The architecture of LSTM cell. by 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. by 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. by 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. by 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. by 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. by Samiul Alam (18563323)

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

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

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

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

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

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

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

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

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

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

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

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

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

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