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

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

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

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

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

The illustration depicts our CQ-CNN architecture for binary image classification. حسب Mominul Islam (3513758)

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

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

Summary of existing CNN models. حسب Nicodemus Songose Awarayi (18414494)

Classification baseline performance. حسب 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. …"

Feature selection results. حسب 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. …"

ANOVA test result. حسب 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. …"

Summary of literature review. حسب 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. …"

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm حسب 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. …"

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment حسب Jianfang Cao (1881379)

"…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …"

Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf حسب Muhammad Awais (263096)

tUbenet Foundation Model Weights حسب Natalie Holroyd (7099391)

Presentation_1_Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy.PPTX حسب Umesh C. Sharma (10785063)

"…Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. …"

Processed dataset to train and test the WGAN-GP_IMOA_DA_Ensemble model حسب Ramya Chinnasamy (21633527)

"…This framework integrates a novel biologically inspired optimization algorithm, the Indian Millipede Optimization Algorithm (IMOA), for effective feature selection. …"