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  1. 1
    Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani

    Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani by Ehsan Taslimi , Renani

    Published 2018
    “…In this method, firstly, Weibull density function is utilized to model the wind speed and then several methods are applied to estimate the parameters of the wind speed distribution. …”
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  2. 2
    Estimation Of Weibull Parameters Using Simulated Annealing As Applied In Financial Data

    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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  3. 3
    Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah

    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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  4. 4
    Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah

    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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  5. 5
    Competing risks for reliability analysis using Cox’s model

    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
    “…Findings – A simulation study indicates that the parametric Cox’s model with Weibull distribution gives similar results to Cox’s with exponential distribution, especially for a large sample size. …”
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  6. 6
    Bayesian survival and hazard estimates for Weibull regression with censored data using modified Jeffreys prior

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

    Published 2013
    “…In this study, firstly, consideration is given to the traditional maximum likelihood estimator and the Bayesian estimator by employing Jeffreys prior and Extension of Jeffreys prior information on the Weibull distribution with a given shape under right censored data. …”
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  7. 7
    An analytical approach on parametric estimation of cure fraction based on weibull distribution using interval censored data.

    An analytical approach on parametric estimation of cure fraction based on weibull distribution using interval censored data. by I. Aljawadi , Bader Ahmad, Abu Bakar, Mohd Rizam, Ibrahim, Noor Akma

    Published 2011
    “…We propose this cure rate model based on the Weibull distribution with interval censored data. …”
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  8. 8
    Variational Bayesian inference for exponentiated Weibull right censored survival data

    Variational Bayesian inference for exponentiated Weibull right censored survival data by Jibril Abubakar, Jibril Abubakar, Mohd Asrul Affendi Abdullah, Mohd Asrul Affendi Abdullah, Oyebayo Ridwan Olaniran, Oyebayo Ridwan Olaniran

    Published 2023
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  9. 9
    Probabilistic evaluation of wind power generation

    Probabilistic evaluation of wind power generation by Razali N.M.M., Misbah M.

    Published 2023
    “…The paper presents an algorithm developed for a random wind speed generator governed by the probability density function of Weibull distribution and evaluates the WTG's output by using the power curve of wind turbines. …”
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  10. 10
    Peak time bandwidth control algorithms with fitted traffic model on broadband network YouTube video traffic: article / Aini Azmi

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

    Published 2016
    “…This paper presents an analysis of video traffic and fitted to best distribution traffic model to control bandwidth usage in a broadband network. …”
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  11. 11
    Peak time bandwidth control algorithms with fitted traffic model on broadband network YouTube video traffic / Aini Azmi

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

    Published 2016
    “…This paper presents an analysis of video traffic and fitted to best distribution traffic model to control bandwidth usage in a broadband network. …”
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  12. 12
    An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia

    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
    Conference Paper
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  13. 13
    Statistical modeling of the electromechanical behaviour of BI-2223 composite wires

    Statistical modeling of the electromechanical behaviour of BI-2223 composite wires by Muhammad Faris Fuaad

    Published 2024
    text::Final Year Project
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  14. 14
    Multiobjective optimization using particle swarm optimization with non-Gaussian random generators

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

    Published 2016
    “…The stochastic engines operate using the Weibull distribution, Gamma distribution, Gaussian distribution and a chaotic mechanism. …”
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  15. 15
    Multiobjective optimization using particle swarm optimization with non-Gaussian random generators

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

    Published 2016
    “…The stochastic engines operate using the Weibull distribution, Gamma distribution, Gaussian distribution and a chaotic mechanism. …”
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  16. 16
    Multiobjective optimization using particle swarm optimization with non-Gaussian random generators

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

    Published 2016
    “…The stochastic engines operate using the Weibull distribution, Gamma distribution, Gaussian distribution and a chaotic mechanism. …”
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  17. 17
    Multiobjective optimization using particle swarm optimization with non-Gaussian random generators

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

    Published 2016
    “…The stochastic engines operate using the Weibull distribution, Gamma distribution, Gaussian distribution and a chaotic mechanism. …”
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  18. 18
    Robustness Analysis Of An Optimized Controller Via Particle Swarm Algorithm

    Robustness Analysis Of An Optimized Controller Via Particle Swarm Algorithm by Chong, Chee Soon, Ghazali, Rozaimi, Jaafar, Hazriq Izzuan, Syed Hussein, Syarifah Yuslinda, Md Rozali, Sahazati

    Published 2017
    “…The finding shows that the SMC that utilized the PSO algorithm parameters are capable to produce smaller robustness index values, which demonstrated better robustness characteristic in confront with the variation of the system parameter.…”
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  19. 19
    Mixture model of the Exponential, Gamma and Weibull distributions to analyse heterogeneous survival data

    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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  20. 20
    Design of low order quantitative feedback theory and H-infinity-based controllers using particle swarm optimisation for a pneumatic actuator system

    Design of low order quantitative feedback theory and H-infinity-based controllers using particle swarm optimisation for a pneumatic actuator system by Ali, Hazem I.

    Published 2010
    “…These algorithms are designed to achieve the robustness over a wide range of system parameters change and disturbances. …”
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