An example of 2-dimensional non-linearly separable and non-separable data for binary classification. حسب Yuji Oyamada (11714754)

DE algorithm flow. حسب Ling Zhao (111365)

"…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …"

Test results of different algorithms. حسب Ling Zhao (111365)

"…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …"

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:

The maximum accuracy (lowest error rate) with the least number of ANOVA-ranked genes achieved by different feature filtering methods and classification algorithms refined by SIRRFE... حسب Song Cui (553627)

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

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification حسب Vaibhavi Patel (19843282)

The overall accuracy of the binary RF classifications of the RSK region, Morocco from 1985 to 2020. حسب Safia Loulad (16913691)

Random forest binary classification of the colorized Corona image for the RSK region, Morocco (1972). حسب Safia Loulad (16913691)

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)

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)

Prediction AUC of Strokeformer and the competitor methods on the ten large-scale binary classification datasets. حسب Maocheng Cao (22127378)

Prediction AUC of Strokeformer and the competitor methods on the nine small-scale binary classification datasets. حسب Maocheng Cao (22127378)

Confusion matrix for a binary outcome (adapted from Fawcett [30]). حسب João Albuquerque (12936572)

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

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

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

Comparison of performance metrics, SVC vs QSVC after using NQE for data embedding. Bold-faced fonts denote that the classifier achieves better performance. حسب Aninda Astuti (21736334)

Comparison of NQE combined with SVC, NN, and QCNN classifiers. The table shows the number of test cases, comparison with classical classifiers, and data imbalance issues in this an... حسب Aninda Astuti (21736334)

The data processing and analysis pipeline. حسب Xin Zhang (35492)