Display of the web prediction interface. by Meng Cao (105914)

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Sample screening flowchart. by Meng Cao (105914)

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Descriptive statistics for variables. by Meng Cao (105914)

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SHAP summary plot. by Meng Cao (105914)

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Performance metrics of the models on the training and test set. by Meng Cao (105914)

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

“…<p dir="ltr">Figures represented here illustrates the <b>metaheuristic-based band selection framework</b> for hyperspectral image classification using <b>Binary Jaya Algorithm enhanced with a mutation operator</b> to improve population diversity and avoid premature convergence. …”

Classification result on the ModelNet40 dataset. by Lei Wang (6656)

Classification results on the FG3D dataset. by Lei Wang (6656)

Objective performance of fully-automated SLAVV software. by Samuel A. Mihelic (11539996)

Calibration curves for NLP classification. by Charlene Jennifer Ong (8997278)

An illustration of the shape-to-graph mapping. by William Pilcher (9339332)

Melanoma Detection by Means of Multiple Instance Learning by Annabella Astorino (11644324)

“…In this work we have applied a MIL algorithm on some clinical data constituted by color dermoscopic images, with the aim to discriminate between melanomas (positive images) and common nevi (negative images). …”

Receiver operating curves for NLP classification. by Charlene Jennifer Ong (8997278)

ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)

Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data by Fei Xue (24567)

“…In this article, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. …”

G-clusteron classification of handwritten digits (MNIST) and the resultant distribution of synaptic locations and activations. by Toviah Moldwin (10866708)

Mean accuracies of the classifiers trained with image descriptor features for the stomata classification task. by Alexandre H. Aono (11603934)

Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans. by Ramsés Noguez Imm (6658637)

DataSheet_1_Histopathology image classification: highlighting the gap between manual analysis and AI automation.pdf by Refika Sultan Doğan (17799677)

“…The study develops an AI-based architecture for image classification using histopathological images, aiming to achieve high performance with less complexity through specific parameters and layers. …”

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

“…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”