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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Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

“…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …”

Convergence curve of feature selection algorithms across 11 datasets with KNN classifier. by Yi Wei Tye (22208735)

(a) Accuracy and (b) loss curves of training and validation during binary class classification for 512 features extracted by the CNN. by Md. Sabbir Hossain (9958939)

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Table 1_A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA).docx by Charmayne Mary Lee Hughes (12959972)

“…</p>Conclusion<p>Machine learning algorithms show promise for the detection of current frailty status, particularly in binary classification. …”

Class distribution for binary classes. by Toheeb Salahudeen (21368040)

“…Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. …”

Evaluation of Binary Classifiers for Asymptotically Dependent and Independent Extremes by Juliette Legrand (21771740)

Explicability results of the crowding classification model. by Kengo Oka (1420585)

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Explicability results of the deep bite classification model. by Kengo Oka (1420585)

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Explicability results of the maxillary protrusion classification model. by Kengo Oka (1420585)

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Bar chart comparing the average fitness values of various feature selection algorithms. by Doaa Sami Khafaga (21463870)

AUC score comparison table for all the deep learning algorithms. by Khandaker Mohammad Mohi Uddin (13124356)

Classification baseline performance. by Doaa Sami Khafaga (21463870)

“…Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”

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

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Comparative overview of all DL and ML algorithms parameters in used in this study. by Khandaker Mohammad Mohi Uddin (13124356)

Box plots with swarm overlays for six feature selection algorithms, annotated with mean and standard deviation values. by Doaa Sami Khafaga (21463870)