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

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)

Classification performance after optimization. by Amal H. Alharbi (21755906)

“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”

ANOVA test for optimization results. by Amal H. Alharbi (21755906)

“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”

Wilcoxon test results for optimization. by Amal H. Alharbi (21755906)

“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”

Data Sheet 1_AI-driven optimization of dynamic vibration absorbers with hydraulic amplifier and mechanical inerter integration.pdf by Ahmed Shamseldin (19759437)

Wilcoxon test results for feature selection. by Amal H. Alharbi (21755906)

“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”

Feature selection metrics and their definitions. by Amal H. Alharbi (21755906)

“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”

Statistical summary of all models. by Amal H. Alharbi (21755906)

“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”

Feature selection results. by Amal H. Alharbi (21755906)

“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”

ANOVA test for feature selection. by Amal H. Alharbi (21755906)

“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”

Classification performance of ML and DL models. by Amal H. Alharbi (21755906)

“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”

Machine Learning-Driven Kinetic Elucidation for Sustainable Solvent-Free Continuous ε‑Caprolactone Production via Propionaldehyde-Mediated Nanocarbon Catalysis by Qing Hu (152962)

Machine Learning-Driven Kinetic Elucidation for Sustainable Solvent-Free Continuous ε‑Caprolactone Production via Propionaldehyde-Mediated Nanocarbon Catalysis by Qing Hu (152962)

Data used to drive the Double Layer Carbon Model in the Qinling Mountains. by Huiwen Li (17705280)

“…The model divides the soil profile into topsoil (0-20 cm) and subsoil (20–100 cm) layers to match the SOC maps of the corresponding two layers generated by data-driven models. Each of these layers contains a young carbon pool (CY) with a higher decomposition rate and an old carbon pool (CO) with a lower decomposition rate. …”