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Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT
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Examination timetabling using genetic algorithm case study: KUiTTHO
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Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof
Published 2019“…From the reviews, it is evident that autonomous system is set to handle finite number of encountered states using finite sequences of actions. In order to learn the optimized states-action policy the self-learning algorithm is developed using hybrid AI algorithm by combining unsupervised weightless neural network, which employs AUTOWiSARD and reinforcement learning algorithm, which employs Q-learning. …”
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Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle
Published 2023“…This paper presents the application of a recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) for optimizing the Deep Learning (DL) parameters to estimate the state of charge (SOC) of a battery for an electric vehicle in the real environment. …”
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Network monopoly / Tan Kean Yeap
Published 2002“…With the powerful networking features of Java, it is expected that this project would produce a "real" game engine. …”
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Machine learning: tasks, modern day applications and challenges
Published 2019“…Machine learning algorithms learned from available data. Further, this learning laid the foundation to develop AI for the various systems around us. …”
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Development of open platform controller for step-NC compliant CNC system
Published 2020“…Therefore, this study covers the system which has been designed and developed from outdated and conventional PROLIGHT 1000 Milling CNC machine from Light Intelitek as the lesser cost solution which further discussed in detail based upon upgrades of hardware, architecture, and algorithm design. …”
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Differential Search Optimized Random Forest Regression Algorithm for State of Charge Estimation in Electric Vehicle Batteries
Published 2023Conference Paper -
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Development of deep reinforcement learning based resource allocation techniques in cloud radio access network
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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy
Published 2019“…The experimental results demonstrate that the proposed algorithm is competitive compared to the state-of-the-art semi-supervised learning algorithms in terms of accuracy. …”
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Integration of image processing algorithm and deep learning approaches to monitor ginger plant
Published 2024“…This study aims to integrate image processing and deep learning algorithms to monitor the growth of ginger plants. …”
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Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm
Published 2024“…This paper presents an improved machine learning approach for the accurate and robust state of charge (SOC) in electric vehicle (EV) batteries using differential search optimized random forest regression (RFR) algorithm. …”
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Integration of image processing algorithm and deep learning approaches to monitor ginger plant
Published 2024“…This study aims to integrate image processing and deep learning algorithms to monitor the growth of ginger plants. …”
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Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques
Published 2023Conference Paper -
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Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning
Published 2019“…Many variants have been developed to cope with this problem and improve algorithm performance. …”
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Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning
Published 2023“…A comparison of both Q-learning and State-ActionReward-State-Action (SARSA) based systems in autonomous drone application was performed for evaluation in this study. …”
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Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning
Published 2023“…A comparison of both Q-learning and State-ActionReward-State-Action (SARSA) based systems in autonomous drone application was performed for evaluation in this study. …”
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