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  1. 1
    Performance improvement through optimal location and sizing of distributed generation / Zuhaila Mat Yasin

    Performance improvement through optimal location and sizing of distributed generation / Zuhaila Mat Yasin by Mat Yasin, Zuhaila

    Published 2014
    “…On top of that, the multiobjective QIEP is also applied to determine the optimal undervoltage load shedding (UVLS) in various loading conditions according to load profile with and without DG. …”
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  2. 2
    Reactive power planning for maximum load margin improvement using Fast Artificial Immune Support Vector Machine (FAISVM)

    Reactive power planning for maximum load margin improvement using Fast Artificial Immune Support Vector Machine (FAISVM) by Aziz N.F.A., Abdul Rahman T.K., Zakaria Z.

    Published 2023
    “…This paper, first, presents a newly voltage stability index called Voltage Stability Condition Indicator (VSCI) to evaluate the voltage stability state of load buses in a system. It also proposes a fast optimization algorithm for reactive power planning problem (RPP) through Fast Artificial Immune Support Vector Machine (FAISVM). …”
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  3. 3
    Fault classification in smart distribution network using support vector machine

    Fault classification in smart distribution network using support vector machine by Chuan O.W., Ab Aziz N.F., Yasin Z.M., Salim N.A., Wahab N.A.

    Published 2023
    “…Right setting parameters are important to learning results and generalization ability of SVM. Gaussian radial basis function (RBF) kernel function has been used for training of SVM to accomplish the most optimized classifier. …”
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  4. 4
    Development of electromyography-controlled 3D printed robot hand and supervised machine learning for signal classification

    Development of electromyography-controlled 3D printed robot hand and supervised machine learning for signal classification by Abdul Wahit, Mohamad Aizat

    Published 2019
    “…After that, the simulation results were validated in the Load Test, and it shows that structure made from PLA material was able to withstand load with 1.7kg while the structure made from ABS material is only able to withstand load with 1.6kg. …”
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  5. 5
    An application of genetic algorithm and least squares support vector machine for tracing the transmission loss in deregulated power system

    An application of genetic algorithm and least squares support vector machine for tracing the transmission loss in deregulated power system by M. W., Mustafa, H., Shareef, M. H., Sulaiman, S. N., Abd. Khalid, S. R., Abd. Rahim, Omar, Aliman

    Published 2011
    “…The idea is to use GA as an optimizer to find the optimal values of hyper-parameters of LS-SVM and adopt a supervised learning approach to train the LS-SVM model. …”
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  6. 6
    An application of genetic algorithm and least squares support vector machine for tracing the transmission loss in deregulated power system

    An application of genetic algorithm and least squares support vector machine for tracing the transmission loss in deregulated power system by Mohd. Herwan, Sulaiman, Siti Rafidah, Abdul Rahim, Mohd Wazir, Mustafa, Hussain, Shareef, Dr., Saifulnizam, Abd. Khalid, Dr., Omar, Aliman

    Published 2011
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  7. 7
    Tracing the real power transfer of individual generators to loads using least squares support vector machine with continuous genetic algorithm

    Tracing the real power transfer of individual generators to loads using least squares support vector machine with continuous genetic algorithm by Mohd Wazir, Mustafa, Dr., Saifulnizam, Abd. Khalid, Dr., Mohd Herwan, Sulaiman, Siti Rafidah, Abd Rahim, Omar, Aliman, Hussain, Shareef, Dr.

    Published 2011
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  8. 8
    Transmission loss allocation in deregulated power system using the hybrid genetic algorithm-support vector machine technique

    Transmission loss allocation in deregulated power system using the hybrid genetic algorithm-support vector machine technique by M. W, Mustafa, Mohd Herwan, Sulaiman, H., Shareef, S. N., Abd. Khalid

    Published 2011
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  9. 9
    Tracing the real power transfer of individual generators to loads using least squares support vector machine with continuous genetic algorithm

    Tracing the real power transfer of individual generators to loads using least squares support vector machine with continuous genetic algorithm by Mohd Wazir, Mustafa, Saifulnizam, Abd.Khalid, Mohd Herwan, Sulaiman, Siti Rafidah, Abd Rahim, Omar, Aliman, Shareef, Hussain

    Published 2011
    “…This paper attempts to trace the real power transfer of individual generators to loads in pool based power system by incorporating the hybridization of Least Squares Support Vector Machine (LS-SVM) with Continuous Genetic Algorithm (CGA)- CGA-LSSVM. …”
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  10. 10
    Prediction of COVID-19 outbbreak using Support Vector Machine / Muhammad Qayyum Mohd Azman

    Prediction of COVID-19 outbbreak using Support Vector Machine / Muhammad Qayyum Mohd Azman by Mohd Azman, Muhammad Qayyum

    Published 2024
    “…A prototype architecture and a user-friendly graphical interface tailored for SVM-based outbreak predictions are developed, accompanied by detailed code snippets elucidating essential steps in data loading, encoding, scaling, and SVM model training. …”
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  11. 11
    Simulated Kalman Filter algorithms for solving optimization problems

    Simulated Kalman Filter algorithms for solving optimization problems by Nor Hidayati, Abdul Aziz

    Published 2019
    “…In this research, two novel estimation-based metaheuristic optimization algorithms, named as Simulated Kalman Filter (SKF), and single-solution Simulated Kalman Filter (ssSKF) algorithms are introduced for global optimization problems. …”
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  12. 12
    Optimization-based simulation algorithm for predictive-reactive job-shop scheduling of reconfigurable manufacturing systems

    Optimization-based simulation algorithm for predictive-reactive job-shop scheduling of reconfigurable manufacturing systems by Tan, Joe Yee

    Published 2022
    “…In this case, the effectiveness and reliability of RMS is increase by combining the simulation with the optimization algorithm.…”
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  13. 13
    A discrete simulated kalman filter optimizer for combinatorial optimization problems

    A discrete simulated kalman filter optimizer for combinatorial optimization problems by Suhazri Amrin, Rahmad

    Published 2022
    “…However, these extensions may result in increased execution times for the algorithm. In this research, a new combinatorial algorithm named discrete simulated Kalman filter optimizer (DSKFO) is proposed to solve combinatorial optimization problem. …”
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  14. 14
    Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm

    Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm by Zuwairie, Ibrahim, Nor Hidayati, Abd Aziz, Nor Azlina, Ab. Aziz, Saifudin, Razali, Mohd Saberi, Mohamad

    Published 2016
    “…In this paper, a new population-based metaheuristic optimization algorithm, named Simulated Kalman Filter (SKF) is introduced. …”
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  15. 15
    Ant colony optimization for rule induction with simulated annealing for terms selection

    Ant colony optimization for rule induction with simulated annealing for terms selection by Saian, Rizauddin, Ku-Mahamud, Ku Ruhana

    Published 2012
    “…This paper proposes a sequential covering based algorithm that uses an ant colony optimization algorithm to directly extract classification rules from the data set.The proposed algorithm uses a Simulated Annealing algorithm to optimize terms selection, while growing a rule.The proposed algorithm minimizes the problem of a low quality discovered rule by an ant in a colony, where the rule discovered by an ant is not the best quality rule, by optimizing the terms selection in rule construction. …”
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  16. 16
    Optimization of turning parameters using genetic algorithm method

    Optimization of turning parameters using genetic algorithm method by Shah Izwandi, Mohd Zawawi

    Published 2008
    “…The simulation based on Genetic Algorithm are successful develop and the optimum parameters values are obtained from the simulation.…”
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  17. 17
    The Hybrid of WOABAT-IFDO Optimization Algorithm and Its Application in Crowd Evacuation Simulation

    The Hybrid of WOABAT-IFDO Optimization Algorithm and Its Application in Crowd Evacuation Simulation by Hamizan, Sharbini, Roselina, Sallehuddin, Habibollah, Haron

    Published 2023
    “…This paper proposes a new hybrid of nature inspired optimization algorithm (IFDO-WOABAT) based on the latest optimization algorithm namely Improved Fitness Dependent Optimization (IFDO) with Whale-Bat Optimization algorithm (WOABAT). …”
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    Proceeding
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  18. 18
    Asynchronous simulated kalman filter optimization algorithm

    Asynchronous simulated kalman filter optimization algorithm by Zuwairie, Ibrahim, Nor Azlina, Ab. Aziz, Nor Hidayati, Abd Aziz, Tasiransurini, Ab Rahman

    Published 2018
    “…Simulated Kalman filter (SKF) is an optimization algorithm which is inspired by Kalman filtering method. …”
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  19. 19
    Simulated Kalman Filter optimization algorithm for maximization of wireless sensor networks coverage

    Simulated Kalman Filter optimization algorithm for maximization of wireless sensor networks coverage by Nor Azlina, Ab. Aziz, Zuwairie, Ibrahim, Kamarulzaman, Ab Aziz, Nor Hidayati, Abdul Aziz

    Published 2019
    “…Simulated Kalman Filter (SKF) is a population based optimization algorithm inspired by the Kalman filtering method. …”
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  20. 20
    Predictive-reactive job shop scheduling for flexible production systems with the combination of optimization and simulation based algorithm

    Predictive-reactive job shop scheduling for flexible production systems with the combination of optimization and simulation based algorithm by Abdul Rahman, Azrul Azwan, Joe Yee, Tan, A Rahman, Muhamad Arfauz, Salleh, Mohd Rizal, Bilge, Pinar

    Published 2020
    “…The predictive part determines the feasible schedule to be used for a flow shop which is generated using a combination of rule-based simulation and optimization: first, using the optimization algorithm to compute a rough plan, followed by using a rule based simulation system to locally fine tune the plan to obtain the final schedule. …”
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