Search Results - (( simulation competing values algorithm ) OR ( java implication tree algorithm ))

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  1. 1

    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
    “…Also, the modification of the two models showed better results compared with Crowder et al., especially for the second causes of failure. Originality/value – A modification of the two competing risk models has mostly been applied in failure time data and simulation data. …”
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  2. 2

    Cutpoint determination methods in competing risks subdistribution model by Noor Akma Ibrahim, Abdul Kudus, Isa Daud, Mohd. Rizam Abu Bakar

    Published 2009
    “…Thus, we consider the problem of obtaining a threshold value of a continuous covariate given a competing risk survival time response using a binary partitioning algorithm as a way to optimally partition data into two disjoint sets. …”
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  3. 3

    Cutpoint determination methods in competing risks subdistribution model by Ibrahim, Noor Akma, Kudus, Abdul, Daud, Isa, Abu Bakar, Mohd Rizam

    Published 2009
    “…Thus, we consider the problem of obtaining a threshold value of a continuous covariate given a competing risk survival time response using a binary partitioning algorithm as a way to optimally partition data into two disjoint sets. …”
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  4. 4

    Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation by Khan, Abdullah

    Published 2022
    “…This thesis proposes and simulates the three novel optimization algorithms to handle DG allocation, different single-objective, and multi-objective OPF problems. …”
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  5. 5

    Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model by Elfaki, Faiz. A. M.

    Published 2000
    “…A generated data where the failure times were taken as exponentially distributed was used to further compare these two methods of estimation. From the simulation study for this particular case, we can conclude that the EM algorithm proved to be more superior in terms of mean value of parameter estimates, bias and root mean square error. …”
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  6. 6

    Development of FRID based 2d localization simulation for autonomous guided vehicle tracking in indoor environment by Al Hazza, Muataz Hazza Faizi, Zainal, Nur Izzati Zainal, Rodzi, Mohd Zuhaili Mohd

    Published 2018
    “…The localization techniques are based on measuring the distance using path loss model from RSSI values provided by RFID and coordinates calculation using trilateration algorithm with multiple reference points. …”
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  7. 7

    Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis by Mohamed Elfaki, Faiz Ahmed

    Published 2004
    “…The Expectation Maximization (EM) algorithm is utilized to obtain the estimate of the parameters in the models. …”
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  8. 8

    Finite impulse response optimizers for solving optimization problems by Tasiransurini, Ab Rahman

    Published 2019
    “…Simulated Kalman filter (SKF) algorithm is one of the algorithms under this classification. …”
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  9. 9

    Finite impulse response optimizers for solving optimization problems by Ab Rahman, Tasiransurini

    Published 2019
    “…Simulated Kalman filter (SKF) algorithm is one of the algorithms under this classification. …”
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  10. 10
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  12. 12

    Blockchain based security framework for device authentication and data communication in decentralized IoT network by Alahmed, Mahmoud Tayseer

    Published 2023
    “…The receiver would compare the hash value to that stored in the authentication table to authenticate the sender. …”
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  13. 13

    Self-configured link adaptation using channel quality indicator-modulation and coding scheme mapping with partial feedback for green long-term evolution cellular systems by Salman, Mustafa Ismael

    Published 2015
    “…To achieve this objective, an iterative approach based on swarm intelligence is used to find the optimal CQI threshold at which the competing criteria are optimized. Since the developed downlink scheduler and the partial feedback scheme affect the QoS, self-configured versions of both algorithms are developed to provide QoS provisioning. …”
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  14. 14

    Modelling and Forecasting the Kuala Lumpur Composite Index Rate of Returns Using Generalised Autoregressive Conditional Heteroscedasticity Models by Abdul Muthalib, Maiyastri

    Published 2004
    “…It is found that this method has a clear edge over its rival because PCA uses actual values of the goodness of fit test criteria (LogL, SBC, and AIC in estimation and RMSE, MAE, AMAPE and MAPE in forecasting) and hence the inability to specify exactly the relative positions of each of the competing models as faced by the ranking method may be overcome. …”
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