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

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

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

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

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

Bar chart comparing the average fitness values of various feature selection 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)

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)

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by 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. …”

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

Feature selection process. by Balraj Preet Kaur (20370832)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

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

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Optimization of tracking parameters based on <i>P</i>_<i>swap</i>. by Benjamin Gallois (10131683)

Classification baseline performance. by Doaa Sami Khafaga (21463870)

“…Additionally, utilize the continuous version of OcOA for hyperparameter optimization, further enhancing CNN performance to a maximum accuracy of 98.24%. …”

Feature selection results. by Doaa Sami Khafaga (21463870)

“…Additionally, utilize the continuous version of OcOA for hyperparameter optimization, further enhancing CNN performance to a maximum accuracy of 98.24%. …”

ANOVA test result. by Doaa Sami Khafaga (21463870)

“…Additionally, utilize the continuous version of OcOA for hyperparameter optimization, further enhancing CNN performance to a maximum accuracy of 98.24%. …”

Summary of literature review. by Doaa Sami Khafaga (21463870)

“…Additionally, utilize the continuous version of OcOA for hyperparameter optimization, further enhancing CNN performance to a maximum accuracy of 98.24%. …”

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