The Pseudo-Code of the IRBMO Algorithm. حسب Chenyi Zhu (9383370)

"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"

IRBMO vs. meta-heuristic algorithms boxplot. حسب Chenyi Zhu (9383370)

"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"

IRBMO vs. feature selection algorithm boxplot. حسب Chenyi Zhu (9383370)

"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"

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)

The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. حسب Olaide N. Oyelade (14047002)

الموضوعات:

One advantage of our algorithm is that it is not binary in nature. حسب Yong Kuk Kim (13955214)

Feature selection convergence time comparison of various binary metaheuristic algorithms. حسب Amal H. Alharbi (21755906)

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. …"

The quantum embedding circuit designed for the miRNA dataset, featuring three layers of repeated Hadamard (H), RZ, and control-NOT unitary gates. حسب Aninda Astuti (21736334)

الموضوعات:

Training loss results plotted against the number of iterations for (a) 9, (b) 10, (c) 11, and (d) 12 principal components. حسب Aninda Astuti (21736334)

الموضوعات:

The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+SVC and NQE+QSVC in the 5 PCs case. حسب Aninda Astuti (21736334)

الموضوعات:

The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC12. حسب Aninda Astuti (21736334)

الموضوعات:

The results of the ten performance metrics for NQE+SVC vs NQE+QSVC using (a) 9, (b) 10, (c) 11, and (d) 12 principle components, respectively. حسب Aninda Astuti (21736334)

الموضوعات:

The workflow diagram for the gene expression biomarker study which involve using principal component analysis (PCA) to reduce the number of features (principal components), thus re... حسب Aninda Astuti (21736334)

الموضوعات:

Classification performance results using 5-fold cross-validation for the training datasets after applying the data imbalance tool, SMOTE and NQE for data embedding. The values with... حسب Aninda Astuti (21736334)

الموضوعات:

Data embedding by using Neural Quantum Embedding (NQE). حسب Aninda Astuti (21736334)

الموضوعات:

The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC11. حسب Aninda Astuti (21736334)

الموضوعات:

The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+NN and NQE+QNN in the 6 PCs case. حسب Aninda Astuti (21736334)

الموضوعات:

The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+NN and NQE+QNN in the 5 PCs case. حسب Aninda Astuti (21736334)

الموضوعات: