Search alternatives:
based optimization » whale optimization (Expand Search)
self optimization » wolf optimization (Expand Search), field optimization (Expand Search), lead optimization (Expand Search)
binary case » binary mask (Expand Search), binary image (Expand Search), primary case (Expand Search)
outer layer » output layer (Expand Search)
case based » made based (Expand Search), game based (Expand Search), rate based (Expand Search)
layer self » lower self (Expand Search)
based optimization » whale optimization (Expand Search)
self optimization » wolf optimization (Expand Search), field optimization (Expand Search), lead optimization (Expand Search)
binary case » binary mask (Expand Search), binary image (Expand Search), primary case (Expand Search)
outer layer » output layer (Expand Search)
case based » made based (Expand Search), game based (Expand Search), rate based (Expand Search)
layer self » lower self (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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Scale Production of a Stretchable Fiber Triboelectric Nanogenerator in Customizable Textile for Human Motion Recognition
Published 2024“…Herein, we develop a hierarchical coaxial F-TENG possessing PU layer, Ag layer, and PA layer from the core to the outer part by an efficient and straightforward two-step braiding method. …”
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Scale Production of a Stretchable Fiber Triboelectric Nanogenerator in Customizable Textile for Human Motion Recognition
Published 2024“…Herein, we develop a hierarchical coaxial F-TENG possessing PU layer, Ag layer, and PA layer from the core to the outer part by an efficient and straightforward two-step braiding method. …”
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Analysis and design of algorithms for the manufacturing process of integrated circuits
Published 2023“…From this, we propose: (i) a new ILP model, and (ii) a new solution representation, which, unlike the reference work, guarantees that feasible solutions are obtained throughout the generation of new individuals. Based on this new representation, we proposed and evaluated other approximate methods, including a greedy algorithm and a genetic algorithm that improve the state-of-the-art results for test cases usually used in the literature. …”
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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. …”
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SHAP analysis for 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. …”
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Comparison of intrusion detection systems.
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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Parameter setting for CBOA and PSO.
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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NSL-KDD dataset description.
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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The architecture of LSTM cell.
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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The architecture of ILSTM.
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. …”