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

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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The parameter settings of the RF algorithm after parameter optimization. by Xini Fang (20861990)

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

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Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

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

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

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

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

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Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

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

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

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

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

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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Iteration curve of the optimization process. by Meijun Shang (22806461)

Feature selection process. by Balraj Preet Kaur (20370832)

Parameter optimization results of the RF model improved by FCM clustering. by Xini Fang (20861990)

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

Proposed Algorithm. by Hend Bayoumi (22693738)

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