The results of the 48 differentially expressed genes obtained by analysing gene expression profiles using the DEseq2 package. by Aninda Astuti (21736334)

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

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The train and test kernel matrix matrices. 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 6 PCs case. by Aninda Astuti (21736334)

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The results of the ten performance metrics for NQE+SVC vs NQE+QSVC based on classifying miRNA expression profiles. by Aninda Astuti (21736334)

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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... by Aninda Astuti (21736334)

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

“…In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

Centered ICE (classification error loss) feature importance plots from Random Forest. by Umanga Gunasekera (21162038)

Classification convergence time showing fitness reduction over 100 iterations for DTOPRS-RBFN, DTO-RBFN, and benchmark models. by Amal H. Alharbi (21755906)

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5.FCV procedure used for classification. by Ebru Ergün (21395498)

“…The ST’s unique ability to capture both local and global features through its hierarchical structure enhances its effectiveness in complex image classification tasks. The model utilizes three distinct datasets: the 12-class BD-Freshwater-Fish dataset, the 10-class SmallFishBD dataset, and the 20-class FishSpecies dataset, focusing on image processing-based classification. …”

Classification accuracy of different methods. by Guanqun Wang (705958)

“…Contemporary artificial intelligence technologies have emerged as the predominant tools for customer segmentation, owing to their robust capabilities in analyzing complex datasets and extracting profound customer insights. …”

Comparison results of multi-classification. by Tao Yang (184280)

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

“…In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

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

“…In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. …”

Pairplot of classification metrics including accuracy, sensitivity, specificity, PPV, NPV, and F-score with regression lines showing linear trends between metric pairs. by Amal H. Alharbi (21755906)

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Details of 2-classification CMHS dataset. by Tao Yang (184280)

Details of 15-classification WADI dataset. by Tao Yang (184280)

Details of 6-classification CMHS dataset. by Tao Yang (184280)

Details of 2-classification WADI dataset. by Tao Yang (184280)

Classification maps for the UP dataset using 1% training samples. by Yuping Yin (3617510)