Search alternatives:
resource optimization » resource utilization (Expand Search), resource utilisation (Expand Search), resource limitations (Expand Search)
design optimization » bayesian optimization (Expand Search)
task resource » a resource (Expand Search)
binary task » binary mask (Expand Search)
binary case » binary mask (Expand Search), binary image (Expand Search), primary case (Expand Search)
case design » based design (Expand Search), game design (Expand Search), core design (Expand Search)
resource optimization » resource utilization (Expand Search), resource utilisation (Expand Search), resource limitations (Expand Search)
design optimization » bayesian optimization (Expand Search)
task resource » a resource (Expand Search)
binary task » binary mask (Expand Search)
binary case » binary mask (Expand Search), binary image (Expand Search), primary case (Expand Search)
case design » based design (Expand Search), game design (Expand Search), core design (Expand Search)
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MSE for ILSTM algorithm in binary classification.
Published 2023“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
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CDF of task latency, approximated as the inverse of the achieved computation rate.
Published 2025Subjects: -
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Comparisons of computation rate performance for different offloading algorithms.for N = 10, 20, 30.
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Comparison of total time consumed for different offloading algorithms.for N = 10, 20, 30.
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The evolution of the Wireless Power Transfer (WPT) time fraction β over simulation frames.
Published 2025Subjects: -
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Summary of LITNET-2020 dataset.
Published 2023“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”