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  1. 1
    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof

    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof by Yusof, Yusman

    Published 2019
    “…From the simulation result, by using these sensors information the AUTOWiSARD algorithm can successfully differentiate and classify states without supervision, while the Q-learning algorithm is able to produce and optimized states-actions policy. …”
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  2. 2
    A new ant based rule extraction algorithm for web classification

    A new ant based rule extraction algorithm for web classification by Ku-Mahamud, Ku Ruhana, Saian, Rizauddin

    Published 2011
    “…Methods to reduce the number of attributes and discretization are two important data pre-processing steps before the data can be used for classification activity. …”
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  3. 3
    Simulation of an adaptive artificial neural network for power system security enhancement including control action

    Simulation of an adaptive artificial neural network for power system security enhancement including control action by Al-Masri, Ahmed Naufal A., Ab Kadir, Mohd Zainal Abidin, Hizam, Hashim, Mariun, Norman

    Published 2015
    “…This paper presents a new method for enhancing power system security, including a remedial action, using an artificial neural network (ANN) technique. …”
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  4. 4
    Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller

    Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller by Ismail, H. Muh Yusuf

    Published 2010
    “…This study investigates the use of Genetic Algorithms (GA) to design and implement of Fuzzy Logic Controllers (FLC). …”
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  5. 5
    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Adil Soomro, Zubair, Adrianshah, Andi

    Published 2023
    “…Through this study, Q-Learning and State-Action-Reward-StateAction (SARSA) are used in this study and the comparison of results involving both the performance and effectiveness of the system based on the simulation of both methods can be seen through the analysis. …”
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  6. 6
    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Zubair Adil Soomro, Zubair Adil Soomro, Andi Adrianshah, Andi Adrianshah

    Published 2023
    “…Through this study, Q-Learning and State-Action-Reward-StateAction (SARSA) are used in this study and the comparison of results involving both the performance and effectiveness of the system based on the simulation of both methods can be seen through the analysis. …”
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  7. 7
    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Soomro, Zubair Adil, Adrianshah, Andi

    Published 2023
    “…Through this study, Q-Learning and State-Action-Reward-StateAction (SARSA) are used in this study and the comparison of results involving both the performance and effectiveness of the system based on the simulation of both methods can be seen through the analysis. …”
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  8. 8
    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Adil Soomro, Zubair, Adrianshah, Andi

    Published 2023
    “…Through this study, Q-Learning and State-Action-Reward-StateAction (SARSA) are used in this study and the comparison of results involving both the performance and effectiveness of the system based on the simulation of both methods can be seen through the analysis. …”
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  9. 9
    Active force control with iterative learning control algorithm for a vehicle suspension

    Active force control with iterative learning control algorithm for a vehicle suspension by Rosmazi, Rosli

    Published 2013
    “…ILC algorithm is implemented into AFC-based control scheme to reduce its complexity and hence faster response, by replacing the use of artificial intelligence (Al) method as proposed by previous researcher. …”
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  10. 10
    A Comparison of Two Methods Using R.M.S. Value and Wavelet-Based Mann and Morrison Algorithm for Voltage Dip Characterization

    A Comparison of Two Methods Using R.M.S. Value and Wavelet-Based Mann and Morrison Algorithm for Voltage Dip Characterization by Abd Halim, Syahirah

    Published 2009
    “…Another method is a combination of wavelet-based analysis using Mann and Morrison algorithm which estimates the amplitude and the phase angle to characterize the dips. …”
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  11. 11
    Deep Reinforcement Learning For Control

    Deep Reinforcement Learning For Control by Bakar, Nurul Asyikin Abu

    Published 2021
    “…The complete project is carried out in the CARLA simulator to determine how to operate in discrete action space using Deep Reinforcement Learning (DRL) algorithms. …”
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  12. 12
    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Mohamad Hafiz Abu Bakar, Mohamad Hafiz Abu Bakar, Abu Ubaidah Shamsudin, Abu Ubaidah Shamsudin, Ruzairi Abdul Rahim, Ruzairi Abdul Rahim, Zubair Adil Soomro, Zubair Adil Soomro, Andi Adrianshah, Andi Adrianshah

    Published 2023
    “…Through this study, Q-Learning and State-Action-Reward-StateAction (SARSA) are used in this study and the comparison of results involving both the performance and effectiveness of the system based on the simulation of both methods can be seen through the analysis. …”
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  13. 13
    Development of a new robust hybrid automata algorithm based on surface electromyography (SEMG) signal for instrumented wheelchair control

    Development of a new robust hybrid automata algorithm based on surface electromyography (SEMG) signal for instrumented wheelchair control by Mohd Hanafi, Muhammad Sidik

    Published 2020
    “…This method would be a control method to activate power assist system and selected based on conditions set in the algorithm. …”
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  14. 14
    A conceptual framework for multi-objective optimization of building performance: Integrating intelligent algorithms, simulation tools, and climate adaptation

    A conceptual framework for multi-objective optimization of building performance: Integrating intelligent algorithms, simulation tools, and climate adaptation by Rong, Li, Shari, Zalina, Ab Kadir, Mohd Zainal Abidin

    Published 2025
    “…A thematic analysis of 40 peer-reviewed articles was conducted using ATLAS.ti, revealing three dominant research themes: intelligent algorithms, building performance simulation techniques, and adaptive design for climate change. …”
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  15. 15
    A Chameleon algorithm for solving economic dispatch problem in microgrid system

    A Chameleon algorithm for solving economic dispatch problem in microgrid system by Zahraoui Y., Alhamrouni I., Mekhilef S., Kor�tko T., Jusoh A., Sutikno T.

    Published 2024
    “…The obtained results from the simulation are compared with the conventional metaheuristic algorithms which have been used in previous studies, such as particle swarm optimization (PSO), genetic algorithm (GA), and artificial bee colony (ABC). …”
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  16. 16
    Optimal Maintenance Scheduling for Multi-Component E-Manufacturing System

    Optimal Maintenance Scheduling for Multi-Component E-Manufacturing System by Arab, Ali

    Published 2009
    “…Furthermore, it indicates that when the mean time to repairs are longer, this method is more efficient. The results in the simulated testbed indicate that the developed scheduling method using simulation optimization functions properly and can be applied in other cases.…”
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  17. 17
    Improving Channel Assignment for Vehicular Ad-hoc Sensor Network in Disaster Management System

    Improving Channel Assignment for Vehicular Ad-hoc Sensor Network in Disaster Management System by Chiu Shoon, Sia

    Published 2024
    “…Therefore, modification of VASNET is a must to improve and solve the current problem of VASNET technology. The proposed algorithms and methods are implemented using an Omnet++ network simulator with network formation, Channel assignment, and Packet Scheduling to reduce node-to-node interference with computational overhead that significantly impacts the performance. …”
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  18. 18
    Master-leader-slave cuckoo search with parameter control for ANN optimization and its real-world application to water quality prediction

    Master-leader-slave cuckoo search with parameter control for ANN optimization and its real-world application to water quality prediction by Jaddi N.S., Abdullah S., Malek M.A.

    Published 2023
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  19. 19
    A Remedial Action Scheme to Prevent Mid/Long-Term Voltage Instabilities

    A Remedial Action Scheme to Prevent Mid/Long-Term Voltage Instabilities by Khoshkhoo H., Yari S., Pouryekta A., Ramachandaramurthy V.K., Guerrero J.M.

    Published 2023
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  20. 20
    Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market

    Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market by Kavita Jain, Muhammed Basheer Jasser, Muzaffar Hamzah, Akash Saxena, Ali Wagdy Mohamed

    Published 2022
    “…Due to the use of the MPNN, case studies show that the suggested methodology delivers a much larger profit than other state-of-the-art methods and has a better computational performance than the benchmark HHO technique.…”
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