Search Results - (( data selection method algorithm ) OR ( data estimation methods algorithm ))

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  1. 1
    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
    “…In particular, an algorithm on model selection for seemingly unrelated regression equations model using iterative feasible generalized least squares estimation method is proposed. …”
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  2. 2
    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 simulation study results and real data sets indicate that the proposed MRFCHCS+LAD-SCAD estimator was found to be the best method compared to other methods in this study.…”
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    Thesis
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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
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  4. 4
    System identification using Extended Kalman Filter

    System identification using Extended Kalman Filter by Alias, Ahmad Hafizi

    Published 2017
    “…Besides, Extended Kalman Filter (EKF) algorithm was selected in this project as an algorithm for offline estimation data purposes. …”
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    Student Project
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  5. 5
    Parameter estimation in double exponential smoothing using genetic algorithm / Foo Fong Yeng, Lau Gee Choon and Zuhaimy Ismail

    Parameter estimation in double exponential smoothing using genetic algorithm / Foo Fong Yeng, Lau Gee Choon and Zuhaimy Ismail by Foo, Fong Yeng, Lau, Gee Choon, Ismail, Zuhaimy

    Published 2014
    “…Exponential smoothing is a simple extrapolative method that seeks to identify pattern of past data. …”
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    Research Reports
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  6. 6
    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares

    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

    Published 2009
    “…The performances of the newly proposed methods are investigated extensively by real data sets and simulations study. …”
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  7. 7
    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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  8. 8
    PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah

    PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah by Abdullah, Siti Muniroh

    Published 2017
    “…Results suggest that the PSO algorithm is viable alternative to other established algorithms for LLS parameter estimation. …”
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  9. 9
    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
    “…This merit is provided by balancing the exploitation of solution structure and exploration of its appropriate weighting factors through use of a robust and efficient optimization algorithm in learning process of GEP approach. To assess the applicability and accuracy of the proposed method for long-term electrical energy consumption, its estimates are compared with those obtained from artificial neural network (ANN), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), rule-based data mining algorithm, GEP, linear, quadratic and exponential models optimized by particle swarm optimization (PSO), cuckoo search algorithm (CSA), artificial cooperative search (ACS) algorithm and backtracking search algorithm (BSA). …”
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  10. 10
    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
    “…To overcome the instability selection problem, a stability selection approach is put forward to enhance the performance of single-split variable selection method. …”
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  11. 11
    Simulation algorithm of bayesian approach for choice-conjoint model

    Simulation algorithm of bayesian approach for choice-conjoint model by Zulhanif

    Published 2011
    “…Generally in Choice-Conjoint method the Multinomial Logit Model (MNL) is normally used to analyze choice conjoint data, but the MNL has some serious limitations. …”
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  12. 12
    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
    “…The empirical results for both algorithms performed well as compared to other models selection procedures, particularly using WQI data where the sample size is bigger and has good quality data. …”
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  13. 13
    Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach

    Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach by Nurnajmin Qasrina Ann, ., Pebrianti, Dwi, Mohamad Fadhil, Abas, Bayuaji, Luhur

    Published 2022
    “…Accuracy by using 80:20 ratio of training and test data gives result 98% of accurate training data, and 73% of test data are predicted with the proposed algorithm while 91 and 40% of the DNN models are predicted in training and test data.…”
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    Conference or Workshop Item
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  14. 14
    Extreme air pollutant data analysis using classical and Bayesian approaches

    Extreme air pollutant data analysis using classical and Bayesian approaches by Mohd Amin, Nor Azrita

    Published 2015
    “…This study started with the analysis of extreme PM10 data based on maximum likelihood estimation technique. …”
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  15. 15
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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  16. 16
    Tree-based contrast subspace mining method

    Tree-based contrast subspace mining method by Florence Sia Fui Sze

    Published 2020
    “…The research works involve first preparing the real world numerical and categorical data sets. Then, the tree-based method, the genetic algorithm based parameter values identification of tree-based method, and followed by the genetic algorithm based tree-based method, for numerical data sets are developed and evaluated. …”
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  17. 17
    Orthogonal least square algorithm and its application for modelling suspension system

    Orthogonal least square algorithm and its application for modelling suspension system by Ahmad, Robiah, Jamaluddin, Hishamuddin

    Published 2001
    “…Modelling based on input and output data is known as system identification. One of the issues in system identification is the parameter estimation and model structure selection where various methods have been studied including the orthogonal least square algorithm. …”
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  18. 18
    Depth linear discrimination-oriented feature selection method based on adaptive sine cosine algorithm for software defect prediction

    Depth linear discrimination-oriented feature selection method based on adaptive sine cosine algorithm for software defect prediction by Nasser, Abdullah, H.M. Ghanem, Waheed Ali, H.Y. Saad, Abdul-Malik, Hamed Abdul-Qawy, Antar Shaddad, A. Ghaleb, Sanaa A, Mohammed Alduais, Nayef Abdulwahab, Din, Fakhrud, Ghetas, Mohamed

    Published 2024
    “…To address these challenges, this research introduces a novel Depth Linear Discrimination-Oriented Feature Selection Method based on Adaptive Sine Cosine Algorithm, named Depth Adaptive Sine Cosine Feature Selection (DASC-FS). …”
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  19. 19
    Indoor positioning using weighted magnetic field signal distance similarity measure and fuzzy based algorithms

    Indoor positioning using weighted magnetic field signal distance similarity measure and fuzzy based algorithms by Bundak, Caceja Elyca

    Published 2021
    “…For the triangle area algorithm, the smallest triangle area is selected from the triangle formed from the matching RPs cluster to estimate location. …”
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  20. 20
    Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network

    Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network by Zafar, R., Kamel, N., Naufal, M., Malik, A.S., Dass, S.C., Ahmad, R.F., Abdullah, J.M., Reza, F.

    Published 2017
    “…MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. …”
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