Search alternatives:
network optimization » swarm optimization (Expand Search), wolf optimization (Expand Search)
primary data » primary care (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
data global » daily global (Expand Search)
network optimization » swarm optimization (Expand Search), wolf optimization (Expand Search)
primary data » primary care (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
data global » daily global (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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Features selected by optimization algorithms.
Published 2024“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things
Published 2025“…Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …”
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Proposed Algorithm.
Published 2025“…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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Hybrid feature selection algorithm of CSCO-ROA.
Published 2024“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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Comparisons between ADAM and NADAM optimizers.
Published 2025“…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”