The source of the fan datasets and details. by Xingtao Wu (22139242)

“…Keywords: Multi-strategy adaptive coati optimization algorithm; Improved weighted extreme learning machine; Wind turbine blade icing fault detection; Fault detection.…”

Importance of the attributes of fan No.15. by Xingtao Wu (22139242)

“…Keywords: Multi-strategy adaptive coati optimization algorithm; Improved weighted extreme learning machine; Wind turbine blade icing fault detection; Fault detection.…”

Framework of MACOA-IWKELM. by Xingtao Wu (22139242)

“…Keywords: Multi-strategy adaptive coati optimization algorithm; Improved weighted extreme learning machine; Wind turbine blade icing fault detection; Fault detection.…”

Structure chart of the IWKELM. by Xingtao Wu (22139242)

“…Keywords: Multi-strategy adaptive coati optimization algorithm; Improved weighted extreme learning machine; Wind turbine blade icing fault detection; Fault detection.…”

Flow chart of the IWKELM. by Xingtao Wu (22139242)

“…Keywords: Multi-strategy adaptive coati optimization algorithm; Improved weighted extreme learning machine; Wind turbine blade icing fault detection; Fault detection.…”

Experimental results for marginal sample sets. by Xingtao Wu (22139242)

“…Keywords: Multi-strategy adaptive coati optimization algorithm; Improved weighted extreme learning machine; Wind turbine blade icing fault detection; Fault detection.…”

The source and details of the datasets. by Xingtao Wu (22139242)

“…Keywords: Multi-strategy adaptive coati optimization algorithm; Improved weighted extreme learning machine; Wind turbine blade icing fault detection; Fault detection.…”

Study flowchart. by Jingqi Dong (22378904)

“…Differential expression gene (DEG) analysis was performed on the profiles, followed by further screening using four machine learning algorithms. Concurrently, weighted gene co-expression network analysis (WGCNA) was applied to identify gene modules, and enrichment analysis of WGCNA-derived genes was conducted to explore their biological functions. …”

The top ten related predicted drug compounds. by Jingqi Dong (22378904)

“…Differential expression gene (DEG) analysis was performed on the profiles, followed by further screening using four machine learning algorithms. Concurrently, weighted gene co-expression network analysis (WGCNA) was applied to identify gene modules, and enrichment analysis of WGCNA-derived genes was conducted to explore their biological functions. …”

Feature importance heat map of fan No.21. by Xingtao Wu (22139242)

“…Keywords: Multi-strategy adaptive coati optimization algorithm; Improved weighted extreme learning machine; Wind turbine blade icing fault detection; Fault detection.…”

Feature importance heat map of fan No.15. by Xingtao Wu (22139242)

“…Keywords: Multi-strategy adaptive coati optimization algorithm; Improved weighted extreme learning machine; Wind turbine blade icing fault detection; Fault detection.…”

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach by Sadaf Aqil (22183571)

“…PhyCysID uses a set of 21 features derived from amino acid composition, in combination with 15 distinct machine learning algorithms, to classify phytocystatin sequences into one of the four subtypes. …”

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach by Sadaf Aqil (22183571)

“…PhyCysID uses a set of 21 features derived from amino acid composition, in combination with 15 distinct machine learning algorithms, to classify phytocystatin sequences into one of the four subtypes. …”

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach by Sadaf Aqil (22183571)

“…PhyCysID uses a set of 21 features derived from amino acid composition, in combination with 15 distinct machine learning algorithms, to classify phytocystatin sequences into one of the four subtypes. …”

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach by Sadaf Aqil (22183571)

“…PhyCysID uses a set of 21 features derived from amino acid composition, in combination with 15 distinct machine learning algorithms, to classify phytocystatin sequences into one of the four subtypes. …”

Wilcoxon test results 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. …”

Feature selection metrics and their definitions. 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. …”

Statistical summary of all 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. …”

Classification performance after 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. …”

ANOVA test for optimization 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. …”