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  • Open Access


    Index-adaptive Triangle-Based Graph Local Clustering

    Zhe Yuan*, Zhewei Wei, Ji-rong Wen

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5009-5026, 2023, DOI:10.32604/cmc.2023.038531

    Abstract Motif-based graph local clustering (MGLC) algorithms are generally designed with the two-phase framework, which gets the motif weight for each edge beforehand and then conducts the local clustering algorithm on the weighted graph to output the result. Despite correctness, this framework brings limitations on both practical and theoretical aspects and is less applicable in real interactive situations. This research develops a purely local and index-adaptive method, Index-adaptive Triangle-based Graph Local Clustering (TGLC+), to solve the MGLC problem w.r.t. triangle. TGLC+ combines the approximated Monte-Carlo method Triangle-based Random Walk (TRW) and deterministic Brute-Force method Triangle-based Forward Push More >

  • Open Access


    A Spacecraft Equipment Layout Optimization Method for Diverse and Competitive Design

    Wei Cong, Yong Zhao*, Bingxiao Du*, Senlin Huo, Xianqi Chen

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 621-654, 2023, DOI:10.32604/cmes.2023.025143

    Abstract The spacecraft equipment layout optimization design (SELOD) problems with complicated performance constraints and diversity are studied in this paper. The previous literature uses the gradient-based algorithm to obtain optimized non-overlap layout schemes from randomly initialized cases effectively. However, these local optimal solutions are too difficult to jump out of their current relative geometry relationships, significantly limiting their further improvement in performance indicators. Therefore, considering the geometric diversity of layout schemes is put forward to alleviate this limitation. First, similarity measures, including modified cosine similarity and gaussian kernel function similarity, are introduced into the layout optimization More >

  • Open Access


    Oversampling Method Based on Gaussian Distribution and K-Means Clustering

    Masoud Muhammed Hassan1, Adel Sabry Eesa1,*, Ahmed Jameel Mohammed2, Wahab Kh. Arabo1

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 451-469, 2021, DOI:10.32604/cmc.2021.018280

    Abstract Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years. When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority. Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications. Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this More >

  • Open Access


    Oversampling Methods Combined Clustering and Data Cleaning for Imbalanced Network Data

    Yang Yang1,*, Qian Zhao1, Linna Ruan2, Zhipeng Gao1, Yonghua Huo3, Xuesong Qiu1

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1139-1155, 2020, DOI:10.32604/iasc.2020.011705

    Abstract In network anomaly detection, network traffic data are often imbalanced, that is, certain classes of network traffic data have a large sample data volume while other classes have few, resulting in reduced overall network traffic anomaly detection on a minority class of samples. For imbalanced data, researchers have proposed the use of oversampling techniques to balance data sets; in particular, an oversampling method called the SMOTE provides a simple and effective solution for balancing data sets. However, current oversampling methods suffer from the generation of noisy samples and poor information quality. Hence, this study proposes More >

  • Open Access


    Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

    Amer Ibrahim Al-Omari1, *, Ibrahim M. Almanjahie2, Amal S. Hassan3, Heba F. Nagy4

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 835-857, 2020, DOI:10.32604/cmc.2020.10944

    Abstract In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained… More >

  • Open Access


    The Volatility of High-Yield Bonds Using Mixed Data Sampling Methods

    Maojun Zhang1,2, Jiajin Yao1, Zhonghang Xia3, Jiangxia Nan1,*, Cuiqing Zhang1

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1233-1244, 2019, DOI:10.32604/cmc.2019.06118

    Abstract It is well known that economic policy uncertainty prompts the volatility of the high-yield bond market. However, the correlation between economic policy uncertainty and volatility of high-yield bonds is still not clear. In this paper, we employ GARCH-MIDAS models to investigate their correlation with US economic policy uncertainty index and S&P high-yield bond index. The empirical studies show that mixed volatility models can effectively capture the realized volatility of high-yield bonds, and economic policy uncertainty and macroeconomic factors have significant effects on the long-term component of high-yield bonds volatility. More >

  • Open Access


    Probabilistic Floor Response Spectrum of Nonlinear Nuclear Power Plant Structure using Latin Hypercube Sampling Method

    Heekun Ju, Hyung-Jo Jung*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.1, pp. 7-7, 2019, DOI:10.32604/icces.2019.05846

    Abstract Latin hypercube sampling (LHS) is widely applied to estimate a probabilistic floor response spectrum (FRS) of nonlinear nuclear power plant (NPP) structure. ASCE 4-16 Standards recommend that the minimum number of simulations should be larger than 30 when using LHS. Although this recommendation is commonly used for the minimum number of the simulation, there is no theoretical background. The variability of the estimations may exist according to the number of the simulation. Stated differently, the minimum number of the simulation may be varied depending on the characteristics of the problem (i.e., problem-dependent). In this context,… More >

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