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

"…This strategy </p><p dir="ltr">not only improves detection efficiency and accuracy but also supports early diagnosis and treatment planning, </p><p dir="ltr">leading to better patient outcomes. By leveraging the binary GWO algorithm to optimize the feature selection </p><p dir="ltr">process and CNNs for image classification, the proposed approach reduces computational costs while increasing </p><p dir="ltr">classification accuracy. …"

The confidence curve of a binary image. حسب Dingming Yang (8505990)

Feature selection convergence time comparison of various binary metaheuristic algorithms. حسب Amal H. Alharbi (21755906)

The confidence change of the binary image after iteration. حسب Dingming Yang (8505990)

The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. حسب Olaide N. Oyelade (14047002)

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

The result of the accuracy of tested algorithms. حسب Nebojsa Bacanin (13932723)

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Feature selection ratio results for tested algorithms. حسب Nebojsa Bacanin (13932723)

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Best fitness value results for tested algorithms. حسب Nebojsa Bacanin (13932723)

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Mean fitness value results for tested algorithms. حسب Nebojsa Bacanin (13932723)

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Standard deviation of fitness value results for tested algorithms. حسب Nebojsa Bacanin (13932723)

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Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification حسب Vaibhavi Patel (19843282)

a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. حسب Nebojsa Bacanin (13932723)

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Boxplots analysis of the tested algorithms using average error rate across 21 datasets. حسب Nebojsa Bacanin (13932723)

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Schematic of iteration process of IDE-IIGA. حسب Ling Zhao (111365)

An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA. حسب Olaide N. Oyelade (14047002)

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Validation of image processing steps and metric results using images of a microfluidic device. حسب Gavrielle R. Untracht (11820311)

Feature selection process. حسب Balraj Preet Kaur (20370832)

Histograms of various water quality parameters, including pH, Hardness, Solids, Chloramines, Sulfate, Organic Carbon, Trihalomethanes, Turbidity, and a binary portability check. حسب Amal H. Alharbi (21755906)

Proposed Algorithm. حسب Hend Bayoumi (22693738)

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