• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• 대기
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• 제22권2호
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• pp.149-161
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• 2012

Going by the research results of the past, of all the uncertainties resulting from the research on climate change, the uncertainty caused by the climate change scenario has the highest degree of uncertainty. Therefore, depending upon what kind of climate change scenario one adopts, the projection of the water resources in the future will differ significantly. As a matter of principle, it is highly recommended to utilize all the GCM scenarios offered by the IPCC. However, this could be considered to be an impractical alternative if a decision has to be made at an action officer's level. Hence, as an alternative, it is deemed necessary to select several scenarios so as to express the possible number of cases to the maximum extent possible. The objective standards in selecting the climate change scenarios have not been properly established and the scenarios have been selected, either at random or subject to the researcher's discretion. In this research, a new scenario selection process, in which it is possible to have the effect of having utilized all the possible scenarios, with using only a few principal scenarios and maintaining some of the uncertainties, has been suggested. In this research, the use of cluster analysis and the selection of a representative scenario in each cluster have efficiently reduced the number of climate change scenarios. In the cluster analysis method, the K-means clustering method, which takes advantage of the statistical features of scenarios has been employed; in the selection of a representative scenario in each cluster, the selection method was analyzed and reviewed and the PDF method was used to select the best scenarios with the closest simulation accuracy and the principal scenarios that is suggested by this research. In the selection of the best scenarios, it has been shown that the GCM scenario which demonstrated high level of simulation accuracy in the past need not necessarily demonstrate the similarly high level of simulation accuracy in the future and various GCM scenarios were selected for the principal scenarios. Secondly, the "Maximum entropy" which can quantify the uncertainties of the climate change scenario has been used to both quantify and compare the uncertainties associated with all the scenarios, best scenarios and the principal scenarios. Comparison has shown that the principal scenarios do maintain and are able to better explain the uncertainties of all the scenarios than the best scenarios. Therefore, through the scenario selection process, it has been proven that the principal scenarios have the effect of having utilized all the scenarios and retaining the uncertainties associated with the climate change to the maximum extent possible, while reducing the number of scenarios at the same time. Lastly, the climate change scenario most suitable for the climate on the Korean peninsula has been suggested. Through the scenario selection process, of all the scenarios found in the 4th IPCC report, principal climate change scenarios, which are suitable for the Korean peninsula and maintain most of the uncertainties, have been suggested. Therefore, it is assessed that the use of the scenario most suitable for the future projection of water resources on the Korean peninsula will be able to provide the projection of the water resources management that maintains more than 70~80% level of uncertainties of all the scenarios.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권6호
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• pp.451-461
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• 2009

적응적 랜덤 테스팅(Adaptive Random Testing, ART)은 테스트 케이스의 효율적인 선택을 통해 순수 랜덤 테스팅(Random Testing, RT)보다 더 적은 수의 테스트 케이스를 이용해 입력 도메인 내의 오류를 찾는 것을 목적으로 한다. ART 기법 중 하나인 입력 도메인 반복 분할 기법(ART through Iterative Partitioning, IP-ART)은 초기 ART 기법의 단점인 많은 연산량을 입력 도메인 분할에 의해 효율적으로 개선되도록 하였으며, 입력 도메인 확장을 이용한 IP-ART(IP-ART with Enlarged Input Domain, EIP-ART)는 IP-ART의 테스트 케이스 분포 특징을 이용하여 추가적인 성능 향상과 확장성을 제공하였다. 하지만 EIP-ART는 입력 도메인 확장에 따라 발생하는 부하로 인해 테스트 케이스 생성에 오랜 시간을 요구하기 때문에 이의 개선이 필요하다. 따라서 본 논문에서는 두 가지의 추가 부하를 유발하지 않는 테스트 케이스 분포 조절 기법을 제안하고, 이들의 성능 개선 가능성을 실험을 통해 확인하였으며, 실험 결과, 제안된 두 기법 중 입력 도메인 타일링 기법이 모든 환경에서 더 우수한 성능 및 확장성을 갖는 것으로 확인되었다.

• 한국정보과학회논문지:정보통신
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• 제27권4호
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• pp.507-521
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• 2000

실시간 멀티미디어 서비스에서 Quality of Service(QoS) 보장의 필요성이 증가하고 있다. 멀티미디어 서비스 제공 형태의 대다수가 될 멀티캐스트 경로설정에서도 QoS 보장은 확장성 신뢰성과 함께 매우 중요한 문제이다. QoS 기반 코어 선택 알고리즘을 제안한다. 제안 알고리즈믄 멀티캐스트 경로설정에서 코어 선택시에 다중 QoS 제약조건을 고려한다. QoS 제약조건은 최소보장 대역폭, 종단 지연, 종단 지연변이 등으로 정의한다. 모의 실험결과는 제안한 QCSA와 Maximum Centered Tree(MCT) Average Centered Tree (ACT) Initial Delay-Constrained Shared Tree(Dcinitial) Random Tree(Random)등의 기존 코어 선택 알고리즘의 성능을 각 항목별로 비교한다 멀티캐스트 그룹 멤버수와 QoS 제약조건을 인자로 한 모의 실험 결과는 제안한 QoS 기반 코어 선택 알고리즘이 기존 코어 선택 알고리즘에 비해서 다중 QoS 제약조건 보장 코어 선택 성공률에서 성능 개선 효과를 가짐을 보여준다. 제안 알고리즘이 본 논문에서 설정한 모의 실험 환경에서는 QoS 기반 코어 선택의 정도를 나타내는 성공률에서 약 10% 정도 기존 알고리즘보다 우수함을 보인다. 이 결과는 제안 알고리즘이 코어 선택 과정의 초기부터 멀티캐스트 그룹내의 모든 멤버에 대한 다중 QoS 제약조건을 고려하는 점이 QoS 기반 코어 선택에서 개선 효과를 나타냄을 보여준다.

• 인터넷정보학회논문지
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• 제17권5호
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• pp.111-119
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• 2016

In recent years, the opinion analysis is one of the key research fronts of any domain. Opinion target extraction is an essential process of opinion analysis. Target is usually referred to noun or noun phrase in an entity which is deliberated by the opinion holder. Extraction of opinion target facilitates the opinion analysis more precisely and in addition helps to identify the opinion polarity i.e. users can perceive opinion in detail of a target including all its features. One of the most commonly employed algorithms is a sequence labeling algorithm also called Conditional Random Fields. In present article, recent opinion target extraction approaches are reviewed based on sequence labeling algorithm and it features combinations by analyzing and comparing these approaches. The good selection of features combinations will in some way give a good or better accuracy result. Features combinations are an essential process that can be used to identify and remove unneeded, irrelevant and redundant attributes from data that do not contribute to the accuracy of a predictive model or may in fact decrease the accuracy of the model. Hence, in general this review eventually leads to the contribution for the opinion analysis approach and assist researcher for the opinion target extraction in particular.

• Plant Biotechnology Reports
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• 제2권3호
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• pp.191-198
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• 2008

Germplasm characterization and evolutionary process in viable populations are important links between the conservation and utilization of plant genetic resources. Here, an investigation is made, based on molecular and biochemical techniques for assessing and exploiting the genetic variability in germplasm characterization of taro, which would be useful in plant breeding and ex situ conservation of taro plant genetic resources. Geographical differentiation and phylogenetic relationships of Indian taro, Colocasia esculenta (L.) Schott, were analyzed by random amplified polymorphic DNA (RAPD) and isozyme of seven enzyme systems with specific reference to the Muktakeshi accession, which has been to be proved resistant to taro leaf blight caused by P. colocasiae. The significant differentiations in Indian taro cultivars were clearly demonstrated by RAPD and isozyme analysis. RAPD markers showed higher values for genetic differentiation among taro cultivars and lower coefficient of variation than those obtained from isozymes. Genetic differentiation was evident in the taro accessions collected from different regions of India. It appears that when taro cultivation was introduced to a new area, only a small fraction of genetic variability in heterogeneous taro populations was transferred, possibly causing random differentiation among locally adapted taro populations. The selected primers will be useful for future genetic analysis and provide taro breeders with a genetic basis for selection of parents for crop improvement. Polymorphic markers identified in the DNA fingerprinting study will be useful for screening a segregating population, which is being generated in our laboratory aimed at developing a taro genetic linkage map.

• The Plant Pathology Journal
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• 제25권3호
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• pp.205-212
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• 2009

Colletotrichum acutatum was the main cause of the recent outbreaks of anthracnose on pepper fruit in Korea. To facilitate molecular analysis of C. acutatum, we generated an arginine auxotrophic mutant of the C acutatum strain JC24 using a targeted gene replacement strategy. A 3.3-kb genomic region carrying an ortholog (designated CaARG2) of the fungal gene encoding N-acetylglutamate synthase, the first enzyme of arginine biosynthesis in fungi, was deleted from the fungal genome. The mutant exhibited normal growth only when arginine was exogenously supplied into the culture medium. Transformation of the arginine auxotrophic mutant with a plasmid DNA carrying an intact copy of CaARG2, which was smaller than the deleted region in the mutant, not only caused random vector insertions in the fungal genome, but also recovered both hyphal growth and pathogenicity of the mutant to the wild-type level. Using this new selection system, we have successfully developed a restriction enzyme-mediated integration procedure, which would provide an economically efficient random mutagenesis method in C. acutatum.

• Structural Engineering and Mechanics
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• 제25권3호
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• pp.347-363
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• 2007

A novel methodology, referred to as Auxiliary Domain Method (ADM), allowing for a very efficient solution of nonlinear reliability problems is presented. The target nonlinear failure domain is first populated by samples generated with the help of a Markov Chain. Based on these samples an auxiliary failure domain (AFD), corresponding to an auxiliary reliability problem, is introduced. The criteria for selecting the AFD are discussed. The emphasis in this paper is on the selection of the auxiliary linear failure domain in the case where the original nonlinear reliability problem involves multiple objectives rather than a single objective. Each reliability objective is assumed to correspond to a particular response quantity not exceeding a corresponding threshold. Once the AFD has been specified the method proceeds with a modified subset simulation procedure where the first step involves the direct simulation of samples in the AFD, rather than standard Monte Carlo simulation as required in standard subset simulation. While the method is applicable to general nonlinear reliability problems herein the focus is on the calculation of the probability of failure of nonlinear dynamical systems subjected to Gaussian random excitations. The method is demonstrated through such a numerical example involving two reliability objectives and a very large number of random variables. It is found that ADM is very efficient and offers drastic improvements over standard subset simulation, especially when one deals with low probability failure events.

• 한국통신학회논문지
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• 제34권3B호
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• pp.213-224
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• 2009

Most of existing routing methods in wireless sensor networks to counter the local eavesdropping-based packet-tracing deal with a single asset and suffer from the packet-delivery latency as they prefer to take a separate path of many hops for each packet being sent. Recently, the author proposed a routing method, GSLP-w(GPSR-based Source-Location Privacy with crew size w), that enhances location privacy of the packet-originating node(i.e., active source) in the presence of multiple assets, yet taking a path of not too long. In this paper, we present a refined routing(i.e., next-hop selection) procedure of it and empirically study privacy strength and delivery latency with varying the crew size w(i.e., the number of packets being sent per path). It turns out that GSLP-w offers the best privacy strength when the number of packets being sent per path is randomly chosen from the range [$1,h_{s-b}/4$] and that further improvements on the privacy are achieved by increasing the random walk length TTLrw or the probability prw that goes into random walk(where, $h_{s-b}$ is the number of hops of the shortest path between packet-originating node s and sink b).

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1993년도 가을 학술발표회논문집
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• pp.248-255
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• 1993

Recently, due to the increasing attention to the structural safety under uncertain environments, many researches on the structural reliability analysis have been peformed. Some useful methods are available to evaluate performance reliability of structures with explicit limit states. However, for large structures, in which structural behaviors can be analyzed with finite element models and the limit states are only expressed implicitly, Monte-Carlo simulation method has been mainly used. However, Monte-Carlo simulation method spends too much computational time on repetitive structural analysis. Many alternative methods are suggested to reduce the computational work required in Monte-Carlo simulation. Response surface method is widely used to improve the efficiency of structural reliability analysis. Response surface method is based on the concept of approximating simple polynomial function of basic random variables for the limit state which is not easily expressed in explicit forms of design random variables. The response surface method has simple algorithm. However, the accuracy of results highly depends on how properly the stochastic characteristics of the original limit state has been represented by approximated function, In this study, an improved response surface method is proposed in which the sampling points for creating response surface are modified to represent the failure surface more adequately and the combined use of a linear response surface function and Rackwitz-Fiessler method has been employed. The method is found to be more effective and efficient than previous response surface methods. In addition more consistent convergence is achieved, Accuracy of the proposed method has been investigated through example.