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  1. 1
    Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data

    Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data by Baba, Ishaq Abdullahi

    Published 2022
    “…The proposed method inherits the robustness properties of the original RFCH estimators. …”
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  2. 2
    Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique

    Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique by Hardev Singh, Jitvinder Dev Singh

    Published 2015
    “…There are many types of motion estimation method but the most used method is the block matching method which is the fixed block matching and the variable block matching. …”
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  3. 3
    Railway wheelset parameter estimation using signals from lateral velocity sensor

    Railway wheelset parameter estimation using signals from lateral velocity sensor by Selamat, H., Alimin, A. J., Sam, Y.M.

    Published 2008
    “…The algorithm includes an instrumental variable (IV) element to reduce estimation bias and a variable forgetting factor for good parameter tracking and smooth steady state. …”
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  4. 4
    Parameter estimation of a continuous-time plant – the least-absolute error with variable forgetting factor method

    Parameter estimation of a continuous-time plant – the least-absolute error with variable forgetting factor method by Selamat, Hazlina, Yusof, Rubiyah, Goodall, Roger M.

    Published 2005
    “…The algorithm includes an instrumental variable (IV) element to reduce estimation bias and a variable forgetting factor for good parameter tracking and smooth steady state.…”
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  5. 5
    Classification of Immunosignature Using Random Forests for Cancer Diagnosis

    Classification of Immunosignature Using Random Forests for Cancer Diagnosis by Zarzar, Mouayad, Razak, Eliza, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah

    Published 2015
    “…In this work, we will develop a robust classification model that can be utilized in cancer diagnosis using immunofingerprint data. …”
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    Proceeding Paper
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  6. 6
    Robust Estimation Methods And Outlier Detection In Mediation Models

    Robust Estimation Methods And Outlier Detection In Mediation Models by Fitrianto, Anwar

    Published 2010
    “…The Ordinary Least Squares (OLS) method is often use to estimate the parameters of the mediation model. …”
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  7. 7
    Finding an effective classification technique to develop a software team composition model

    Finding an effective classification technique to develop a software team composition model by Gilal, Abdul Rehman, Jaafar, Jafreezal, Capretz, Luiz Fernando, Omar, Mazni, Basri, Shuib, Abdul Aziz, Izzatdin

    Published 2017
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  8. 8
    Finding an effective classification technique to develop a software team composition model

    Finding an effective classification technique to develop a software team composition model by Gilal, A.R., Jaafar, J., Capretz, L.F., Omar, M., Basri, S., Aziz, I.A.

    Published 2018
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  9. 9
    Finding an effective classification technique to develop a software team composition model

    Finding an effective classification technique to develop a software team composition model by Gilal, A.R., Jaafar, J., Capretz, L.F., Omar, M., Basri, S., Aziz, I.A.

    Published 2018
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  10. 10
    Deep learning detector for pests and plant disease recognition

    Deep learning detector for pests and plant disease recognition by Ileladewa, Oluwatimilehin Adekunle

    Published 2020
    “…Meanwhile, evolution in deep convolutional neural networks for image classification has rapidly improved the accuracy of object detection, classification and system recognition. …”
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    Final Year Project / Dissertation / Thesis
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  11. 11
    Robust variable selection methods for large- scale data in the presence of multicollinearity, autocorrelated errors and outliers

    Robust variable selection methods for large- scale data in the presence of multicollinearity, autocorrelated errors and outliers by Uraibi, Hassan S.

    Published 2016
    “…The results signify that our proposed RNGVS.RFCH method able to correctly select the important variables in the final model. …”
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  12. 12
    Bayesian logistic regression model on risk factors of type 2 diabetes mellitus

    Bayesian logistic regression model on risk factors of type 2 diabetes mellitus by Chiaka, Emenyonu Sandra

    Published 2016
    “…The significant variables determined by maximum likelihood method were then estimated using the BLR method. …”
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  13. 13
    Y-type Random 2-satisfiability In Discrete Hopfield Neural Network

    Y-type Random 2-satisfiability In Discrete Hopfield Neural Network by Guo, Yueling

    Published 2024
    “…Finally, a new logic mining model namely Y-Type Random 2-Satisfiability Reverse Analysis was proposed, which showed optimal performances in terms of several metrics as compared to the existing classification models. …”
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  14. 14
    Model selection approaches of water quality index data

    Model selection approaches of water quality index data by Kamarudin, Nur Azulia, Ismail, Suzilah

    Published 2016
    “…In order to select the best model, it is vital to ensure that proper estimation method is chosen in the modelling process.Different estimators have been proposed for the estimation of parameters of a model, including the least square and iterative estimators.This study aims to evaluate the forecasting performances of two algorithms on water quality index (WQI) of a river in Malaysia based on root mean square error (RMSE) and geometric root mean square error (GRMSE).Feasible generalised least squares (FGLS) and iterative maximum likelihood (ML) estimation methods are used in the algorithms, respectively.The results showed that SUREMLE-Autometrics has surpassed SURE-Autometrics; another simultaneous selection procedure of multipleequation models.Two individual selections, namely Autometrics-SUREMLE and Autometrics-SURE, though showed consistency only for GRMSE.All in all, ML estimation is a more appropriate method to be employed in this seemingly unrelated regression equations (SURE) model selection.…”
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  15. 15
    Control of IC Engine: Design a Novel MIMO Fuzzy Backstepping Adaptive Based Fuzzy Estimator Variable Structure Control.

    Control of IC Engine: Design a Novel MIMO Fuzzy Backstepping Adaptive Based Fuzzy Estimator Variable Structure Control. by Sulaiman, Nasri, Piltan, Farzin, Talooki, Iraj Asadi, Ferdosali, Payman

    Published 2011
    “…This paper expands a Multi Input Multi Output (MIMO) fuzzy estimator variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm. …”
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  16. 16
    Class binarization with self-adaptive algorithm to improve human activity recognition

    Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…However, the learning complexity of classification is increased due to the expansion number of learning model. …”
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  17. 17
    Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)

    Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925) by Kamarudin, Nur Azulia

    Published 2021
    “…Hence, this study concentrates on an automated model selection procedure for the SURE model by integrating the expectation-maximization (EM) algorithm estimation method, named SURE(EM)-Autometrics. …”
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    Monograph
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  18. 18
    Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri

    Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri by Mohd Asri, Azinuddin

    Published 2022
    “…The suitable independent variables (hL, DBH, and CPA) were vital to estimating the dependent variable (Sc) and producing a carbon stock map for the final result. …”
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  19. 19
    Digital economy tax compliance model in Malaysia using machine learning approach

    Digital economy tax compliance model in Malaysia using machine learning approach by Raja Azhan Syah Raja Wahab, Azuraliza Abu Bakar

    Published 2021
    “…The experimental results show that the ensemble method can improve the single classification model’s accuracy with the highest classification accuracy of 87.94% compared to the best single classification model. …”
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  20. 20
    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli

    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

    Published 2018
    “…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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