Search Results - (( variables regression methods algorithm ) OR ( java application optimization algorithm ))

Refine Results
  1. 1
  2. 2

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
    Get full text
    Get full text
    Conference or Workshop Item
  3. 3

    Route Optimization System by Zulkifli, Abdul Hayy

    Published 2005
    “…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
    Get full text
    Get full text
    Final Year Project
  4. 4

    Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition by Al Jawarneh, Abdullah Suleiman Saleh

    Published 2021
    “…The proposed techniques are compared with four traditional regression methods employed in the previous study.…”
    Get full text
    Get full text
    Thesis
  5. 5

    Penalized Quantile Regression Methods And Empirical Mode Decomposition For Improving The Accuracy Of The Model Selection by Ambark, Ali Saleh Al-Massri

    Published 2024
    “…Therefore, three methods based on a combination of the empirical mode decomposition (EMD) algorithm and penalized quantile regression have been proposed in this study. …”
    Get full text
    Get full text
    Thesis
  6. 6
  7. 7
  8. 8

    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. …”
    Get full text
    Get full text
    Thesis
  9. 9

    Characteristic wavelength optimization for partial least squares regression using improved flower pollination algorithm by Pauline Ong, Pauline Ong, Jinbao Jian, Jinbao Jian, Jianghua Yin, Jianghua Yin, Guodong Ma, Guodong Ma

    Published 2023
    “…This study proposes a new wavelength selection method, interval flower pollination algorithm (iFPA), for spectral variable selection in the partial least squares regression (PLSR) model. …”
    Get full text
    Get full text
    Get full text
    Article
  10. 10
  11. 11
  12. 12

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

    Published 2006
    “…In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory variables. …”
    Get full text
    Get full text
    Thesis
  13. 13
  14. 14

    The use of Cox regression and genetic algorithm (CoRGA) for identifying risk factors for mortality in older people by Ahmad, Rabiah, Bath, Peter A

    Published 2004
    “…However, research has been limited by the range of risk factors included in regression models. This is partly because traditional statistical methods and software packages allow a restricted number of variables and combinations of variables. …”
    Get full text
    Get full text
    Get full text
    Article
  15. 15

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

    Published 2006
    “…In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory variables. …”
    Get full text
    Get full text
    Get full text
    Article
  16. 16

    Elastic net penalized Quantile Regression Model and Empirical Mode Decomposition for Improving the Accuracy of the Model Selection by Ali S.A. Ambark, Mohd Tahir Ismail, Abdullah S. Al-Jawarneh, Samsul Ariffin Abdul Karim

    Published 2023
    “…Such methods are ridge penalized quantile regression, lasso penalized quantile regression, and elastic net penalized quantile regression which are used for variable selection and regularization and deals with the multicollinearity problem when it exists between the predictor variables. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  17. 17

    The performance of Taguchi�s T-method with binary bat algorithm based on great value priority binarization for prediction by Marlan Z.M., Ramlie F., Jamaludin K.R., Harudin N.

    Published 2023
    “…In enhancing prediction accuracy, the T-method employed Taguchi�s orthogonal array as a variable selection approach to determine a subset of independent variables that are significant toward the dependent variable or output. …”
    Article
  18. 18

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

    Published 2006
    “…In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory variables. …”
    Get full text
    Get full text
    Get full text
    Article
  19. 19

    Comparison between fuzzy bootstrap weighted multiple linear regression and multiple linear regression: a case study for oral cancer modelling by Mohd Ibrahim, Mohamad Shafiq, Wan Ahmad, Wan Muhamad Amir, Hasan, Ruhaya, Harun, Masitah Hayati

    Published 2018
    “…Methodology: Methodology building is based on the SAS algorithm (SAS 9.4 software) which is a robust computational statistic that consists the combination of robust regression, bootstrap, weighted data, Bayesian, and fuzzy regression method. …”
    Get full text
    Get full text
    Proceeding Paper
  20. 20