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

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

MSE for ILSTM algorithm in binary classification. 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. …”

Training process. by Ning Zhao (84707)

Several loop patterns found in binary code. by Alba Pedro-Zapater (8600382)

Quantitative experimental results of the present algorithm and other methods. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-01. by Tingting Liu (267387)

Flow chart of particle swarm algorithm. by Nour Eldeen Mahmoud Khalifa (19259450)

“…The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50).…”

Pseudo-color results of our algorithm versus other methods on Lytro-05. by Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-05. by Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-01. by Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-05. by Tingting Liu (267387)

Proposed architecture testing phase. by Yasir Khan Jadoon (21433231)

“…The proposed architecture is trained on the selected datasets, whereas the hyperparameters are chosen using the particle swarm optimization (PSO) algorithm. The trained model is employed in the testing phase for the feature extraction from the self-attention layer and passed to the shallow wide neural network classifier for the final classification. …”

Process of chromosome selection. by Xiaoxuan Wu (4455202)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

Health Macro Region Clustering and Final Dengue Regionalization of Brazil by Ayrton Sena Gouveia (21587635)

The structure of the Resnet50. by Nour Eldeen Mahmoud Khalifa (19259450)

“…The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50).…”