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  1. 1
    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, the integration of EM algorithm estimation is applicable in improving the performance of automated model selection procedures for multiple equations models…”
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  2. 2
    Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm

    Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm by Nur Azulia, Kamarudin

    Published 2019
    “…This extension of Autometrics for model selection was also developed for multiple equations by integrating it with seemingly unrelated regressions equations (SURE) and estimated using feasible generalized least squares (FGLS), known as SURE-Autometrics algorithm. …”
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  3. 3
    Model selection approaches of water quality index data

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

    Published 2016
    “…Automatic model selection by using algorithm can avoid huge variability in model specification process compared to manual selection.With the employment of algorithm, the right model selected is then also used for forecasting purposes. …”
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  4. 4
    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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  5. 5
    Comparison between Newton’s Method and a new Scaling Newton Method / Ramizah Baharuddin

    Comparison between Newton’s Method and a new Scaling Newton Method / Ramizah Baharuddin by Baharuddin, Ramizah

    Published 2021
    “…Being one of the most widely used method of root finding, the procedure attempt to find a solution of the equation f (x) = 0 where f (x) is a function of one variable, continuous and differentiable. …”
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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
    “…Simple mediation model consists of three regression equations. The Ordinary Least Squares (OLS) method is often use to estimate the parameters of the mediation model. …”
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  7. 7
    Power System State Estimation In Large-Scale Networks

    Power System State Estimation In Large-Scale Networks by NURSYARIZAL MOHD NOR, NURSYARIZAL

    Published 2010
    “…The Weighted Least Squares (WLS) method is the most popular technique of SE. This thesis provides solutions to enhance the WLS algorithm in order to increase the performance of SE. …”
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  8. 8
    Fuzzy Type-1 Triangular Membership Function Approximation Using Fuzzy C-Means

    Fuzzy Type-1 Triangular Membership Function Approximation Using Fuzzy C-Means by Azam, M.H., Hasan, M.H., Hassan, S., Abdulkadir, S.J.

    Published 2020
    “…Using a set of equations, these values then estimate the parameters of the triangular membership function. …”
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  9. 9
    Comparing three methods of handling multicollinearity using simulation approach

    Comparing three methods of handling multicollinearity using simulation approach by Adnan, Norliza

    Published 2006
    “…The algorithm is described and for the purpose of comparing the three methods, simulated data sets where the number of cases was less than the number of observations were used. …”
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  10. 10
    A Comparative Study On Some Methods For Handling Multicollinearity Problems

    A Comparative Study On Some Methods For Handling Multicollinearity Problems by Adnan, Norliza, Ahmad, Maizah Hura, Adnan, Robiah

    Published 2006
    “…The algorithm is described and for the purpose of comparing the three methods, simulated data sets where the number of cases were less than the number of observations used. …”
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  11. 11
    A comparative study on some methods for handling multicollinearity problems

    A comparative study on some methods for handling multicollinearity problems by Adnan, Norliza, Ahmad, Maizah Hura, Adnan, Robiah

    Published 2006
    “…The algorithm is described and for the purpose of comparing the three methods, simulated data sets where the number of cases were less than the number of observations used. …”
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  12. 12
    DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS

    DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS by AYOUB MOHAMMED, MOHAMMED ABDALLA

    Published 2011
    “…Graphical User Interface (GUI) has been generated to help apply the ANN model results while an applicable equation can be used for Group Method of Data Handling model. …”
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  13. 13
    Seismic wave modeling and high-resolution imaging

    Seismic wave modeling and high-resolution imaging by Bashir, Y., Ghosh, D.P., Alashloo, S.Y.M.

    Published 2022
    “…Dip frequency filtering (DFF) is a quick and reliable method for preserving diffraction. To suppress data reflection, this algorithm works in the frequency domain by defining a slope filter with respect to dt/dx. …”
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  14. 14
    Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data

    Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data by Oyebayo, Olaniran Ridwan

    Published 2018
    “…Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. …”
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  15. 15
    Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption

    Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption by Zamani, Seyed Ali

    Published 2015
    “…The incremental back propagation algorithm demonstrated the best results and which has been used as learning algorithm for ANN in combination with Genetic Algorithm in the optimization. …”
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  16. 16
    Study on numerical solution of a variable order fractional differential equation based on symmetric algorithm

    Study on numerical solution of a variable order fractional differential equation based on symmetric algorithm by Liu, Jingrui, Pan, Dongyang

    Published 2019
    “…A fully symmetric classification of the boundary value problem for a class of fractional differential equations with variable sequences is determined by using a fully symmetric differential sequence sorting algorithm. …”
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  17. 17
    Semiparametric inference procedure for the accelarated failure time model with interval-censored data

    Semiparametric inference procedure for the accelarated failure time model with interval-censored data by Karimi, Mostafa

    Published 2019
    “…The rank-based methods, estimating algorithms, and resampling techniques that are developed do not involve the difficulties of the existing estimating procedures. …”
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  18. 18
    Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network

    Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network by Mohamad Afiq, Mohd Asrul

    Published 2023
    “…Therefore, the main objective of this study is to improve the validity and reliability of the simplified biofilm growth model to optimize the laboratory MEC process of biohydrogen production from sago wastewater substrate using MATLAB. Model validity describes the first sub-objective, which is to solve the complexity of the nonlinear interaction of multiple MEC input variables related to the hydrogen production rate response using artificial neural networks (ANN) before validating the mathematical modeling results by comparing experimental data with the predicted substrate concentration profile and hydrogen production rate profile based on the re-estimated input values of the model parameters using single-objective optimization based on the nonlinear convex method using gradient descent algorithm. …”
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  19. 19
    SURE-Autometrics algorithm for model selection in multiple equations

    SURE-Autometrics algorithm for model selection in multiple equations by Norhayati, Yusof

    Published 2016
    “…Thus, this study aims to develop an algorithm for model selection in multiple equations focusing on seemingly unrelated regression equations (SURE) model. …”
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  20. 20
    Multiple equations model selection algorithm with iterative estimation method

    Multiple equations model selection algorithm with iterative estimation method by Kamarudin, Nur Azulia, Ismail, Suzilah

    Published 2016
    “…This estimation method is equivalent to maximum likelihood estimation at convergence. …”
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