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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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    Thesis
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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
    A review of phishing email detection approaches with deep learning algorithm implementation

    A review of phishing email detection approaches with deep learning algorithm implementation by Nursyafiqah Hazira Mohamad Nazir, Nordaliela Mohd Rusli, Tan, Soo Fun, Chin, K. O.

    Published 2020
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  9. 9
    Machine learning using robust AI techniques / Prof. Madya Dr. Nordin Abu Bakar

    Machine learning using robust AI techniques / Prof. Madya Dr. Nordin Abu Bakar by Abu Bakar, Nordin (Prof. Madya Dr.)

    Published 2012
    “…Many algorithms have been applied to aid the assessment process. …”
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    Research Reports
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  10. 10
    Coalition of genetic algorithms and artificial neural network for isolated spoken Malay speech recognition / Noraini Seman

    Coalition of genetic algorithms and artificial neural network for isolated spoken Malay speech recognition / Noraini Seman by Seman, Noraini

    Published 2012
    “…However, due to the stochastic nature of this algorithm, the learning process can reach an optimal solution with much higher probability than many standard neural network techniques.…”
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    Book Section
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  11. 11
    Voltage stability index prediction by using genetics algorithm-based machine learning (GBML) technique / Zainab Mohd Ghazali

    Voltage stability index prediction by using genetics algorithm-based machine learning (GBML) technique / Zainab Mohd Ghazali by Mohd Ghazali, Zainab

    Published 2007
    “…The proposed technique is using Genetic Algorithms-Based Machine Learning (GBML) to predict the voltage stability index. …”
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  12. 12
    Differential evolution for neural networks learning enhancement

    Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

    Published 2008
    “…Evolutionary computation is the name given to a collection of algorithms based on the evolution of a population toward a solution of a certain problem. …”
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  13. 13
    Extreme learning machine for user location prediction in mobile environment

    Extreme learning machine for user location prediction in mobile environment by Mantoro, Teddy, Olowolayemo, Akeem, Olatunji, Sunday O., Ayu, Media Anugerah, Md. Tap, Abu Osman

    Published 2011
    “…Purpose – Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single-hidden-layer neural networks. …”
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  14. 14
    Extreme learning machine for user location prediction in mobile environment

    Extreme learning machine for user location prediction in mobile environment by Mantoro, Teddy, Olowolayemo, Akeem, Olatunji, Sunday O., Ayu, Media A., Abu Osman, Md. Tap

    Published 2011
    “…Purpose – Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single-hidden-layer neural networks. …”
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  15. 15
    Interpretation of machine learning model using medical record visual analytics

    Interpretation of machine learning model using medical record visual analytics by Mohd Khalid, Nur Hidayah, Ismail, Amelia Ritahani, Abdul Aziz, Normaziah

    Published 2021
    “…The state of the art of medical application that being implemented are mostly based on common machine learning model. …”
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    Proceeding Paper
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  16. 16
    Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection

    Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection by Olufemi, Osaji Emmanuel, Othman, Mohammad Lutfi, Hizam, Hashim, Othman, Muhammad Murtadha, Ammar, Aker Elhadi Emhemed Alhaaj, Okeke, Chidiebere Akachukwu, Onuabuchi, Nwagbara Samuel

    Published 2019
    “…The supervised machine learning algorithm from Bayesian network classified 99.15 % faults correctly with the operation time of 0.01 s to produced best-generalized model with an RMS error value of 0.05 for single line-to-ground (SLG) fault identification and classification. …”
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  17. 17
    Interpretation of machine learning model using medical record visual analytics

    Interpretation of machine learning model using medical record visual analytics by Mohd Khalid, Nur Hidayah, Ismail, Amelia Ritahani, Abdul Aziz, Normaziah

    Published 2022
    “…The state of the art of medical application that being implemented are mostly based on common machine learning model. …”
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    Proceeding Paper
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  18. 18
    RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION

    RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION by CATUR ANDRYANI, NUR AFNY

    Published 2010
    “…The proposed training algorithms discussed in this thesis are derived for fixed size RBF network and being compared with Extreme Learning Machine (ELM) as the ELM technique just randomly assigned centers and width of the hidden neurons and update the output connected weights. …”
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  19. 19
    Exploration of machine learning forecasting methods in M4 competition / Muhammad Halim Hamdan and Shuzlina Abdul-Rahman

    Exploration of machine learning forecasting methods in M4 competition / Muhammad Halim Hamdan and Shuzlina Abdul-Rahman by Hamdan, Muhammad Halim, Abdul-Rahman, Shuzlina

    Published 2021
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  20. 20
    Study on crack detection using image processing techniques and deep learning – a survey

    Study on crack detection using image processing techniques and deep learning – a survey by Saleem, Muhammad Asif, Senan, Norhalina, Ahmad, Rehan

    Published 2020
    “…So it very well may be done consequently by utilizing image processing. Deep learning algorithms have been used for the solution of multiple issues in the area of image classification. …”
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