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

“…This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”

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

“…This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”

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

“…This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”

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

“…This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”

Supplementary file 1_An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction.docx by Songpeng Zhao (21563714)

Table 1_Static and temporal dynamic changes in brain activity in patients with post-stroke balance dysfunction: a pilot resting state fMRI.docx by Zhiqing Tang (12895406)

“…XGBoost is a promising machine learning method to explore these changes.</p>…”

Adaptive ionic liquid polymer microwave modulation surface with reprogrammable dielectric properties by Haipeng Lu (22770302)

“…Building on this mechanism, we applied machine learning algorithms to establish correlations between temperature, ionic liquid concentration, and dielectric constant, enabling the design of a reprogrammable dielectric microwave modulation surface. …”

Significance of variables in the RF model. by Chen Chen (6544)

“…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. We included 199 subjects, aged 20 to 70, and measured cognitive performance with the Wisconsin Card Sorting Test. …”

Performance metrics of the RF model. by Chen Chen (6544)

“…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. We included 199 subjects, aged 20 to 70, and measured cognitive performance with the Wisconsin Card Sorting Test. …”

Heat map of the top 9 protein variables. by Chen Chen (6544)

“…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. We included 199 subjects, aged 20 to 70, and measured cognitive performance with the Wisconsin Card Sorting Test. …”

Demographic data of the study population. by Chen Chen (6544)

“…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. We included 199 subjects, aged 20 to 70, and measured cognitive performance with the Wisconsin Card Sorting Test. …”

Overall working procedure of this study. by Chen Chen (6544)

“…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. We included 199 subjects, aged 20 to 70, and measured cognitive performance with the Wisconsin Card Sorting Test. …”

WCST scores and protein expression levels. by Chen Chen (6544)

“…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. We included 199 subjects, aged 20 to 70, and measured cognitive performance with the Wisconsin Card Sorting Test. …”

The overall framework of this study. by Tianbao Feng (21722233)

“…Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and 8 machine learning algorithms were used to narrowed down hub genes. …”

PANoptosis related genes. by Tianbao Feng (21722233)

“…Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and 8 machine learning algorithms were used to narrowed down hub genes. …”

Primer sequences of <i>Bm</i>x and β-actin. by Tianbao Feng (21722233)

“…Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and 8 machine learning algorithms were used to narrowed down hub genes. …”

Dataset: EEG-Controlled Exoskeleton for Walking and Standing - A Longitudinal Study of Healthy Individuals by Shantanu Sarkar (20848745)

S1 Data - by Ali Akbar Moosavi (17769033)

“…First, physico-chemical inputs as bulk density (BD), initial water content (W_i), saturated water content (W_s), mean weight diameter (MWD), and geometric mean diameter (GMD) of aggregates, pH, electrical conductivity (EC), and calcium carbonate equivalent (CCE) were measured. Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

Sample ESTIMATE score dataset (AR vs CTRL). by MaoMeng Wang (22177417)

“…A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. …”

STRING PPI network edges dataset. by MaoMeng Wang (22177417)

“…A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. …”