466
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• 2002

A reliable fault phase selection algorithm plays a very important role in transmission line protection, Particularly in Extra High Voltage (EHV) networks. The conventional fault phase selection algorithm used the phase difference between positive and negative sequence current excluding load current. But, it is difficult to pick out only fault current since we can not know when a fault occurs and select the fault phase in weak-infeed conditions that dominate zero-sequence current in phase current. The proposed algorithm can select the accurately fault phase using the sum of unit vectors which are calculated by positive-sequence voltage and negative-sequence voltage.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.254-257
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• 2002

인터넷 보급의 확산과 전자상거래의 활성화와 유.무선 인터넷의 보급에 따른 악의적인 사이버 공격의 시도의 성공사례가 증가하고 있다. 미로 인해 점차 더 많은 문제가 야기될 것으로 예상된다. 현재 일반적인 인터넷상의 시스템은 악의적인 공격에 적절하게 대응해오지 못하고 있으며, 다른 범용의 시스템들도 기존의 백신 프로그램에 의존하며 그 공격에 대응해오고 있다. 따라서 새로운 침입에 대하여는 대처하기 힘든 단점을 가지고 있다. 본 논문에서는 생체 자율분산시스템의 일부분인 T세포의 positive selection과 negative selection을 이용한 자기/비자기 인식 알고리즘을 제안한다. 제안한 알고리즘은 네트워크 환경에서 침입탐지 시스템에 적용하여 기존에 알려진 침입뿐만 아니라 새로운 침입에 대해서도 대처할 수 있다