Search Results - (( development pressure evolution algorithm ) OR ( java evolution optimization algorithm ))

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  1. 1

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…The aim of this evolution is to reflect the unseen time overhead incurred by optimal real-time algorithm, represented by LRE-TL, which might hinder the claimed optimality of such algorithms when they are practically implemented. …”
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  2. 2

    Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia by Zubaidi S.L., Kumar P., Al-Bugharbee H., Ahmed A.N., Ridha H.M., Mo K.H., El-Shafie A.

    Published 2024
    “…Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. …”
    Article
  3. 3

    Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff by Mohd Yusoff, Nurulanis

    Published 2018
    “…This thesis proposed an efficient algorithm, namely, the Quality of Service (QoS) and Energy Efficient Aware (QEEA). …”
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  4. 4

    Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff by Mohd Yusoff, Nurulanis

    Published 2017
    “…The results showed that the QEEA algorithm outperformed the other algorithms as it could achieve up to 18% of maximum throughput, 27% reduction in ECR, and 36% improvement in EE in terms of radius ranging from 200 m to 1000 m. …”
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    Thesis
  5. 5

    Nonlinear modeling and control of a spark ignition engine idle speed / Hazem Mohamed by Hazem, Mohamed

    Published 1998
    “…The fuzzy controller is formulated as a radial basis function network trained by the orthogqnalleast squares algorithm to estimate the controller parameters. The train~ng data of the network is the entries of an optimal control table derived by analyzing the evolution of the state trajectories of the system under different initial conditions using the cell to cell mapping technique. …”
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    Thesis
  6. 6

    Optimized adaptive neuro-fuzzy inference system using metaheuristic algorithms: Application of shield tunnelling ground surface settlement prediction by Liu, Xinni, Hussein, Sadaam Hadee, Kamarul Hawari, Ghazali, Tung, Tran Minh, Yaseen, Zaher Mundher

    Published 2021
    “…The predictive models were various nature-inspired frameworks, such as differential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimizer (ACO) to tune the adaptive neuro-fuzzy inference system (ANFIS). …”
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    Article
  7. 7

    Shear strength test on unsaturated soil using natural microscopic surface tension force / Basharudin Abdul Hadi by Abdul Hadi, Basharudin

    Published 2017
    “…There were two sets of algorithms in developing the UPLRB ANN model and every algorithm set consisted of model inputs data preparation, neural network 29 30 base of the specimen until the flow ceased..…”
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    Book Section
  8. 8

    Formulating the plastic deformation expression on coated substrate as a coating selection tool by Muniandy, Nagentrau

    Published 2017
    “…The FE model is validated by comparing with theoretical Hertzian contact solution for homogeneous substrate, meanwhile coated substrate is validated by comparing with reported literature. The evolutions of contact pressure, von mises stress, equivalent plastic strain, tangential stress and surface profile are examined for various coating parameters. …”
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    Thesis