Search Results - optimal ((cloud algorithm) OR (((window algorithm) OR (learning algorithm))))

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

“…The first proposed algorithm aims to optimize the EE by controlling the on/off status of RRH via a deep Q network (DQN) and subsequently solving a power optimization problem. …”
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Multi-objective algorithms for effective resource management in Edge-Fog-Cloud computing by Saif, Faten Ameen Mohammed

“…Second, proposed a Multi-objective Grey Wolf Optimizer (MGWO) algorithm for optimizing task scheduling to reduce transmission delay on edge-cloud computing and energy consumption on edge-fog computing. …”
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Hybrid Henry Gas-Harris Hawks comprehensive-opposition algorithm for task scheduling in cloud computing by Omran Alkaam, Nora, Md Sultan, Abu Bakar, Hussin, Masnida, Yatim Sharif, Khaironi

“…This method is based on two elements: comprehensive opposition-based learning (COBL) and Harris Hawks Optimization (HHO). …”
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Fog-cloud scheduling simulator for reinforcement learning algorithms by Al-Hashimi, Mustafa Ahmed Adnan, Rahiman, Amir Rizaan, Muhammed, Abdullah, Hamid, Nor Asilah Wati

“…Therefore, supplying optimized scheduling algorithms to provide satisfactory quality service for the node’s task execution and processing becomes demanding. …”
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3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm by Yong, Ericsson, Muhammad Aizzat, Zakaria, Mohamad Heerwan, Peeie, M. Izhar, Ishak

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Single Step Multivariate Solar Power Forecasting using Adaptive Learning Rate LSTM Model with Optimized Window Size by Kunalan D., Krishnan P.S., Permal N.

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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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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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Optimized processing of satellite signal via evolutionary search algorithm by Hassan, Azmi, Othman, Rusli, Tang, Kieh Ming

“…A robust strategy, called the Pseudo Randomized Search Strategy (PRSS) has been developed to counter the effect of this AS policy. The PRSS algorithm is an adaptive search technique that can learn a high performance knowledge structure in reactive environments that provide information as an objective function. …”
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IOT-based fertigation system / Mohamad Amir Furqan Darus by Darus, Mohamad Amir Furqan

“…These sensors provide real-time data about the crops’ environment, which is then sent to a central hub or cloud platform. Advanced algorithms and machine learning processes this data to determine the ideal irrigation and fertilization needs. …”
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Harnessing reinforcement learning in fog-cloud computing: challenges, insights, and future directions by Al-Hashimi, Mustafa, Rahiman, Amir Rizaan, Muhammed, Abdullah, Hamid, Nor Asilah Wati

“…This article explores the different RL algorithms, emphasizing their distinct strengths, weaknesses, and practical implications in fog-cloud environments. …”
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Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system by Maseer, Ziadoon Kamil

“…The optimized DBN algorithm, known as the HW-DBN algorithm, integrated through feature learning based on a Gaussian–Bernoulli Restricted Boltzmann Machine as well as classification task through a weight neuron network. …”
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Deep reinforcement learning based resource allocation strategy in cloud-edge computing system by Ahmed Adhoni, Zameer, Habelalmateen, Mohammed, DR Janardhana, DR Janardhana, Abdul Sattar, Khalid Nazim, Audah, Lukman

“…This research focuses on the utility of Multiagent Learning framework with Deep Reinforcement Learning (MAL-DRL) which is used for solution deployment concerning resource allocation in such systems, such that the end user enjoys optimization while operators optimize resource utilization. …”
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Deep reinforcement learning based resource allocation strategy in cloud-edge computing system by Ahmed Adhoni, Zameer, Habelalmateen, Mohammed I, D R, Janardhana, Abdul Sattar, Khalid Nazim, Audah, Lukman

“…This research focuses on the utility of Multiagent Learning framework with Deep Reinforcement Learning (MAL-DRL) which is used for solution deployment concerning resource allocation in such systems, such that the end user enjoys optimization while operators optimize resource utilization. …”
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Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm by Aljanad A., Tan N.M.L., Agelidis V.G., Shareef H.

System program management environment in cloud computing using hybrid Genetic Algorithm and Moth Flame Optimization (GA-MFO) by Mohd Erwan Mazalan

“…In this project, Genetic Algorithms (GA) is combine Moth Flame Optimization (MFO) to improve the cloud computing environment. …”
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Design and optimization of a test case generation algorithm for real-time embedded systems based on adaptive Q-Learning by Yingbei, Niu, Chai, Soo See

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The exploration of hybrid metaheuristics-based approaches: A bibliometric analysis by Nur Hidayah, Azmidi, Noryanti, Muhammad, Rozieana, Khairuddin

“…The rapid evolution of computational intelligence has driven significant interest in hybrid metaheuristics, which combine multiple optimization algorithms to solve complicated problems more efficiently. …”
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Malware Detection using Deep Learning (DL) by Chowdhury Sajadul Islam, Madihah Mohd Saudi, Nur Hafiza Zakaria, Muji Setiyo