Search Results - (( federation classifications learning algorithm ) OR ( java adaptation optimization algorithm ))

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  1. 1
    Parallel distributed genetic algorithm development based on microcontrollers framework

    Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

    Published 2023
    Conference paper
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  2. 2
    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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  3. 3
    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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  4. 4
    Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions

    Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

    Published 2017
    “…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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  5. 5
    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
    “…This research aims to identify the most important features for flight delay prediction, build supervised machine learning algorithms (i.e., logistic regression (LR), random forest (RF) and artificial neural network (ANN)) for predicting flight arrival delay and compare the performances of the methods. …”
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  6. 6
    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
    “…© 2010 International Federation for Medical and Biological Engineering.…”
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