Search Results - (( using action learning algorithm ) OR ( using optimization based algorithm ))

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  1. 1

    Development of deep reinforcement learning based resource allocation techniques in cloud radio access network by Amjad, Iqbal

    Published 2022
    “…A step towards long network performance optimization is theterm use of deep reinforcement learning (DRL), which can learn the best policy via interaction with the environment. …”
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    Final Year Project / Dissertation / Thesis
  2. 2

    Particle swarm optimization with deep learning for human action recognition by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

    Published 2021
    “…If the scene is stationary, the identification of the moving people is addressed based on the correlation tracking technique. Finally, a deep learning neural network is used to evaluate the methodâ��s effectiveness. …”
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    Article
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    Web Usage Mining for UUM Learning Care Using Association Rules by Azizul Azhar, Ramli

    Published 2004
    “…In order to produce the university E-Learning (UUM Educare) portal usage patterns and user behaviors, this paper implements the high level process of Web usage mining using basic Association Rules algorithm - Apriori Algorithm. …”
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    Thesis
  5. 5

    Web usage mining for UUM learning care using association rules by Ramli, Azizul Azhar

    Published 2004
    “…In order to produce the university E-Learning (UUM Educare) portal usage patterns and user behaviors, this paper implements the high level process of Web usage mining using basic Association Rules algorithm - Apriori Algorithm. …”
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    Thesis
  6. 6

    Distributed learning based energy-efficient operations in small cell networks by Mughees, Amna

    Published 2023
    “…Both user association and power allocation are solved in their respective action spaces. Simulation results demonstrate improved performance in power consumption, load, sum rate, utility, learning rate, convergence, and energy efficiency for small base stations (SBSs) and user equipment (UEs) compared to four benchmarked algorithms, including WMMSE, game theory, Q-learning, and DRL. …”
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    Thesis
  7. 7

    A novel softsign fractional-order controller optimized by an intelligent nature-inspired algorithm for magnetic levitation control by Ahmad, Mohd Ashraf, Izci, Davut, Ekinci, Serdar, Mohd Tumari, Mohd Zaidi

    Published 2025
    “…This study presents a novel softsign-function-based fractional-order proportional–integral–derivative (softsign-FOPID) controller optimized using the fungal growth optimizer (FGO) for the stabilization and precise position control of an unstable magnetic ball suspension system. …”
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    Article
  8. 8

    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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    Monograph
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    Hierarchical gaussian reinforcement learning for path planning in uncertain environments by AlDahoul, Nouar, Htike@Muhammad Yusof, Zaw Zaw, Akmeliawati, Rini, Shafie, Amir Akramin

    Published 2015
    “…We propose a path planning algorithm for robots in uncertain environments by using hierarchical Gaussian Q-learning. …”
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    Article
  11. 11

    HELM based Reinforcement Learning for Goal Localization by AlDahoul, Nouar, Htike@Muhammad Yusof, Zaw Zaw

    Published 2016
    “…Hierarchical Extreme Learning Machine (H-ELM) algorithm was used to find good features for effective representation. …”
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    Proceeding Paper
  12. 12

    Prediction analysis of COVID-19 in Selangor by using Backpropagation Algorithm with Conjugate Gradient Method by Noor Amirah Ajmal Khan, Siti Mahani Marjugi

    Published 2024
    “…Backpropagation is a form of artificial neural network (ANN) algorithm that may be used to resolve issues in prediction analysis. …”
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    Article
  13. 13

    Prediction analysis of COVID-19 in Selangor by using backpropagation algorithm with conjugate gradient method by Ajmal Khan, Noor Amirah, Marjugi, Siti Mahani

    Published 2024
    “…Backpropagation is a form of artificial neural network (ANN) algorithm that may be used to resolve issues in prediction analysis. …”
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    Article
  14. 14

    Hierarchical extreme learning machine based reinforcement learning for goal localization by AlDahoul, Nouar, Htike, Zaw Zaw, Akmeliawati, Rini

    Published 2017
    “…Hierarchical Extreme Learning Machine (H-ELM) was used as a fast deep model that doesn’t fine tune the weights. …”
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    Proceeding Paper
  15. 15

    Stock indicator scanner customization tool using deep reinforcement learning by Cheong, Desmond YongHong

    Published 2022
    “…This indicator can then be used as the input of the predictive model. The stock indicators selected by user will be the input of DQN algorithm and act as state while the actions allowed for the DQN agent will be buy and sell. …”
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    Final Year Project / Dissertation / Thesis
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    A simplified PID-like ANFIS controller trained by genetic algorithm to control nonlinear systems by Lutfy, Omar Farouq, Mohd Noor, Samsul Bahari, Marhaban, Mohammad Hamiruce, Abbas, Kassim A.

    Published 2010
    “…In addition, the real-coded Genetic Algorithm (GA) has been utilized to train this ANFIS controller, instead of the hybrid learning methods that are widely used in the literature, and hence, the necessity for the teaching signal required by other techniques has been eliminated. …”
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    Article
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    Graphical user interface test case generation for android apps using Q-learning / Husam N. S. Yasin by Husam , N. S. Yasin

    Published 2021
    “…Instead of randomly selecting the inputs, the test generator learns how to act in an optimal way that explores new states by using new actions to gain more rewards. …”
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    Thesis
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    Intelligent traffic lights using Q-learning by Mohd Yusop, Muhammad Aminuddin, Mansor, Hasmah, Gunawan, Teddy Surya, Nasir, Haidawati,

    Published 2022
    “…Q-learning derives benefits from past experiences and determines the optimal course of action based on them. …”
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    Proceeding Paper
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