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  1. 1
    Improved clustering using robust and classical principal component

    Improved clustering using robust and classical principal component by Hassn, Ahmed Kadom

    Published 2017
    “…To remedy this problem, we propose to integrate Principal Component analysis (PCA) which is useful for dimensionality reduction of a dataset with the k-means clustering algorithm. …”
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  2. 2
    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
    “…This paper focuses on improving the nu-SVR algorithm to handle the problem of outliers. A new hybrid PCA with the nu-SVR technique (PCA-SVR) has been established. …”
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  3. 3
    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
    “…Several approaches have been devoted to study such problems using linear and non-linear classification rules, but limited to group imbalance rather than the combination of both problems. …”
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  4. 4
    An Integrated Principal Component Analysis And Weighted Apriori-T Algorithm For Imbalanced Data Root Cause Analysis

    An Integrated Principal Component Analysis And Weighted Apriori-T Algorithm For Imbalanced Data Root Cause Analysis by Ong, Phaik Ling

    Published 2016
    “…However, frequent pattern mining (FPM) using Apriori-like algorithms and support-confidence framework suffers from the myth of rare item problem in nature. …”
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  5. 5
    Evaluating integrated weight linear method to class imbalanced learning in video data

    Evaluating integrated weight linear method to class imbalanced learning in video data by Mohd Apandi, Ziti Fariha, Mustapha, Norwati, Affendey, Lilly Suriani

    Published 2011
    “…In the second phase, the reduces instances are refined using the weight linear algorithm. The experiment results using 9 soccer video demonstrate that the integration of PCA and WL is capable to alleviates the imbalanced problem and able to improve classification performance in video data.…”
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  6. 6
    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. …”
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  7. 7
    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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  8. 8
    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
    “…This project will study the applicability of the combined algorithm for history matching problem. The study conducted on PCA and RLS method shows high chances of success in applying these methods for history matching problem. …”
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    Final Year Project
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  9. 9
    Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models

    Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models by Quadros, Jaimon Dennis, Khan, Sher Afghan, Aabid, Abdul, Baig, Muneer

    Published 2023
    “…Based on the identical dataset, the GA-BP and PSO-BP algorithms are also compared to the PCA-BAS-ENN algorithm. …”
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  10. 10
    Improved robust principal component analysis based on minimum regularized covariance determinant for the detection of high leverage points in high dimensional data

    Improved robust principal component analysis based on minimum regularized covariance determinant for the detection of high leverage points in high dimensional data by Habshah Midi, Jaaz Suhaiza, Mohd Aslam, Hani Syahida, Emi Amielda

    Published 2025
    “…However, the MRCD-PCA algorithm is quite cumbersome and required longer computational running time. …”
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  11. 11
    Towards Practical Face Recognition System Employing Row-Based Distance Method In 2dpca Based Algorithms

    Towards Practical Face Recognition System Employing Row-Based Distance Method In 2dpca Based Algorithms by Al-Arashi, Waled Hussein Mohammed

    Published 2014
    “…One of the most important extensions of PCA is Two-dimensional PCA (2DPCA). However, 2DPCA-based features are matrices rather than vectors as in PCA. …”
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  12. 12
    Pattern Recognition for Human Diseases Classification in Spectral Analysis

    Pattern Recognition for Human Diseases Classification in Spectral Analysis by Nur Hasshima Hasbi, Abdullah Bade, Fuei, Pien Chee, Muhammad Izzuddin Rumaling

    Published 2022
    “…On the other hand, classification methods are techniques or algorithms used to group samples into a predetermined category. …”
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  13. 13
    Optimized clustering with modified K-means algorithm

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

    Published 2021
    “…Clustering technique is able to find hidden patterns and to extract useful information from huge data. Among the techniques, the k-means algorithm is the most commonly used technique for determining optimal number of clusters (k). …”
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  14. 14
    Improved robust principal component analysis based on minimum regularized covariance determinant for the detection of high leverage points in high dimensional data (penambahbaikan analisis komponen utama berdasarkan penentu kovarian teratur minimum bagi mengecam titik tuasan tinggi untuk data dimensi tinggi)

    Improved robust principal component analysis based on minimum regularized covariance determinant for the detection of high leverage points in high dimensional data (penambahbaikan... by Midi, Habshah, Suhaiza, Jaaz, Mohd Aslam, ., Hani Syahida, ., Emi Amielda, .

    Published 2025
    “…However, the MRCD-PCA algorithm is quite cumbersome and required longer computational running time. …”
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  15. 15
    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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  16. 16
    Comparing three methods of handling multicollinearity using simulation approach

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

    Published 2006
    “…PCR is a combination of principal component analysis (PCA) and ordinary least squares regression (OLS) while PLSR is an approach similar to PCR because a component that can be used to reduce the number of variables need to be constructed. …”
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  17. 17
    Feature extraction of power disturbance signal using time frequency analysis

    Feature extraction of power disturbance signal using time frequency analysis by Sihab, Norsabrina

    Published 2006
    “…Those signals are transformed into time frequency plane using Bdistribution algorithm. Then the important feature vectors or components are extracted using Singular Value Decomposition (SVD) and Principle Component Analysis (PCA). …”
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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
    “…PCR is a combination of principal component analysis (PCA) and ordinary least squares regression (OLS) while PLSR is an approach similar to PCR because a component that can be used to reduce the number of variables need to be constructed. …”
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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
    “…PCR is a combination of principal component analysis (PCA) and ordinary least squares regression (OLS) while PLSR is an approach similar to PCR because a component that can be used to reduce the number of variables need to be constructed. …”
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  20. 20
    Optimized intrusion detection mechanism using soft computing techniques

    Optimized intrusion detection mechanism using soft computing techniques by Ahmad, iftikhar, Azween, Abdullah, Alghamdi, Abdullah, Hussain, Muhammad

    Published 2011
    “…The selecting of an appropriate number of principal components is a critical problem. So, Genetic Algorithm (GA) is used in the optimum selection of principal components instead of using traditional method. …”
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