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

“…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 by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Nor Farizan, Zakaria, Mohd Mawardi, Saari

“…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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Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions by Gorment N.Z., Selamat A., Cheng L.K., Krejcar O.

Machine learning: tasks, modern day applications and challenges by Aljuaid, Lamyaa Zaed, Koh, Tieng Wei, Sharif, Khaironi Yatim

“…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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Differential Search Optimized Random Forest Regression Algorithm for State of Charge Estimation in Electric Vehicle Batteries by Hossain Lipu M.S., Hannan M.A., Hussain A., Ansari S., Ayob A., Saad M.H.M., Muttaqi K.M.

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

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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

“…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 by Tan, Cheng Yong

“…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 by Hossain Lipu M.S., Hannan M.A., Hussain A., Ansari S., Rahman S.A., Saad M.H.M., Muttaqi K.M.

“…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. …”

Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm by Hossain Lipu M.S., Hannan M.A., Hussain A., Saad M.H., Ayob A., Uddin M.N.

Integration of image processing algorithm and deep learning approaches to monitor ginger plant by Tan, Cheng Yong

“…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 by Lipu M.S.H., Hussain A., Saad M.H.M., Hannan M.A.

Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning by Alomoush, Alaa A., Alsewari, Abdulrahman A., Alamri, Hammoudeh S., Aloufi, Khalid, Kamal Z., Zamli

“…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 by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Adil Soomro, Zubair, Adrianshah, Andi

“…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 by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Zubair Adil Soomro, Zubair Adil Soomro, Andi Adrianshah, Andi Adrianshah

“…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 by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Soomro, Zubair Adil, Adrianshah, Andi

“…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 by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Adil Soomro, Zubair, Adrianshah, Andi

“…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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Estimation of electric vehicle turning radius through machine learning for roundabout cornering by Ashaa, Supramaniam, Muhammad Aizzat, Zakaria, Kunjunni, Baarath, Mohamad Heerwan, Peeie, Ahmad Fakhri, Ab. Nasir, Muhammad Izhar, Ishak

“…This paper presents an alternative approach for estimating the turning radius using machine learning technique. While on-board sensors are unable to offer adequate information on vehicle states to the algorithm, vehicle states other than those directly detected by on-board sensors can be inferred using machine learning (ML) approaches based on the collected data. …”
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Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm by Hannan M.A., Lipu M.S.H., Hussain A., Saad M.H., Ayob A.

Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm by Lipu M.S.H., Hannan M.A., Hussain A., Saad M.H.M., Ayob A., Muttaqi K.M.