Result comparison with other existing models. by Md. Sabbir Hossain (9958939)

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Overview of the proposed simple CNN for extracting features from CT scan images. by Md. Sabbir Hossain (9958939)

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Parameter attributes of the CNN feature extractor model. by Md. Sabbir Hossain (9958939)

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Evaluation of performance across various settings. by Md. Sabbir Hossain (9958939)

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Dataset distribution. by Md. Sabbir Hossain (9958939)

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Visualization of color maps using Grad-CAM for different lung cancer classes. by Md. Sabbir Hossain (9958939)

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CNN structure for feature extraction. by Md. Sabbir Hossain (9958939)

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Class distribution for 5-class classification. 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. …”

Class distribution for 3-class classification. 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. …”

Receiver operating characteristic curve for the prostate dataset. by Hootan Kamran (21519846)

Receiver operating characteristic curve for the breast dataset. by Hootan Kamran (21519846)

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

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

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)

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. …”

Flow diagram of the proposed model. by Uğur Ejder (22683228)

“…Clinical, demographic, and supplement variables were preprocessed into 21 predictors. Four algorithms (K-Nearest Neighbors, Classification and Regression Tree, Support Vector Machine, and Random Forest) were implemented alongside their LR–ABC hybrid counterparts. …”

Feature selection results. 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%. …”

ANOVA test result. 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%. …”

Summary of literature review. 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%. …”

Model & data for automated classification of male and female house wren song by Karan Odom (18271399)