Search Results - (( weibull distribution ((_ algorithm) OR (new algorithm)) ) OR ( pre evaluation case algorithm ))

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  1. 1

    Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah by Abdullah, Mohd Azrul

    Published 2015
    “…Analysis results present Weibull Distribution model is the best fitted model. …”
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    Article
  2. 2

    Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah by Abdullah, Mohd Azrul

    Published 2015
    “…Analysis results present Weibull Distribution model is the best fitted model. …”
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    Thesis
  3. 3

    Adaptive policing and shaping algorithms on inbound traffic using generalized Pareto distribution / Nor Azura Ayop by Ayop, Nor Azura

    Published 2016
    “…Log likelihood estimation technique is used to fit the best 2-parameter CDF compared to Weibull, Normal and Rician distribution model. The percentage level 5% under original bandwidth used is developed on policing and shaping algorithms to control bandwidth used. …”
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    Thesis
  4. 4

    Estimation Of Weibull Parameters Using Simulated Annealing As Applied In Financial Data by Hamza, Abubakar

    Published 2023
    “…The finding reveals that the Weibull distribution is well-suited to describing the investment behaviour of the MPS based on the estimates via the SA algorithm. …”
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    Thesis
  5. 5

    Competing risks for reliability analysis using Cox’s model by Mohamed Elfaki, Faiz Ahmed, Daud, Isa, Ibrahim, Nor Azowa, Abdullah, M. Y., Usman, Mustofa

    Published 2007
    “…Purpose – Cox’s model with Weibull distribution and Cox’s with exponential distribution are the most important models in reliability analysis. …”
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    Article
  6. 6

    Bayesian survival and hazard estimates for Weibull regression with censored data using modified Jeffreys prior by Ahmed, Al Omari Mohammed

    Published 2013
    “…For the Weibull model with right censoring and unknown shape, the full conditional distribution for the scale and shape parameters are obtained via Gibbs sampling and Metropolis-Hastings algorithm from which the survival function and hazard function are estimated. …”
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    Thesis
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    Parameter estimation of Kumaraswamy Burr type X models based on cure models with or without covariates by Yusuf, Madaki Umar

    Published 2017
    “…In this thesis, we considered two methods via the classical maximum likelihood estimation (MLE) and the Bayes estimation using the Gibbs sampling (G-S) algorithm to estimate the parameters of BKBX, KBX and Beta-Weibull (BWB) models. …”
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    Thesis
  9. 9

    Some families of count distributions for modelling zero-inflation and dispersion / Low Yeh Ching by Low, Yeh Ching

    Published 2016
    “…Two new mixed Poisson distributions, namely a three-parameter Poisson-exponentiated Weibull distribution and a fourparameter generalized Sichel distribution is introduced to model over dispersed, zeroinflated and long-tailed count data. …”
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    Thesis
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    Peak time bandwidth control algorithms with fitted traffic model on broadband network YouTube video traffic: article / Aini Azmi by Azmi, Aini

    Published 2016
    “…Bandwidth Control Algorithms is developed based on Peak Time of day and night. …”
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    Article
  13. 13

    Peak time bandwidth control algorithms with fitted traffic model on broadband network YouTube video traffic / Aini Azmi by Azmi, Aini

    Published 2016
    “…Bandwidth Control Algorithms is developed based on Peak Time of day and night. …”
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    Thesis
  14. 14

    An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia by Hwang Y.K., Ibrahim M.Z., Ahmed A.N., Albani A.

    Published 2023
    “…Data mining; Genetic algorithms; Meteorology; Neural networks; Planning; Sustainable development; Weibull distribution; Climate forecasts; Measure-correlate-predict; Measurement instruments; Measurement sites; Meteorological data; Reanalysis; Weibull frequency; Wind measurement; Forecasting…”
    Conference Paper
  15. 15

    Mixture model of the Exponential, Gamma and Weibull distributions to analyse heterogeneous survival data by Mohammed, Yusuf Abbakar, Yatim, Bidin, Ismail, Suzilah

    Published 2015
    “…Aims: In this study a survival mixture model of three components is considered to analyse survival data of heterogeneous nature.The survival mixture model is of the Exponential, Gamma and Weibull distributions.Methodology: The proposed model was investigated and the Maximum Likelihood (ML) estimators of the parameters of the model were evaluated by the application of the Expectation Maximization Algorithm (EM).Graphs, log likelihood (LL) and the Akaike Information Criterion (AIC) were used to compare the proposed model with the pure classical parametric survival models corresponding to each component using real survival data.The model was compared with the survival mixture models corresponding to each component.Results: The graphs, LL and AIC values showed that the proposed model fits the real data better than the pure classical survival models corresponding to each component.Also the proposed model fits the real data better than the survival mixture models corresponding to each component. …”
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    Article
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    Multiobjective optimization using particle swarm optimization with non-Gaussian random generators by Ganesan, T., Vasant, P., Elamvazuthi, I.

    Published 2016
    “…The two non-Gaussian distributions are the Weibull and Gamma distributions. …”
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    Article
  18. 18

    Multiobjective optimization using particle swarm optimization with non-Gaussian random generators by Ganesan, T., Vasant, P., Elamvazuthi, I.

    Published 2016
    “…The two non-Gaussian distributions are the Weibull and Gamma distributions. …”
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    Article
  19. 19

    Multiobjective optimization using particle swarm optimization with non-Gaussian random generators by Ganesan, T., Vasant, P., Elamvazuthi, I.

    Published 2016
    “…The two non-Gaussian distributions are the Weibull and Gamma distributions. …”
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    Article
  20. 20

    Multiobjective optimization using particle swarm optimization with non-Gaussian random generators by Ganesan, T., Vasant, P., Elamvazuthi, I.

    Published 2016
    “…The two non-Gaussian distributions are the Weibull and Gamma distributions. …”
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    Article