The architecture of LSTM cell. حسب Asmaa Ahmed Awad (16726315)

"…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. …"

The architecture of ILSTM. حسب Asmaa Ahmed Awad (16726315)

"…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. …"

Parameter setting for LSTM. حسب Asmaa Ahmed Awad (16726315)

"…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. …"

LITNET-2020 data splitting approach. حسب Asmaa Ahmed Awad (16726315)

"…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. …"

Transformation of symbolic features in NSL-KDD. حسب Asmaa Ahmed Awad (16726315)

"…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. …"

Latent state estimations for experimental data with interruptions for subject 18. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 22. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 17. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 13. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 3. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 17. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 15. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 7. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 13. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 1. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 16. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 20. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 15. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 21. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 5. حسب Samiul Alam (18563323)