Search Results - (( parallel estimation learning algorithm ) OR ( variable detection queue algorithm ))

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  1. 1

    Adopting genetic algorithm to enhance state-sensitivity partitioning by Sultan, Ammar Mohammed, Baharom, Salmi, Abd Ghani, Abdul Azim, Din, Jamilah, Zulzalil, Hazura

    Published 2015
    “…Therefore, there is a need to optimize those test cases generated by the SSP in enhancing its effectiveness in detecting faults. Genetic algorithm (GA) has been identified as the most common potential technique among several optimization techniques. …”
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    Conference or Workshop Item
  2. 2

    Adopting genetic algorithm to enhance state-sensitivity partitioning by Mohammed Sultan, Ammar, Baharom, Salmi, Abd Ghani, Abdul Azim, Din, Jamilah, Zulzalil, Hazura

    Published 2015
    “…Therefore, there is a need to optimize those test cases generated by the SSP in enhancing its effectiveness in detecting faults. Genetic algorithm (GA) has been identified as the most common potential technique among several optimization techniques.Thus, GA is investigated for the integrating with the existing SSP. …”
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    Conference or Workshop Item
  3. 3

    Super resolution imaging using modified lanr based on separable filtering by Somadina, Ike Chidiebere

    Published 2019
    “…The underlying idea is to process and reconstruct information in low and high frequency sub-bands based on separable property of neighbourhood filtering to achieve fast parallel and vectorized operation, while enhancing algorithmic performance by reducing computational burden resulting from computing the weighted function of every pixel for each pixel in an image. …”
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    Thesis
  4. 4

    Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model by Megat Syahirul Amin, Megat Ali, Azlee, Zabidi, Nooritawati, Md Tahir, Ihsan, Mohd Yassin, Eskandari, Farzad, Azlinda, Saadon, Mohd Nasir, Taib, Abdul Rahim, Ridzuan

    Published 2024
    “…System Identification (SI), a methodology utilized in domains like engineering and mathematical modeling to construct or refine dynamic system models from captured data, relies significantly on the Nonlinear Auto-Regressive (NAR) model due to its reliability and capability of integrating nonlinear functions, complemented by contemporary machine learning strategies and computational algorithms to approximate complex system dynamics to address these limitations. …”
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    Article
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