• Journal of Information Processing Systems
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• 제17권1호
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• pp.75-88
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• 2021

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.

• Steel and Composite Structures
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• 제46권3호
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• pp.435-449
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• 2023

Time domain method and frequency domain method are commonly used in the current fatigue life calculation theory. The time domain method has complicated procedures and needs a large amount of calculation, while the frequency domain method has poor applicability to different materials and different spectrum, and improper selection of spectrum model will lead to large errors. Considering that artificial neural network has strong ability of nonlinear mapping and generalization, this paper applied this technique to random fatigue life prediction, and the effect of average stress was taken into account, thereby achieving more accurate prediction result of random fatigue life.

• Asian-Australasian Journal of Animal Sciences
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• 제18권12호
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• pp.1675-1683
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• 2005

In the process of crossbreeding, the linkage disequilibria between the quantitative trait loci (QTL) and their linked markers were reduced gradually with increasing generations. To study the potential of QTL mapping using the crossbred population, we presented a mixed effect model that treated the mean allelic value of the different founder populations as the fixed effect and the allelic deviation from the population mean as random effect. It was assumed that there were fifty QTLs having effect on the trait variation, the population mean and variance were divided to each QTL in founder generation in our model. Only the additive effect was considered in this model for simulation. Six schemes (S1-S6) of crossbreeding were studied. The selection index was used to evaluate the synthetic breeding value of two traits of the individual in the scheme of S2, S4 and S6, and the individuals with high selection index were chosen as the parents of the next generation. Random selection was used in the scheme of S1, S3 and S5. In this study, we premised a QTL explained 40% of the genetic variance was located in a region of 20 cM by the linkage analysis previously. The log likelihood ratio (log LR) was calculated to determine the presence of a QTL at the particular chromosomal position in each of the generations from the fourth to twentieth. The profiles of log LR and the number of the highest log LR located in the region of 5, 10 and 20 cM were compared between different generations and schemes. The profiles and the correct number reduced gradually with the generations increasing in the schemes of S2, S4 and S6, but both of them increased in the schemes of S1, S3 and S5. From the results, we concluded that the crossbreeding population undergoing random selection was suitable for improving the resolution of QTL mapping. Even experiencing index selection, there was still enough variation existing within the crossbred population before the fourteenth generation that could be used to refine the location of QTL in the chromosome region.

• 대한간호학회지
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• 제37권3호
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• pp.414-421
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• 2007

Background: Nursing intervention studies often suffer from a selection bias introduced by failure of random assignment. Evaluation with selection bias could under or over-estimate any intervention's effects. PS matching (PSM) can reduce a selection bias through matching similar Propensity Scores (PS). PS is defined as the conditional probability of being treated given the individual's covariates and it can be reused to balance the covariates of two groups. Purpose: This study was done to assess the significance of PSM as an alternative evaluation method of nursing interventions. Method: An intervention study for patients with some baseline individual characteristic differences between two groups was used for this demonstration. The result of a t-test with PSM was compared with a t-test without matching. Results: The level of HbA1c at 12 months after baseline was different between the two groups in terms of matching or not. Conclusion: This study demonstrated the effects of a quasi-random assignment. Evaluation using PSM can reduce a selection bias impact that affects the result of the nursing intervention. Analyzing nursing research more objectively to reduce selection bias using PSM is needed.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• Journal of electromagnetic engineering and science
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• 제12권1호
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• pp.13-19
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• 2012

In this paper, we investigate cooperative transmission in one-dimensional random wireless networks. In this scheme, a stationary source communicates with a stationary destination with the help of N relays, which are randomly placed in a one-dimensional network. We derive exact and approximate expressions of the average outage probability over Rayleigh fading channels. Various Monte-Carlo simulations are presented to verify the accuracy of our analyses.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.405-406
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• 2006

This paper proposes the new algorithm for the extraction of the corresponding points. Our algorithm is based on RANSAC(Random Sample Consensus) with EM(Expectation-Maximization). In the procedure of RANSAC, N-points are selected by the result of EM instead of the random selection. EM+SAC algorithm is applied to the correspondence for the mosaicing.

• Journal of the Korean Data and Information Science Society
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• 제21권2호
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• pp.363-369
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• 2010

In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

• 한국통신학회:학술대회논문집
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• 한국통신학회 1986년도 춘계학술발표회 논문집
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• pp.156-159
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• 1986

In this paper, we discribed characteristics of two buffer which are limited area in packet communication networks, we selected JSQ(Join the Shortest Queue) method for buffrt management and compared it with single, random methods. The blocking probabilities of massage using JSQ method is decreased about (2.5 times) in compared with that of single queue method and (0.5 times) in random selection method. This results could be used in designing packet switching communication networks.

• 대한의용생체공학회:의공학회지
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• 제33권3호
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• pp.148-154
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• 2012

Hemorrhagic shock is a primary cause of deaths resulting from injury in the world. Although many studies have tried to diagnose accurately hemorrhagic shock in the early stage, such attempts were not successful due to compensatory mechanisms of humans. The objective of this study was to construct a survival prediction model of rats in acute hemorrhagic shock using a random forest (RF) model. Heart rate (HR), mean arterial pressure (MAP), respiration rate (RR), lactate concentration (LC), and peripheral perfusion (PP) measured in rats were used as input variables for the RF model and its performance was compared with that of a logistic regression (LR) model. Before constructing the models, we performed 5-fold cross validation for RF variable selection, and forward stepwise variable selection for the LR model to examine which variables were important for the models. For the LR model, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (ROC-AUC) were 0.83, 0.95, 0.88, and 0.96, respectively. For the RF models, sensitivity, specificity, accuracy, and AUC were 0.97, 0.95, 0.96, and 0.99, respectively. In conclusion, the RF model was superior to the LR model for survival prediction in the rat model.