Search Results - (( using (vectorisation OR vectorization) _ algorithm ) OR ( using function method algorithm ))

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  1. 1

    3D silhouette rendering algorithms using vectorisation technique from Kedah topography map by Che Mat, Ruzinoor, Nordin, Norani

    Published 2004
    “…The vectorisation software has been used for producing these data. …”
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    Conference or Workshop Item
  2. 2

    Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models by Mok, Ren Hao, Mohd Ashraf, Ahmad

    Published 2024
    “…This article proposes an identification method of continuous-time fractional-order Hammerstein model using smoothed functional algorithm with a norm-limited update vector (NL-SFA). …”
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    Article
  3. 3

    Landslide risk zoning using support vector machine algorithm by Ghiasi V., Pauzi N.I.M., Karimi S., Yousefi M.

    Published 2024
    “…The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. …”
    Article
  4. 4

    Support directional shifting vector: A direction based machine learning classifier by Kowsher, Md., Hossen, Imran, Tahabilder, Anik, Prottasha, Nusrat Jahan, Habib, Kaiser, Zafril Rizal, M Azmi

    Published 2021
    “…The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. …”
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    Article
  5. 5

    Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Pr... by Sammen S.S., Ehteram M., Sheikh Khozani Z., Sidek L.M.

    Published 2024
    “…For water level prediction, lagged rainfall and water level are used. In this study, we used extreme learning machine (ELM)-multi-kernel least square support vector machine (ELM-MKLSSVM), extreme learning machine (ELM)-LSSVM-polynomial kernel function (PKF) (ELM-LSSVM-PKF), ELM-LSSVM-radial basis kernel function (RBF) (ELM-LSSVM-RBF), ELM-LSSVM-Linear Kernel function (LKF), ELM, and MKLSSVM models to predict water level. …”
    Article
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    Hand gesture recognition for autism diagnosis using Support Vector Machine (SVM) Algorithm / Muhammad Asyraf Mohamad Zain by Mohamad Zain, Muhammad Asyraf

    Published 2020
    “…To counter this problem, a system has been proposed to detect the hand gesture using one of the machine learning technique which is Support Vector Machine (SVM) Algorithm. …”
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    Thesis
  9. 9

    Utilizing artificial bee colony algorithm as feature selection method in Arabic text classification by Hijazi, Musab, Zeki, Akram M., Ismail, Amelia Ritahani

    Published 2023
    “…Support Vector Machine and Naïve Bayes classifiers were used as a fitness function for the ABC algorithm. …”
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    Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications by Sabry, Ahmad H., Wan Hasan, Wan Zuha, Ab Kadir, M. Zainal A., Mohd Radzi, Mohd Amran, Shafie, Suhaidi

    Published 2018
    “…The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.…”
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    Sliding Mode Speed Control for Induction Motor Drives with State-Dependent Gain Method by Sulaiman, Marizan, Ibrahim, Zulkifilie, Patakor, Fizatul Aini

    Published 2013
    “…The induction motor is controlled by vector control strategy, using indirect field orientation and Space Vector Pulse Width Modulation technique. …”
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    Article
  19. 19

    An improved algorithm for iris classification by using support vector machine and binary random machine learning by Kamarulzalis, Ahmad Haadzal

    Published 2018
    “…This study focuses on supervised learning that seek to classify all the Iris dataset respect to three species (setosa, versicolor and virginica) in order them to mimic the actual dataset by using Support Vector Machine with four different kernel function (Linear, Radial Basis, Sigmoid and Polynomial), Random Forest (RF), k-Nearest Neighbors(k-NN) and Random Nearest Neighbors (RNN) as a method. …”
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    Thesis
  20. 20

    A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island by Mohd Shafri, Helmi Zulhaidi, Ramle, F. S. H.

    Published 2009
    “…The study indicates that the classification accuracy of SVM algorithm was better than DT algorithm. The overall accuracy of the SVM using four kernel types was above 73% and the overall accuracy of the DT method was 69%. …”
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    Article