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  1. 1
    Performance evaluation of real-time multiprocessor scheduling algorithms

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

    Published 2016
    “…The CPU profiler of JavaTM VisualVM measures the number of invocations of scheduling event handlers (procedures) in each algorithm as well as the total time spent in all invocations of this handler. …”
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  2. 2
    Hexagon pattern particle swarm optimization based block matching algorithm for motion estimation / Siti Eshah Che Osman

    Hexagon pattern particle swarm optimization based block matching algorithm for motion estimation / Siti Eshah Che Osman by Che Osman, Siti Eshah

    Published 2019
    “…In this study, a pattern based using Particle Swarm Optimization (PSO) is proposed named as Hexagon PSO (HPSO). …”
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    Thesis
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  3. 3
    A Toolkit for Simulation of Desktop Grid Environment

    A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

    Published 2014
    “…In this type of environment it is nearly impossible to prove the effectiveness of a scheduling algorithm. Hence the main objective of this study is to develop a desktop grid simulator toolkit for measuring and modeling scheduler algorithm performance. …”
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    Final Year Project
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  4. 4
    Runtime reduction in optimal multi-query sampling-based motion planning

    Runtime reduction in optimal multi-query sampling-based motion planning by Khaksar W., Sahari K.S.B.M., Ismail F.B., Yousefi M., Ali M.A.

    Published 2023
    “…Algorithms; Dispersions; Manufacture; Query processing; Robotics; High-dimensional; Low dispersions; Optimal solutions; Path length; Planning tasks; Sampling-based; Sampling-based algorithms; Sampling-based motion planning; Motion planning…”
    Conference Paper
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  5. 5
    MARKERLESS ARTICULATED HUMAN MOTION TRACKING USING HIERARCHICAL MULTI-SWARM COOPERATIVE PARTICLE SWARM OPTIMIZATION

    MARKERLESS ARTICULATED HUMAN MOTION TRACKING USING HIERARCHICAL MULTI-SWARM COOPERATIVE PARTICLE SWARM OPTIMIZATION by SAINI, SANJA Y

    Published 2016
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  6. 6
    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT

    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

    Published 2006
    “…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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  7. 7
    Hierarchical approach for articulated 3D human motion tracking using PF-based PSO

    Hierarchical approach for articulated 3D human motion tracking using PF-based PSO by Saini, S., Rambli, D.R.B.A., Sulaiman, S.B., Zakaria, M.N.B.

    Published 2014
    “…In vision-based human motion tracking, two algorithms most extensively have been used, namely, PF and PSO. …”
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  8. 8
    Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence

    Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence by S. S. S. , Ranjit

    Published 2011
    “…There are several block-matching algorithm based on block-based motion estimation techniques have been developed. …”
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    Book Chapter
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  9. 9
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Shamsudin, Shamsudin, Mohamaddan, Shahrol

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  10. 10
    Application of sampling-based motion planning algorithms in autonomous vehicle navigation

    Application of sampling-based motion planning algorithms in autonomous vehicle navigation by Khaksar, Weria, Mohamed Sahari, Khairul Salleh, Tang, Sai Hong

    Published 2016
    “…In this chapter, a novel sampling-based navigation architecture is introduced, which employs the optimal properties of RRT* planner and the low running time property of low-dispersion sampling-based algorithms. …”
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  11. 11
    Improving Class Timetabling using Genetic Algorithm

    Improving Class Timetabling using Genetic Algorithm by Qutishat, Ahmed Mohammed Ali

    Published 2006
    “…This paper reports the power fill techniques using GA in scheduling. Class timetabling problem is one of the applications in scheduling. …”
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  12. 12
    The impact of executive function and aerobic exercise recognition in obese children under deep learning

    The impact of executive function and aerobic exercise recognition in obese children under deep learning by JING, XIN, ABDULLAH, BORHANNUDIN, ABU SAAD, HAZIZI, YANG, XIANGKUN

    Published 2025
    “…Initially, a motion recognition model based on STN and Lucas–Kanade optical flow algorithm optimization was constructed. …”
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  13. 13
    Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions

    Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions by Ibrahimy, Muhammad Ibn, Ahsan, Md. Rezwanul, Khalifa, Othman Omran

    Published 2013
    “…The outcomes of the research show that the optimal design of Levenberg-Marquardt based neural network classifier can perform well with an average classification success rate of 88.4%. …”
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  14. 14
    Examination timetabling using genetic algorithm case study: KUiTTHO

    Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  15. 15
    Multilevel optimization for dense motion estimation

    Multilevel optimization for dense motion estimation by Saaban, Azizan, Kalmoun, El Mostafa, Ibrahim, Haslinda, Ramli, Razamin, Omar, Zurni

    Published 2011
    “…We evaluated the performance of different optimization techniques developed in the context of optical flow computation with different variational models.In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we developed the use of efficient multilevel schemes for computing the optical flow.More precisely, we evaluated the performance of a standard unidirectional multilevel algorithm - called multiresolution optimization (MR/Opt), to a bidrectional multilevel algorithm - called full multigrid optimization (FMG/Opt).The FMG/Opt algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. …”
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    Monograph
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  16. 16
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Shahrol Mohamaddan, Shahrol Mohamaddan

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  17. 17
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  18. 18
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  19. 19
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2022
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  20. 20
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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