Case 1: PADR evaluation without scheduling. by Hisham Alghamdi (20114096)

“…MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. …”

SPVE hourly varying irradiance. by Hisham Alghamdi (20114096)

“…MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. …”

Hourly varying ambient temperature. by Hisham Alghamdi (20114096)

“…MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. …”

Estimated SPVE generation. by Hisham Alghamdi (20114096)

“…MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. …”

Case 2: Hourly scheduled load profile. by Hisham Alghamdi (20114096)

“…MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. …”

Storage batteries charging level. by Hisham Alghamdi (20114096)

“…MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. …”

Utility pricing scheme. by Hisham Alghamdi (20114096)

“…MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. …”

Quantum Computing and peptide folding by Akshay Uttarkar (19699990)

“…<p dir="ltr">The work "Peptide Folding with Quantum CVaR-VQE Algorithm" represents a significant advancement in the field of computational biology, particularly in the challenging domain of protein folding. …”

Table 3_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.xlsx by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Table 2_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.xlsx by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Image 3_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.tif by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Image 2_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.tif by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Image 1_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.tif by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Table 1_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.xlsx by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Data Sheet 1_Investigating neural markers of Alzheimer's disease in posttraumatic stress disorder using machine learning algorithms and magnetic resonance imaging.pdf by Gabriella Yakemow (20137758)

“…Introduction<p>Posttraumatic stress disorder (PTSD) is a mental health disorder caused by experiencing or witnessing traumatic events. …”

Supplementary file 1_Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.zip by Nina de Lacy (6559520)

“…Deep learning was guided by an evolutionary algorithm that jointly performed optimization across hyperparameters and automated feature selection, allowing more candidate predictors and a wider variety of predictor types to be analyzed than the largest previous comparable machine learning studies.…”

-value on CEC2022 (dim = 20). by Yuqi Xiong (12343771)

“…<div><p>Metaheuristic optimization algorithms often face challenges such as complex modeling, limited adaptability, and a tendency to get trapped in local optima when solving complex optimization problems. …”

Precision elimination strategy. by Yuqi Xiong (12343771)

“…<div><p>Metaheuristic optimization algorithms often face challenges such as complex modeling, limited adaptability, and a tendency to get trapped in local optima when solving complex optimization problems. …”

Results of low-light image enhancement test. by Yuqi Xiong (12343771)

“…<div><p>Metaheuristic optimization algorithms often face challenges such as complex modeling, limited adaptability, and a tendency to get trapped in local optima when solving complex optimization problems. …”

Evaluation metrics obtained by SBOA and MESBOA. by Yuqi Xiong (12343771)

“…<div><p>Metaheuristic optimization algorithms often face challenges such as complex modeling, limited adaptability, and a tendency to get trapped in local optima when solving complex optimization problems. …”