• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.38-59
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• 2022

Web services instances can be classified into two categories, namely trusted and untrusted from users. A web service with high throughput (TP) and low response time (RT) instance values is a trusted web service. Web services are not trustworthy due to the mismatch in the guaranteed instance values and the actual values achieved by users. To perform web services selection from users' attained TP and RT values, we need to verify the correct prediction of trusted and untrusted instances from invoked web services. This accurate prediction of web services instances is used to perform the selection of web services. We propose to construct fuzzy rules to label web services instances correctly. This paper presents web services selection using a well-known machine learning algorithm, namely REPTree, for the correct prediction of trusted and untrusted instances. Performance comparison of REPTree with five machine learning models is conducted on web services datasets. We have performed experiments on web services datasets using a ten k-fold cross-validation method. To evaluate the performance of the REPTree classifier, we used accuracy metrics (Sensitivity and Specificity). Experimental results showed that web service (WS1) gained top selection score with the (47.0588%) trusted instances, and web service (WS2) was selected the least with (25.00%) trusted instances. Evaluation results of the proposed web services selection approach were found as (asymptotic sig. = 0.019), demonstrating the relationship between final selection and recommended trust score of web services.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 추계학술대회
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• pp.161-164
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• 1996

An actively-shielded $r^{2}$-gradient coil has been developed for brain localized MRI or MRS. Spatial localization is very useful for spatial volume selection in MRI or MR Spectroscopy(MRS). The radial(or $R^{2}-$) gradient coil is useful in reducing the artifact or in improving the SNR by selecting the volume with less number of RF pulses. It is, however, difficult to implement the coil with a gradient intensity strong enough to use it for practical whole-body MRI system. For example, the smallest volume size for selection is just 6 cm in diameter with a 250 Ampere of current driving for a whole-body system (in case of 70-cm-diameter). In this study, an asymetric $r^{2}$-coil with a small diameter of 35 cm has been designed and implemented for brain localized MRI or MRS. An 8-rod high-pass-type birdcage RF coil has also been implemented. The coil set has been developed for 1.0 Tesla Medison MRI system and its performance has been verified experimentally.

• 기술혁신학회지
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• 제3권2호
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• pp.48-60
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• 2000

An effort has been made in this paper to measure the project manager's reaction to R&D project evaluation and present the useful information related to rebuilding an R&D evaluation system. Results of this study show that project manager's reaction is related to the selection of reasonable items for evaluation, the rater's expertise, the project manager's participation in evaluation process and so on. Accordingly, managers of the research institute have to set up the evaluation system after deeply considering these results.

• International Journal of Industrial Entomology and Biomaterials
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• 제14권1호
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• pp.1-7
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• 2007

In order to find out the appropriate parents for the breeding programme, twelve bivoltine and three multivoltine silkworm breeds were evaluated on the basis of multivariate selection index and isozyme analysis. Of which, four [CSR2, D6 (P), SK3, SK4] bivoltine and two multivoltine (Nistari, Cambodge) breeds were selected and breeding initiated to develop higher survival bivoltine silkworm breed suitable for tropical conditions. Among two isozyme (Esterase and acid phosphatase) analyzed, only esterase exhibited polymorphism among the bivoltine breeds. No polymorphism was observed among multivoltine in respect of esterase as well as acid phosphatase.

• Asian Journal of Innovation and Policy
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• 제12권2호
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• pp.261-267
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• 2023

In the paper of 'Natural Selection Favors AIs over Humans,' Dan Hendrycks applies principles of Darwinian evolution to forecast potential trajectories of AI development. He proposes that competitive pressures within corporate and military realms could lead to AI replacing human roles and exhibiting self-interested behaviors. However, such claims carry the risk of oversimplifying the complex issues of competition and natural selection without clear criteria for judging whether AI is selfish or altruistic, necessitating a more in-depth analysis and critique. Other studies, such as ''The Threat of AI and Our Response: The AI Charter of Ethics in South Korea,' offer diverse opinions on the natural selection of artificial intelligence, examining major threats that may arise from AI, including AI's value judgment and malicious use, and emphasizing the need for immediate discussions on social solutions. Such contemplation is not merely a technical issue but also significant from an ethical standpoint, requiring thoughtful consideration of how the development of AI harmonizes with human welfare and values. It is also essential to emphasize the importance of cooperation between artificial intelligence and humans. Hendrycks's work, while speculative, is supported by historical observations of inevitable evolution given the right conditions, and it prompts deep contemplation of these issues, setting the stage for future research focused on AI safety, regulation, and ethical considerations.

• 한국경영과학회지
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• 제6권2호
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• pp.35-49
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• 1981

This paper is concerned with developing a Multi-period Behavioral Model for the portfolio selection problem. The unique feature of the model is that it treats a number of factors and decision variables considered germane in decision making on an interrelated basis. The formulated problem has the structure of a Chance Constrained programming Model. Then empoloying arguments of Central Limit Theorem and normality assumption the stochastic model is reduced to that of a Non-Linear Programming Model. Finally, a number of interesting properties for the reduced model are established.

• Journal of Animal Science and Technology
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• 제56권7호
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• pp.23.1-23.7
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• 2014

Background: Scanning of the genome for selection signatures between breeds may play important role in understanding the underlie causes for observable phenotypic variations. The discovery of high density single nucleotide polymorphisms (SNPs) provide a useful starting point to perform genome-wide scan in pig populations in order to identify loci/candidate genes underlie phenotypic variation in pig breeds and facilitate genetic improvement programs. However, prior to this study genomic region under selection in commercially selected Berkshire and Korean native pig breeds has never been detected using high density SNP markers. To this end, we have genotyped 45 animals using Porcine SNP60 chip to detect selection signatures in the genome of the two breeds by using the $F_{ST}$ approach. Results: In the comparison of Berkshire and KNP breeds using the FDIST approach, a total of 1108 outlier loci (3.48%) were significantly different from zero at 99% confidence level with 870 of the outlier SNPs displaying high level of genetic differentiation ($F_{ST}{\geq}0.490$). The identified candidate genes were involved in a wide array of biological processes and molecular functions. Results revealed that 19 candidate genes were enriched in phosphate metabolism (GO: 0006796; ADCK1, ACYP1, CAMK2D, CDK13, CDK13, ERN1, GALK2, INPP1; MAK, MAP2K5, MAP3K1, MAPK14, P14KB, PIK3C3, PRKC1, PTPRK, RNASEL, THBS1, BRAF, VRK1). We have identified a set of candidate genes under selection and have known to be involved in growth, size and pork quality (CART, AGL, CF7L2, MAP2K5, DLK1, GLI3, CA3 and MC3R), ear morphology and size (HMGA2 and SOX5) stress response (ATF2, MSRB3, TMTC3 and SCAF8) and immune response (HCST and RYR1). Conclusions: Some of the genes may be used to facilitate genetic improvement programs. Our results also provide insights for better understanding of the process and influence of breed development on the pattern of genetic variations.

• Journal of Multimedia Information System
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• 제3권2호
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• pp.1-8
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• 2016

In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.

• 대한약학회:학술대회논문집
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• 대한약학회 2002년도 Proceedings of the Convention of the Pharmaceutical Society of Korea Vol.2
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• pp.324.1-324.1
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• 2002

Human 5-reductase type II(5AR2) is an important target for the treatment of benign prostatic hyperplasia. In this study we describe the establishment of cell line which stably expressed 3X FLAG tagged human 5AR2. We used this cell line as a cell based assay tool and source for 5AR2 enzyme. First a plasmid (3XFLAGpCMVl0-5AR2) for the expression of 5AR2 was constructed by the use of the vector 3XFLAGpCMV10 and transfected into the HEK 293. By selection with G418 sulfate. ten HEK 293 single cell clones were obtained of which three stably exhibited high 5AR2 activity. (omitted)

• Communications for Statistical Applications and Methods
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• 제26권2호
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• pp.79-89
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• 2019

Classification models pertaining to receiver operating characteristic (ROC) curve analysis have been extended from univariate to multivariate setup by linearly combining available multiple markers. One such classification model is the multivariate ROC curve analysis. However, not all markers contribute in a real scenario and may mask the contribution of other markers in classifying the individuals/objects. This paper addresses this issue by developing an algorithm that helps in identifying the important markers that are significant and true contributors. The proposed variable selection framework is supported by real datasets and a simulation study, it is shown to provide insight about the individual marker's significance in providing a classifier rule/linear combination with good extent of classification.