Multiple collinearity diagnosis results. by Peiwen Zhang (1863202)

Histogram of accuracy values for different machine learning models across multiple runs. by Amal H. Alharbi (21755906)

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

Table 1_A machine learning approach to predict postoperative sleep disturbance after total knee arthroplasty: a comparative study of multiple algorithms.docx by Yi-xiang Zhang (22563236)

Prediction results of different models. by Qinghua Li (398885)

Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms by Guangyin Jia (21075537)

“…By comparing 8 machine learning algorithms and using a weighted soft voting method to integrate the two optimal algorithms, we established 108 prediction models and identified the best ensemble learning model MACCS_LK for screening and defining its application domain. …”

Framework of SW-Metapath2vec algorithm. by Lang Chai (20521817)

“…The study was conducted using multiple real-world and synthetic datasets to validate the proposed algorithm’s performance. …”

Table 1_Using machine learning to predict the rupture risk of multiple intracranial aneurysms.xlsx by Junqiang Feng (10300150)

“…The widely used PHASES score does not incorporate morphological parameters of aneurysms and is not specifically designed for patients with multiple aneurysms. Therefore, we constructed a risk prediction model for the rupture of MIAs by machine learning algorithms.…”

Machine Learning Modeling for ABC Transporter Efflux and Inhibition: Data Curation, Model Development, and New Compound Interaction Predictions by Nada J. Daood (18626266)

“…In recent years, multiple computational studies have used machine learning models to predict substrate binding and inhibition of ATP-binding cassette (ABC) transporters. …”

Equitable Hospital Length of Stay Prediction for Patients with Learning Disabilities and Multiple Long-term Conditions Using Machine Learning by Emeka Abakasanga (14235503)

“…<p dir="ltr"><b>Purpose:</b> Individuals with learning disabilities (LD) often face higher rates of premature mortality and prolonged hospital stays compared to the general population. Predicting the length of stay (LOS) for patients with LD and multiple long-term conditions (MLTCs) is critical for improving patient care and optimising medical resource allocation. …”

Supplementary information files for "Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning" by Emeka Abakasanga (14235503)

“…Predicting the length of stay (LOS) for patients with LD and multiple long-term conditions (MLTCs) is critical for improving patient care and optimising medical resource allocation. …”

Data Sheet 1_Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning.pdf by Emeka Abakasanga (20037582)

“…Purpose<p>Individuals with learning disabilities (LD) often face higher rates of premature mortality and prolonged hospital stays compared to the general population. Predicting the length of stay (LOS) for patients with LD and multiple long-term conditions (MLTCs) is critical for improving patient care and optimising medical resource allocation. …”

The operating principle of APDCA. by Yangsong He (13909371)

Overall flow chart of the model. by Yetong Fang (21433414)

“…Traditional credit scoring methods often have difficulty in fully capturing the characteristics of large-scale, high-dimensional financial data, resulting in limited prediction performance. To address these issues, this paper proposes a credit score prediction model that combines CNNs and BiGRUs, and uses the GWO algorithm for hyperparameter tuning. …”

Flow chart of the BiGRU model. by Yetong Fang (21433414)

“…Traditional credit scoring methods often have difficulty in fully capturing the characteristics of large-scale, high-dimensional financial data, resulting in limited prediction performance. To address these issues, this paper proposes a credit score prediction model that combines CNNs and BiGRUs, and uses the GWO algorithm for hyperparameter tuning. …”

Flow chart of the CNN model. by Yetong Fang (21433414)

“…Traditional credit scoring methods often have difficulty in fully capturing the characteristics of large-scale, high-dimensional financial data, resulting in limited prediction performance. To address these issues, this paper proposes a credit score prediction model that combines CNNs and BiGRUs, and uses the GWO algorithm for hyperparameter tuning. …”

Flow chart of the GWO model. by Yetong Fang (21433414)

“…Traditional credit scoring methods often have difficulty in fully capturing the characteristics of large-scale, high-dimensional financial data, resulting in limited prediction performance. To address these issues, this paper proposes a credit score prediction model that combines CNNs and BiGRUs, and uses the GWO algorithm for hyperparameter tuning. …”

Meta-paths of the “Author-Paper-Venue” network. by Lang Chai (20521817)

“…The study was conducted using multiple real-world and synthetic datasets to validate the proposed algorithm’s performance. …”

Learning achievement prediction results. by Tingting Tong (11993299)

“…To overcome these challenges, this research combines the strengths of various machine learning algorithms to design a robust model that performs well across multiple metrics, and uses interpretability analysis to elucidate the prediction results. …”

High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization by Stephanos Karafiludis (12902591)

“…This work highlights the potential of integrating automated synthesis platforms with data-driven algorithms to accelerate the discovery of high-entropy materials, offering an efficient design pathway to advanced functional materials.…”