Search Results - (( java implementation graph algorithm ) OR ( parameter optimization isotherm algorithm ))

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  1. 1
    Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter

    Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter by Azhar, Nur Huwaina

    Published 2019
    “…In the benchmark paper, the result of implementation of LACE algorithm in 400, 600, 800 and 1000 servers were plotted at different graphs. …”
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  2. 2
    Priority and dynamic quantum time algorithms for central processing unit scheduling

    Priority and dynamic quantum time algorithms for central processing unit scheduling by Mohammed, Maysoon A.

    Published 2018
    “…The proposed algorithms (Priority Dynamic Quantum Time and Multi Priority Dynamic Quantum Time Algorithms) are implemented using JAVA programming language and validated using Key Performance Indicators equations. …”
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  3. 3
    Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer

    Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer by Temitope T., Dele-Afolabi, Masoud, Ahmadipour, Mohamed Ariff, Azmah Hanim, A.A., Oyekanmi, M.N.M., Ansari, Sikiru, Surajudeen, Kumar, Niraj

    Published 2024
    “…An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. …”
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  4. 4
    Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer

    Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer by Dele-Afolabi T.T., Ahmadipour M., Azmah Hanim M.A., Oyekanmi A.A., Ansari M.N.M., Sikiru S., Kumar N.

    Published 2025
    “…An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. …”
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  5. 5
    Effective adsorption of metolachlor herbicide by MIL-53(Al) metal-organic framework: Optimization, validation and molecular docking simulation studies

    Effective adsorption of metolachlor herbicide by MIL-53(Al) metal-organic framework: Optimization, validation and molecular docking simulation studies by Ahmad Isiyaka, H., Jumbri, K., Soraya Sambudi, N., Uba Zango, Z., Ain Fathihah Binti Abdullah, N., Saad, B.

    Published 2022
    “…The optimization by the RSM was significant with minimum number of experimental runs, lesser error and showed a simultaneous interaction of the adsorption parameters in predicting MET adsorption capacity. …”
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  6. 6
    Effective adsorption of metolachlor herbicide by MIL-53(Al) metal-organic framework: Optimization, validation and molecular docking simulation studies

    Effective adsorption of metolachlor herbicide by MIL-53(Al) metal-organic framework: Optimization, validation and molecular docking simulation studies by Ahmad Isiyaka, H., Jumbri, K., Soraya Sambudi, N., Uba Zango, Z., Ain Fathihah Binti Abdullah, N., Saad, B.

    Published 2022
    “…The optimization by the RSM was significant with minimum number of experimental runs, lesser error and showed a simultaneous interaction of the adsorption parameters in predicting MET adsorption capacity. …”
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  7. 7
    Performance of amidoxime-modified poly(acrylonitrile- Co-acrylic acid) for removal of boron in aqueous solution

    Performance of amidoxime-modified poly(acrylonitrile- Co-acrylic acid) for removal of boron in aqueous solution by Lau, Kia Li

    Published 2019
    “…The best fit model for adsorption isotherm was Sips model with heterogeneity factor (n) = 0.7611. …”
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  8. 8
    Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework

    Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework by Isiyaka, H.A., Jumbri, K., Sambudi, N.S., Zango, Z.U., Abdullah, N.A.F.B., Saad, B.

    Published 2022
    “…The adsorption kinetics and isotherm models show the presence of multilayer adsorption and chemisorption controlled adsorption process. …”
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  9. 9
    Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework

    Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework by Isiyaka, H.A., Jumbri, K., Sambudi, N.S., Zango, Z.U., Abdullah, N.A.F.B., Saad, B.

    Published 2022
    “…The adsorption kinetics and isotherm models show the presence of multilayer adsorption and chemisorption controlled adsorption process. …”
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  10. 10
    Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework

    Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework by Isiyaka, H.A., Jumbri, K., Sambudi, N.S., Zango, Z.U., Abdullah, N.A.F.B., Saad, B.

    Published 2022
    “…The adsorption kinetics and isotherm models show the presence of multilayer adsorption and chemisorption controlled adsorption process. …”
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  11. 11
    Modelling and simulation of hollow profile aluminium extruded product

    Modelling and simulation of hollow profile aluminium extruded product by Sulaiman, Shamsuddin, Baharudin, B. T. Hang Tuah, Mohd Ariffin, Mohd Khairol Anuar, Magid, Hani Mizhir

    Published 2015
    “…This process is an isothermal process with an extrusion ratio of 3.3. Subsequently, the optimized algorithm for these extrusion parameters was suggested based on the simulation results. …”
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  12. 12
    Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)

    Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN) by Khan T., Manan T.S.B., Isa M.H., Ghanim A.A.J., Beddu S., Jusoh H., Iqbal M.S., Ayele G.T., Jami M.S.

    Published 2023
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  13. 13
    Modeling and simulation of forward Al extrusion process using FEM

    Modeling and simulation of forward Al extrusion process using FEM by Magid, Hani Mizhir, Sulaiman, Shamsuddin, Mohd Ariffin, Mohd Khairol Anuar, Baharudin, B. T. Hang Tuah

    Published 2014
    “…Optimized algorithms for extrusion parameters were proposed regarding the concluded simulating results. …”
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  14. 14
    Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)

    Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN) by Khan, T., Manan, T.S.B., Isa, M.H., Ghanim, A.A.J., Beddu, S., Jusoh, H., Iqbal, M.S., Ayele, G.T., Jami, M.S.

    Published 2020
    “…The Fletcher-Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R2 of 0.989). …”
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  15. 15
    Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann)

    Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann) by Khan, Taimur, Abd Manan, Teh Sabariah, Hasnain Isa, Mohamed, A. J. Ghanim, Abdulnoor, Beddu, Salmia, Jusoh, Hisyam, Iqbal, Muhammad Shahid, Ayele, Gebiaw T, Jami, Mohammed Saedi

    Published 2020
    “…The Fletcher–Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R2 of 0.989). …”
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  16. 16
    Evolutionary cost-cognizant regression test case prioritization for object-oriented programs

    Evolutionary cost-cognizant regression test case prioritization for object-oriented programs by Bello, AbdulKarim

    Published 2019
    “…Therefore, this study proposed a cost-cognizant TCP approach for object-oriented software that uses path-based integration testing to identify the possible execution path extracted from the Java System Dependence Graph (JSDG) model of the source code using forward slicing technique. …”
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