Search Results - (( java application optimisation algorithm ) OR ( dynamics optimization learning algorithm ))

A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot by Low, Ee Soong, Ong, Pauline, Low, Cheng Yee

“…This study proposes an improved Q-learning (IQL) algorithm to address the challenges of path planning in such environments. …”
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A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot by Ee Soong Low, Ee Soong Low, Pauline Ong, Pauline Ong, Cheng Yee Low, Cheng Yee Low

“…This study proposes an improved Q-learning (IQL) algorithm to address the challenges of path planning in such environments. …”
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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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Adaptable algorithms for performance optimization of dynamic batch manufacturing processes by Teo, Kenneth Tze Kin

“…The dynamic changes causing the need of dynamic modelling for a better dynamic optimization will be catered via a specifically formulated fitness function. …”
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Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications by Shanmugam Y., Narayanamoorthi R., Ramachandaramurthy V.K., Bernat P., Shrestha N., Son J., Williamson S.S.

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A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot by Ee Soong Low, Ee Soong Low, Pauline Ong, Pauline Ong, Cheng Yee Low, Cheng Yee Low

“…This study proposes an improved Q-learning (IQL) algorithm to address the challenges of path planning in such environments. …”
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An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks by Mustapha, Ibrahim, Mohd Ali, Borhanuddin, A. Rasid, Mohd Fadlee, Sali, Aduwati, Mohamad, Hafizal

“…We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. …”
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Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA by Zahari, Taha, Farzad, Tahriri, Siti Zawiah, Md Dawal

“…An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. …”
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Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems by Din, Fakhrud, Khalid, Shah, Fayaz, Muhammad, Gwak, Jeonghwan, Kamal Z., Zamli, Mashwani, Wali Khan

“…Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. …”
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Q-Learning traffic signal optimization within multiple intersections traffic network by Chin, Yit Kwong, Kow, Wei Yeang, Khong, Wei Leong, Tan, Min Keng, Teo, Kenneth Tze Kin

“…The experimental results show that the Q-Learning algorithm is able to learn from the dynamic traffic flow and optimized the traffic flow.…”
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Designing a new model for worm response using security metrics by Madihah Mohd Saudi, Taib B.M.

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New bio-inspired barnacle optimizers based least-square support vector machine for time-series prediction of pandemic outbreaks by Marzia, Ahmed

“…By integrating evolutionary optimization with advanced machine learning techniques, these models offer potent solutions to effectively tackle the challenges posed by pandemics, such as the complex dynamics of COVID-19.…”
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Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms by Koh, Johnny Siaw Paw

“…Also, this research involves in developing a machine-learning system and program via genetic algorithm that is capable of performing independent learning capability and optimization for scanning sequence using novel GA operators. …”
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Investigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Condition by Yew W.H., Fat Chau C., Mahmood Zuhdi A.W., Syakirah Wan Abdullah W., Yew W.K., Amin N.

“…These DRL-based algorithms optimize the local and global maximum power point (MPP) using deep Q-learning and deep deterministic policy gradient (DDPG). …”

A reinforcement learning-based energy-efficient spectrum-aware clustering algorithm for cognitive radio wireless sensor network by Mustapha, Ibrahim

“…Simulation results show convergence, learning and adaptability of the RL based algorithms to dynamic environment toward achieving the optimal solutions. …”
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee by Chong , Hue Yee

“…In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
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Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid by Ab Hamid, Salbiah

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Designing a new model for Trojan horse detection using sequential minimal optimization by Madihah Mohd Saudi, Abuzaid A.M., Taib B.M., Abdullah Z.H.

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

“…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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Wavelet neural networks based solutions for elliptic partial differential equations with improved butterfly optimization algorithm training by Lee, Sen Tan, Zainuddin, Zarita, Ong, Pauline

“…Although the gradient information of the commonly used gradient descent training algorithm in WNNs may direct the search to optimal weight solutions that minimize the error function, the learning process is slow due to the complex calculation of the partial derivatives. …”
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