The Pseudo-Code of the IRBMO Algorithm. by 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. by 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. by 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. by Asmaa Ahmed Awad (16726315)

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

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

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

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

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

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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. by Aninda Astuti (21736334)

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

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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. by Aninda Astuti (21736334)

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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... by Aninda Astuti (21736334)

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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... by Aninda Astuti (21736334)

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

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

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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. by Aninda Astuti (21736334)

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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. by Aninda Astuti (21736334)

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The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+NN and NQE+QNN in the 4 PCs case. by Aninda Astuti (21736334)

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The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC10. by Aninda Astuti (21736334)

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