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  1. 1
    Improved nu-support vector regression algorithm based on principal component analysis

    Improved nu-support vector regression algorithm based on principal component analysis by Abdullah Mohammed, Rashid, Habshah, Midi

    Published 2023
    “…The performance of the proposed PCA-SVR algorithm is extensively assessed by two real data sets and simulation studies. …”
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  2. 2
    Optimal Weighted Learning of PCA and PLS for Multicollinearity Discriminators and Imbalanced Groups in Big Data (S/O: 13224)

    Optimal Weighted Learning of PCA and PLS for Multicollinearity Discriminators and Imbalanced Groups in Big Data (S/O: 13224) by Mahat, Nor Idayu, Engku Abu Bakar, Engku Muhammad Nazri, Zakaria, Ammar, Mohd Nazir, Mohd Amril Nurman, Misiran, Masnita

    “…Both simulation on bivariate and multivariate cases show some promising results that the weighted PCA on LDA and the weighted PLS on LDA are better than the traditional LDA, kernel discriminant, and PCA+LDA methods. …”
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  3. 3
    Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors

    Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors by Ehteram M., Afan H.A., Dianatikhah M., Ahmed A.N., Fai C.M., Hossain M.S., Allawi M.F., Elshafie A.

    Published 2023
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  4. 4
    Optimized clustering with modified K-means algorithm

    Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

    Published 2021
    “…In dealing with correlated variables, PCA was embedded in the proposed algorithm. The developed algorithms were tested on uncorrelated and correlated simulated data sets, generated under various conditions. …”
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  5. 5
    The performance of k-means clustering method based on robust principal components

    The performance of k-means clustering method based on robust principal components by Kadom, Ahmed, Midi, Habshah, Rana, Sohel

    Published 2018
    “…To remedy this problem, we proposed to integrate robust principal component analysis (RPCA) with the k-means algorithm. Simulation study and real examples are carried out to compare the performance of the classical k-means, k-means based on PCA and k-means based on RPCA. …”
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  6. 6
    Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA

    Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA by Javed, E., Faye, I., Malik, A.S., Abdullah, J.M.

    Published 2017
    “…Methods We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. …”
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  7. 7
    Combining Recursive Least Square and Principal Component Analysis for Assisted History Matching

    Combining Recursive Least Square and Principal Component Analysis for Assisted History Matching by Md. Anuar, Nurul Syaza

    Published 2014
    “…Therefore, in this project, Principal Component Analysis (PCA) is used to reduce the number of parameters. …”
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    Final Year Project
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  8. 8
    Infomax and FASTICA using principle component analysis as preprocessor for airwave removal in seabed logging

    Infomax and FASTICA using principle component analysis as preprocessor for airwave removal in seabed logging by Ansari, A., Shafie, A.B., Ansari, S., Said, A.B.M., Nyamasvisva, E.T., Abdulkarim, M., Rauf, M.

    Published 2014
    “…Hence, the Infomax and FASTICA de-convolution algorithms are used, considering PCA as a pre-processor to filter out the airwaves which disrupt the subsurface signals within the receiver response. …”
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    Conference or Workshop Item
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  9. 9
    Classification for large number of variables with two imbalanced groups

    Classification for large number of variables with two imbalanced groups by Ahmad Hakiim, Jamaluddin

    Published 2020
    “…Both proposed algorithms outperform the conventional LDA on principal components (PCA-LDA) in classifying the minority group. …”
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  10. 10
    Minimum regularized covariance determinant and principal component analysis-based method for the identification of high leverage points in high dimensional sparse data

    Minimum regularized covariance determinant and principal component analysis-based method for the identification of high leverage points in high dimensional sparse data by Siti Zahariah, Midi, Habshah

    Published 2022
    “…A simulation study and two real data sets are used to illustrate the merit of our proposed method compared to the RMD-MRCD and Robust PCA (ROBPCA) methods. …”
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  11. 11
    Detection and Severity Identification of control valve stiction in industrial loops using integrated partially retrained CNN-PCA frameworks

    Detection and Severity Identification of control valve stiction in industrial loops using integrated partially retrained CNN-PCA frameworks by YAU, YONG SONG

    Published 2021
    “…In this thesis, an integrated framework using such partially retrained convolutional neural networks in conjunction with principal component analysis (CNN-PCA) is proposed for simultaneous control valve stiction detection and automatic identification of the severity of the problem. …”
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  12. 12
    Comparing three methods of handling multicollinearity using simulation approach

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

    Published 2006
    “…The analysis including all simulations and calculations were done using statistical package S-Plus 2000 software.…”
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  13. 13
    Fault detection and diagnosis using correlation coefficients

    Fault detection and diagnosis using correlation coefficients by Mak, Weng Yee

    Published 2005
    “…Normal Correlation (NC), Principal Component Analysis (PCA) and Partial Correlation Analysis (PCorrA) are used to develop the correlation coefficients between the selected key process variables with the quality variables of interest in the process from the NOC data. …”
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  14. 14
    Ocean Colour Remote Sensing Of Case 2 Waters Using An Optimised Neural Network

    Ocean Colour Remote Sensing Of Case 2 Waters Using An Optimised Neural Network by Anwar, Saumi Syahreza

    Published 2016
    “…The simulated Landsat TM and AVNIR-2 data were tested against in situ reflectance spectra measurements using ASD Spectroradiometer. …”
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  15. 15
    Multi-modal association learning using spike-timing dependent plasticity (STDP)

    Multi-modal association learning using spike-timing dependent plasticity (STDP) by Ibrahim, Mohammed Fadhil

    Published 2014
    “…Prior to a learning simulation, we extract the features of the biometrics used in the study. …”
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  16. 16
    On some methods of feature engineering useful for craniodental morphometrics of rats, shrews and kangaroos / Aneesha Pillay Balachandran Pillay

    On some methods of feature engineering useful for craniodental morphometrics of rats, shrews and kangaroos / Aneesha Pillay Balachandran Pillay by Aneesha Pillay , Balachandran Pillay

    Published 2024
    “…The results obtained from the simulation studies and application to real data showed that FDGM performed better than GM when PCA and LDA were employed. …”
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  17. 17
    Development of fault detection, diagnosis and control system identification using multivariate statistical process control (MSPC)

    Development of fault detection, diagnosis and control system identification using multivariate statistical process control (MSPC) by Ibrahim, Kamarul 'Asri, Ahmad, Arshad, Ali, Mohamad Wijayanuddin, Mak, Weng Yee

    Published 2006
    “…The developed FDD algorithm was implemented on a simulated distillation column which is a single equipment process. …”
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    Monograph
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  18. 18
    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 analysis including all simulations and calculations were done using statistical package S-Plus 2000 software. …”
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  19. 19
    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 analysis including all simulations and calculations were done using statistical package S-Plus 2000 software. …”
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  20. 20
    Computational analysis of biological data: Where are we?

    Computational analysis of biological data: Where are we? by Soreq, Lilach, Mohamed, Wael Mohamed Yousef

    Published 2024
    “…Computer modeling allows such electrical stimulations using statistics, bioinformatics and advanced machine-learning algorithms. …”
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