MSE for ILSTM algorithm in binary classification. 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. …"

Comparison between ILSTM and machine learning-based algorithms in binary classification. حسب Asmaa Ahmed Awad (16726315)

Comparison between ILSTM and deep learning-based algorithms in binary classification. حسب Asmaa Ahmed Awad (16726315)

Binary encoding scheme. حسب Binh Thanh Dang (12368831)

Box plots with swarm overlays for six feature selection algorithms, annotated with mean and standard deviation values. حسب Doaa Sami Khafaga (21463870)

Confusion matrices for KDDTest+ in binary classification. حسب Asmaa Ahmed Awad (16726315)

Shap analysis for NSL-KDD in binary classification. حسب Asmaa Ahmed Awad (16726315)

Confusion matrices for KDDTest-21 in binary classification. حسب Asmaa Ahmed Awad (16726315)

Confusion matrices for LITNET-2020 dataset in binary classification. حسب Asmaa Ahmed Awad (16726315)

LSTM vs ILSTM for LITNET-2020 in binary classification. حسب Asmaa Ahmed Awad (16726315)

LSTM vs ILSTM for KDDTest-21 for binary classification. حسب Asmaa Ahmed Awad (16726315)

Statistic Test for KDDTest+ and KDDTest-21 in binary classification. حسب Asmaa Ahmed Awad (16726315)

The parameter settings of the RF algorithm after parameter optimization. حسب Xini Fang (20861990)

Accuracy and number of iterations for LSTM and ILSTM using KDDTest+ in binary classifications. حسب Asmaa Ahmed Awad (16726315)

Comparison between LSTM and ILSTM using LITNET-2020 in binary classification. حسب Asmaa Ahmed Awad (16726315)

Comparison between other methods in literature using KDDTest+ for binary classification. حسب Asmaa Ahmed Awad (16726315)

Features selected by optimization algorithms. حسب Afnan M. Alhassan (18349378)

"…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …"

Comparison between LSTM, LSTM-BOA, LSTM-CBOA and ILSTM using KDDTest+ and KDDTest-21 in binary classification with average 10 runs. حسب Asmaa Ahmed Awad (16726315)

Iteration curve of the optimization process. حسب Meijun Shang (22806461)

Parameter optimization results of the RF model improved by FCM clustering. حسب Xini Fang (20861990)