• Journal of Information Processing Systems
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• v.20 no.2
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• pp.263-272
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• 2024

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.8
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• pp.1031-1040
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• 2011

The objective of this study was to characterize factors influencing genetic improvement of dairy cattle for milk production at farm level. Data were accumulated from 305-day milk yields and pedigree information from 1,921 first-lactation dairy cows that calved from 1990 to 2007 on 161 farms in Central Thailand. Variance components were estimated using average information restricted maximum likelihood procedures. Animal breeding values were predicted by an animal model that contained herd-year-season, calving age, and regression additive genetic group as fixed effects, and cow and residual as random effects. Estimated breeding values from cows that calved in a particular month were used to estimate genetic trends for each individual farm. Within-farm genetic trends (b, regression coefficient of farm milk production per month) were used to classify farms into 3 groups: i) farms with negative genetic trend (b<-0.5 kg/mo), ii) farms with no genetic trend (-0.5 kg/$mo{\leq}b{\leq}0.5$ kg/mo), and iii) farms with positive genetic trend (b>0.5 kg/mo). Questionnaires were used to gather information from individual farmers on educational background, herd characteristics, farm management, decision making practices, and opinion on dairy farming. Farmer's responses to the questionnaire were used to test the association between these factors and farm groups using Fisher's exact test. Estimated genetic trend for the complete population was $0.29{\pm}1.02$ kg/year for cows. At farm level, most farms (40%) had positive genetic trend ($0.63{\pm}4.67$ to $230.79{\pm}166.63$ kg/mo) followed by farms with negative genetic trend (35%; $-173.68{\pm}39.63$ to $-0.62{\pm}2.57$ kg/mo) and those with no genetic trend (25%; $-0.52{\pm}3.52$ to $0.55{\pm}2.68$ kg/mo). Except for educational background (p<0.05), all other factors were not significantly associated with farm group.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.602-604
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• 2017

As human life span has increased, people have wanted to live healthier desires. Especially Korea has rapidly entered an aging society, leading to the burden of medical expenses to the increase of disease accompanying aging. To alleviate the burden of medical expenses, prediction and prevention are important rather than treatment of diseases. It is possible to predict and prevent diseases by measuring individual genetic information. In order to utilize individual's genetic information Korea's genetic information is grasped through SNP (800 thousand) and GWAS optimized for the discovery of genetic factors of phenotype and disease of Koreans, The genetic information of each individual is analyzed in the genetic (constitutional) characteristics of the individual. In this thesis we develop a classification index so that we can classify populations of specific chronic diseases (obesity, diabetes or cardiovascular system). Try to develop health care services to manage custom diet and exercise associated with chronic illness.

• Genomics & Informatics
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• v.16 no.4
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• pp.31.1-31.7
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• 2018

The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis was performed in two stages (training and test sets). Model A was designed with only clinical information (age, sex, body mass index, smoking status, alcohol consumption status, and exercise status), and for model B, genetic information (for 10 polymorphisms) was added to model A. Of the 7,502 nonobese participants, 647 (8.6%) had MS. In the test set analysis, for the maximum sensitivity criterion, NB showed the highest sensitivity: 0.38 for model A and 0.42 for model B. The specificity of NB was 0.79 for model A and 0.80 for model B. In a comparison of the performances of models A and B by NB, model B (area under the receiver operating characteristic curve [AUC] = 0.69, clinical and genetic information input) showed better performance than model A (AUC = 0.65, clinical information only input). We designed a prediction model for MS in a nonobese population using clinical and genetic information. With this model, we might convince nonobese MS individuals to undergo health checks and adopt behaviors associated with a preventive lifestyle.

• Proceedings of the Korean Society of Developmental Biology Conference
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• 2003.10a
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• pp.82-82
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• 2003

Offspring have been produced from somatic cells in a number of species. This biotechnology introduced a new phenomenon in reprogramming and differentiation of somatic cell, namely totipotency. However, birth of oversized calves and perinatal abnormalities such as increased gestation length, lack of spontaneous parturition, higher incidence of dystocia, and reduced perinatal viability of offspring are frequently observed in pregnancies of cloned bovine fetuses. Disturbance of feto-placenta has been proposed as likely causes for abnomal growth. However. Little is known the mechanism responsible for the perinatal problems. Therefore, we focused on gestation length in somatic cell nuclear recipient cows. To solve this issues, placental tissues of control and cloned bovine were obtained by a cesarean section (C-section) before 5 days of paturition. Total RNA from control and cloned bovine placenta was extractd by TRIzol reagent. GeneFishing DEG kits (Seegene) were used to identify differentially expression genes. Total RNA (3 ug) were synthesized by M-MLV reverse transcriptase (200 u/ul) with 10 uM dT-annealing control primer (ACP1) at 42C for 90 min. Then, first-strand cDNA (50 ng) was amplified using the 5 uM arbitary ACP (1-20) and 10 uM dT-ACP2 primers. Some specific expression genes were amplified, Now, we are cloning and sequencing. These finding strongly can be support to solve the problems for parturition delay in nuclear transfer cows, suggest that placenta specific proteins are key indicators for the aberration of gestation and placental function in cows.

• ETRI Journal
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• v.41 no.3
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• pp.348-357
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• 2019

Fog radio access network (F-RAN) architectures provide markedly improved performance compared to conventional approaches. In this paper, an efficient genetic algorithm-based content distribution scheme is proposed that improves the throughput and reduces the transmission delay of a F-RAN. First, an F-RAN system model is presented that includes a certain number of randomly distributed fog access points (F-APs) that cache popular content from cloud and other sources. Second, the problem of efficient content distribution in F-RANs is described. Third, the details of the proposed optimal genetic algorithm-based content distribution scheme are presented. Finally, simulation results are presented that show the performance of the proposed algorithm rapidly approaches the optimal throughput. When compared with the performance of existing random and exhaustive algorithms, that of the proposed method is demonstrably superior.

• The Journal of Korean Medicine
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• v.27 no.2 s.66
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• pp.225-231
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• 2006

Objectives : This study was performed to determine if unknown species of antler samples could be identified by genetic distance methods. Methods : The DNAs of 4 antler samples were extracted, amplified by PCR, and sequenced. The DNAs of antlers were identified by genetic distance. Genetic distance method was made using MEGA software (Molecular Evolutionary Genetics Analysis, 3.1). Results : By genetic distance methods, all 4 antler samples were closest to Cervus elaphus nelsoni among Cervus species. Conclusion : These results suggest that genetic distance methods might be used as a tool for the identification of Cervus species.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.115-123
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• 2016

In this study, we collect various side effect pairs which are appeared frequently at many drugs, and select side effect pairs that have higher severity. For every selected side effect pair, we extract common genetic networks which are shared by side effects' genes and drugs' target genes based on PPI(Protein-Protein Interaction) network. For this work, firstly, we gather drug related data, side effect data and PPI data. Secondly, for extracting common genetic network, we find shortest paths between drug target genes and side effect genes based on PPI network, and integrate these shortest paths. Thirdly, we develop a classification model which uses this common genetic network as a classifier. We calculate similarity score between the common genetic network and genetic network of a drug for classifying the drug. Lastly, we validate our classification model by means of AUC(Area Under the Curve) value.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.3-5
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• 2005

In this paper. we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C-Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Journal of Korea Society of Industrial Information Systems
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• v.1 no.1
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• pp.63-85
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• 1996

This paper describes the applications of genetic algorithm to nonlinear constrained optimization problems. Genetic algorithms are combinatorial in nature, and therefore are computationally suitable for treating continuous and idstrete integer design variables. For several problems , the conventional genetic algorithms are ill-defined , which comes from the application of penalty function , encoding and decoding methods, fitness scaling, and premature convergence of solution. Thus, we develope a hybrid genetic algorithm to resolve these problems and present two examples to demonstrate the effectiveness of the methodology developed in this paper.