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

“…Our results show that deep learning and optimization </p><p dir="ltr">methods, such as the binary GWO algorithm, can be successfully applied to melanoma diagnosis. …”

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

Initial parameter settings for the optimization algorithms. by Doaa Sami Khafaga (21463870)

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

Study selection. by Sebastião Rogério da Silva Neto (11944944)

List of selected mRNAs in the feature selection step. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Binary sentiment polarity distribution of tweets. by Mohamed Kentour (11785652)

Line plots showing performance trends for six evaluation metrics across CNN combined with different bio-inspired optimization algorithms. by Doaa Sami Khafaga (21463870)

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

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

The performance of classifiers based on 60 selected mRNAs. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. by Tehnan I. A. Mohamed (16846175)

Feature selection results. by Doaa Sami Khafaga (21463870)

“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. 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%. …”

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

Selected mRNAs based on association rule mining analysis, for early and late stages of PTC. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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The performance of the SVM classifier based on 60 (30 plus 30-zeroed) selected mRNAs for GSE 60542 dataset. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF by Haidi Wang (9694004)

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

The overview of the proposed method. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Top mRNAs based on repeat count in early-stage and late-stage rules. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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