بدائل البحث:
resource optimization » resource utilization (توسيع البحث), resource utilisation (توسيع البحث), resource limitations (توسيع البحث)
design optimization » bayesian optimization (توسيع البحث)
task resource » a resource (توسيع البحث)
binary task » binary mask (توسيع البحث)
binary case » binary mask (توسيع البحث), binary image (توسيع البحث), primary case (توسيع البحث)
case design » based design (توسيع البحث), game design (توسيع البحث), core design (توسيع البحث)
resource optimization » resource utilization (توسيع البحث), resource utilisation (توسيع البحث), resource limitations (توسيع البحث)
design optimization » bayesian optimization (توسيع البحث)
task resource » a resource (توسيع البحث)
binary task » binary mask (توسيع البحث)
binary case » binary mask (توسيع البحث), binary image (توسيع البحث), primary case (توسيع البحث)
case design » based design (توسيع البحث), game design (توسيع البحث), core design (توسيع البحث)
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MSE for ILSTM algorithm in binary classification.
منشور في 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.
منشور في 2025الموضوعات: -
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Comparisons of computation rate performance for different offloading algorithms.for N = 10, 20, 30.
منشور في 2025الموضوعات: -
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Comparison of total time consumed for different offloading algorithms.for N = 10, 20, 30.
منشور في 2025الموضوعات: -
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The evolution of the Wireless Power Transfer (WPT) time fraction β over simulation frames.
منشور في 2025الموضوعات: -
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Summary of LITNET-2020 dataset.
منشور في 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. …"