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  1. 1
    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
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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    Thesis
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  2. 2
    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde

    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

    Published 2022
    “…The support vector machines (SVM) algorithm obtained the overall best results of 94.5% accuracy, 91.8% precision, 91.7% recall, and 91.1% f-Measure while the naïve bayes (NB) algorithm obtained the best AUC score of 0.944 with the tweet data of Dato Seri Anwar. …”
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  3. 3
    Development Of Machine Learning User Interface For Pump Diagnostics

    Development Of Machine Learning User Interface For Pump Diagnostics by Lee, Zhao Yang

    Published 2022
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    Monograph
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  4. 4
    Identification and Grading of Manage Using Image Processing

    Identification and Grading of Manage Using Image Processing by Shukor, Syazwan

    Published 2021
    “…The criteria of mangos to be assessed by the grading algorithm are color and weight. Classification of these mangos are conducted based on standards set by Federal Agriculture Marketing Authority (FAMA) mango ripeness index, Project activities have started using a proposed activity flow for algorithm development using Python and the experimental chamber setup for actual mange data collection. …”
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    Final Year Project
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  5. 5
    Embedded Fuzzy Classifier for Detection and Classification of Preseizure State Using Real EEG Data

    Embedded Fuzzy Classifier for Detection and Classification of Preseizure State Using Real EEG Data by Qidwai, Uvais, Malik, Aamir Saeed, Shakir, Mohamed

    Published 2013
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    Book Section
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  6. 6
    Unsupervised chest X-ray opacity classification using minimal deep features

    Unsupervised chest X-ray opacity classification using minimal deep features by Che Azemin, Mohd Zulfaezal, Mohd Tamrin, Mohd Izzuddin, Md. Ali, Mohd. Adli, Jamaludin, Iqbal

    Published 2022
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  7. 7
    Detection on ambiguous software requirements specification written in malay using machine learning

    Detection on ambiguous software requirements specification written in malay using machine learning by Zahrin, Mohd Firdaus

    Published 2017
    “…Four (4) algorithms have been evaluated to find the suitable classification algorithm for this purpose. …”
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  8. 8
    Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system

    Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system by Seyed Mohammad Hussein, Ahmad, Siti Anom, Hassan, Mohd Khair, Ishak, Asnor Juraiza

    Published 2013
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  9. 9
    Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network

    Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network by Ibrahim, F., Faisal, T., Mohamad Salim, M.I., Taib, M.N.

    Published 2010
    “…The ANN was trained via the steepest descent back propagation with momentum algorithm using the log-sigmoid transfer function while the sum-squared error was used as the network's performance indicator. …”
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  10. 10
    A comparative study on aviation arrival delay prediction using machine learning methods

    A comparative study on aviation arrival delay prediction using machine learning methods by Chew, Pui Ting

    Published 2023
    “…Hence, the best performing method in this research can be used to predict flight arrival delays to reduce the above-mentioned flight delay costs and aid the classification of high or low risks of the insured to be used as input for future work to calculate insurance premium based on the sum of mean and standard deviation of losses.…”
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