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

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

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)

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

RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values . by Jakub Stoklosa (3315042)

Convergence curve of feature selection algorithms across 11 datasets with KNN classifier. by Yi Wei Tye (22208735)

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)

Binary contingency table. by Lu Xiao (314030)

Event-driven data flow processing. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

Classification baseline performance. by Doaa Sami Khafaga (21463870)

Feature selection results. by Doaa Sami Khafaga (21463870)

ANOVA test result. by Doaa Sami Khafaga (21463870)

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

Confusion matrix. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

Parameter settings. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

Dynamic resource allocation process. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

Flow diagram of the proposed model. by Uğur Ejder (22683228)

“…Local Interpretable Model-agnostic Explanations (LIME) were applied to improve interpretability. Across all algorithm models, LR–ABC hybrids outperformed their baseline models (e.g., Random Forest: 85.2% → 91.36% accuracy). …”

Dataset 2: Zip file containing the Tables of the presented method and results by Suchismita Behera (22027316)