Correlation heatmap of performance scores among different feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. حسب Doaa Sami Khafaga (21463870)

Density distribution plot of performance metrics for various feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. حسب Doaa Sami Khafaga (21463870)

Urgent versus nonurgent binary classification performance. حسب Kyung Won Kwon (18150416)

a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. حسب Nebojsa Bacanin (13932723)

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

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

Classification baseline performance. حسب Doaa Sami Khafaga (21463870)

"…To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. …"

Performance of the bAD-PSO-Guided WOA algorithm compared with another algorithm. حسب Ahmed M. Elshewey (21463867)

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)

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)

Dataset 1: Zip file containing the figures of the presented methods and results in jpeg files حسب Suchismita Behera (22027316)

The performance of the machine learning algorithms to differentiate between W and other sleep stages during three and five class binary classification tasks on the test set sleep s... حسب Sowmya M. Ramaswamy (18954755)

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)

ROC curves of the proposed method for binary class classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning. حسب Md. Sabbir Hossain (9958939)

The train and test kernel matrix matrices. حسب Aninda Astuti (21736334)

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

The workflow of integrating NQE with QSVC and QNN classifiers. حسب Aninda Astuti (21736334)

Training loss results plotted against the number of iterations. حسب Aninda Astuti (21736334)

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)

The results of the 4 differentially expressed miRNAs obtained by analysing miRNAs expression profiles using the R ‘limma’ package. حسب Aninda Astuti (21736334)