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  1. 1
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
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  2. 2
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
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  3. 3
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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  4. 4
    Mining Sequential Patterns Using I-PrefixSpan

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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  5. 5
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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    Final Year Project / Dissertation / Thesis
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  6. 6
    Mining Sequential Patterns using I-PrefixSpan

    Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

    Published 2008
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    Citation Index Journal
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  7. 7
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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  8. 8
    Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud

    Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

    Published 2023
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  9. 9
    Crossover and mutation operators of genetic algorithms

    Crossover and mutation operators of genetic algorithms by Lim, Siew Mooi, Md. Sultan, Abu Bakar, Sulaiman, Md. Nasir, Mustapha, Aida, Leong, Kuan Yew

    Published 2017
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  10. 10
    Crossover and mutation operators of genetic algorithms

    Crossover and mutation operators of genetic algorithms by Siew, Mooi Lim, Md. Sultan, Abu Bakar, Sulaiman, Md. Nasir, Mustapha, Aida, Leong, K. Y.

    Published 2017
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  11. 11
    Development of an explainable machine learning model for predicting depression in adults with type 2 diabetes mellitus: a cross-sectional SHAP-based analysis of NHANES 2009-2023

    Development of an explainable machine learning model for predicting depression in adults with type 2 diabetes mellitus: a cross-sectional SHAP-based analysis of NHANES 2009-2023 by Tang, Yan, Jia, Lei, Zhou, Junjun, Dou, Jin, Qian, Jingjuan, Yi, Xin, Soh, Kim Lam

    Published 2026
    “…Five machine learning algorithms - random forest, extreme gradient boosting (XGBoost), multilayer perceptron, logistic regression, and support vector machine - were trained and evaluated using 5-fold cross-validation. …”
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  12. 12
    Advance speech recognition / Mohammad Amarulhariz Mohd Fairuz … [et al.]

    Advance speech recognition / Mohammad Amarulhariz Mohd Fairuz … [et al.] by Mohd Fairuz, Mohammad Amarulhariz, Mohd Nazri, Muhammad Aiman, Saharudin, Muhammad Haiqal, Hashim, Abdu Syafi Adil, Mahadhir, Khair Izz Khan, Saifol Bakhri, Muhammad Syazwi Hilmi, Shaidin, Sumayyah

    Published 2024
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  13. 13
    Cleansing of inconsistent sample in linear regression model based on rough sets theory

    Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Efendi, Riswan, Mohd. Nawi, Nazri, Mat Derisf, Mustafa, Aqil Burney, S.M.

    Published 2023
    “…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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  14. 14
    Cleansing of inconsistent sample in linear regression model based on rough sets theory

    Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Efendi, Riswan, Mohd. Nawi, Nazri, Mat Deris, Mustafa, Burney, S.M.Aqil

    Published 2023
    “…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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  15. 15
    Cleansing of inconsistent sample in linear regression model based on rough sets theory

    Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Efendi, Riswan, Mohd. Nawi, Nazri, Mat Deris, Mustafa, Aqil Burney, S.M.

    Published 2023
    “…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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  16. 16
    Cleansing of inconsistent sample in linear regression model based on rough sets theory

    Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Riswan Efendi, Riswan Efendi, Mohd. Nawi, Nazri, Mat Deris, Mustafa, Aqil Burney, S.M.

    Published 2023
    “…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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  17. 17
    Cleansing of inconsistent sample in linear regression model based on rough sets theory

    Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Riswan Efendi, Riswan Efendi, Nazri Mohd. Nawi, Nazri Mohd. Nawi, Mustafa Mat Deris, Mustafa Mat Deris, S.M.Aqil Burney, S.M.Aqil Burney

    Published 2023
    “…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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  18. 18
    Cleansing of inconsistent sample in linear regression model based on rough sets theory

    Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Efendi, Riswan, Mohd. Nawi, Nazri, Mat Deris, Mustafa, S.M.Aqil Burney, S.M.Aqil Burney

    Published 2023
    “…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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  19. 19
    Cleansing of inconsistent sample in linear regression model based on rough sets theory

    Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Efendi, Riswan, Mohd. Nawi, Nazri, Mat Deris, Mustafa, S.M.Aqil Burney, S.M.Aqil Burney

    Published 2023
    “…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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  20. 20
    Cleansing of inconsistent sample in linear regression model based on rough sets theory

    Cleansing of inconsistent sample in linear regression model based on rough sets theory by Rasyidah, Rasyidah, Efend, Riswan, Mohd. Nawi, Nazri, Mat Derisf, Mustafa, S.M.Aqil Burney, S.M.Aqil Burney

    Published 2023
    “…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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