Search alternatives:
based optimization » whale optimization (Expand Search)
solid optimization » policy optimization (Expand Search), wolf optimization (Expand Search), acid optimization (Expand Search)
binary case » binary mask (Expand Search), primary case (Expand Search)
image solid » stage solid (Expand Search), image solely (Expand Search)
case based » made based (Expand Search), game based (Expand Search), rate based (Expand Search)
based optimization » whale optimization (Expand Search)
solid optimization » policy optimization (Expand Search), wolf optimization (Expand Search), acid optimization (Expand Search)
binary case » binary mask (Expand Search), primary case (Expand Search)
image solid » stage solid (Expand Search), image solely (Expand Search)
case based » made based (Expand Search), game based (Expand Search), rate based (Expand Search)
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21
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. …”
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22
Parameter setting for LSTM.
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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23
LITNET-2020 data splitting approach.
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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24
Transformation of symbolic features in NSL-KDD.
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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25
Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf
Published 2020“…And we propose two new multilabel uses of the Common Spatial Pattern (CSP) algorithm to optimize the signal-to-noise ratio, namely MC2CMI and MC2SMI approaches. …”
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27
Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield...
Published 2022“…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”