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  1. 1
    Maximum 2-satisfiability in radial basis function neural network

    Maximum 2-satisfiability in radial basis function neural network by Shehab Abdulhabib Alzaeemi, Saratha Sathasivam, Mohd Shareduwan Mohd Kasihmuddin, Mohd. Asyraf Mansor

    Published 2020
    “…In this study, the effectiveness of RBFNN-MAX2SAT can be estimated by evaluating the proposed models with testing data sets. …”
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  2. 2
    Improved Location And Positioning Utilizing Single MIMO Base Station In IMT-Advanced System

    Improved Location And Positioning Utilizing Single MIMO Base Station In IMT-Advanced System by Azmi, Awang Md Isa, Mustafa H., Othman, Zahariah, Manap, Mohd Sa'ari, Mohamad Isa, Mohd Shahril Izuan, Mohd Zin, Mohd Shakir, Md Saat, Zahriladha, Zakaria

    Published 2016
    “…The simulation results showed that the proposed SMVirBS technique always outperforms the linear least square (LLS) algorithm in terms of estimated location accuracy. …”
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  3. 3
    Enhancement Of Wifi Indoor Positioning System

    Enhancement Of Wifi Indoor Positioning System by Aboodi, Ahed Hussein

    Published 2011
    “…The algorithm calculates the distances from the RSSs col- lected around the area, and checks for an error occurrence after the location estimation is calcu- lated with Least Square Algorithm (LSA). …”
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  4. 4
    A study of frame timing synchronization for WiMAX applications

    A study of frame timing synchronization for WiMAX applications by V., Jeoti, K.L., Ang, K.M., Yew

    Published 2007
    “…The accuracy and performance of both methods are quantified in terms of mean squared error between the exact and the estimated start of a frame. …”
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  5. 5
    Dynamic transmit antenna shuffling scheme for hybrid multiple-input multiple-output in layered architecture

    Dynamic transmit antenna shuffling scheme for hybrid multiple-input multiple-output in layered architecture by Chong, Jin Hui

    Published 2010
    “…The computational complexity (total number of arithmetic operations) of proposed LC-QR algorithm is significantly lower than the conventional QR decomposition, zero-forcing (ZF) and minimum mean square error (MMSE) detection algorithm. …”
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  6. 6
    Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach

    Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach by Marpaung, Faridawaty, Ramadhani, Fanny, Dinata, Dewan

    Published 2024
    “…The best model was created using the grid search cross-validation, while the best prediction results were created using the RF algorithm, with the following parameters: n-estimator = 50, max depth = 10, min samples split = 2, and min samples leaf = 1. …”
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