S1 Data - by Dai Wan (20108122)

Subjects:

Comparison of Proposed Model using Unseen Data. by Saim Chishti (22272188)

“…Various models have successfully identified beta-thalassemia carriers using CBC data alone, but these models perform classification and prediction based on normalized data. …”

Comparison of proposed model using unseen data. by Saim Chishti (22272188)

“…Various models have successfully identified beta-thalassemia carriers using CBC data alone, but these models perform classification and prediction based on normalized data. …”

Figures of NEXT STEPS IN THE DEVELOPMENT OF PCA-NN MODEL FOR THE IONOSPHERE: DIFFERENT PARAMETERS, REGIONS, NN ALGORITHMS AND IMPUT PARAMETERS by Sara Garcia (22419613)

List of the time used by each algorithm. by JiaMing Gong (20427837)

“…To this end, this paper proposes an entropy-based dynamic ensemble classification algorithm (EDAC) to consider data streams with class imbalance and concept drift simultaneously. …”

Knowledge Graph validation using SHACL Shapes by Ángel Iglesias Préstamo (19745767)

S1 Data - by Jingru Dong (14076094)

Subjects:

Sanitized databases using MLHProtector algorithm. by Loan T. T. Nguyen (20660789)

“…<div><p>Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. …”

Data Sheet 1_The prediction of karst-collapse susceptibility levels based on the ISSA-ELM integrated model.docx by Jiaxin Wang (483624)

ML algorithms used in this study. by Toheeb Salahudeen (21368040)

Notations and definitions used in the algorithm. by Nazym Alimbekova (22766158)

DataSheet1_A novel strain-based bone-fracture healing algorithm is able to predict a range of healing outcomes.PDF by George T. Morgan (19867125)

“…This study introduces a novel, strain-based fracture-healing algorithm designed to predict a wide range of healing outcomes, including both successful unions and non-unions. …”

Contributions of the clinical features variables to risk prediction using feature importance. by Jingru Dong (14076094)

Subjects:

Data Sheet 1_Flow-based parameterization for DAG and feature discovery in scientific multimodal data.pdf by Elise Walker (19937997)

Various depictions of figures can be created using different software using the same data. by Hector Torres (11708207)

Data Sheet 1_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.docx by Ming Xie (420493)

“…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …”

Data inclusion and exclusion process. by Jingru Dong (14076094)

Subjects:

Schematic diagram of fusion model based on voting mechanism. by Jingru Dong (14076094)

Subjects:

Data Sheet 1_Multi-stage bidirectional informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidance.pdf by Jian Li (41607)

Supplementary file 1_The value of unsupervised machine learning algorithms based on CT and MRI for predicting sarcopenia.doc by Huayan Zuo (22308277)

“…Objectives<p>This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold partitioning, in predicting sarcopenia based on computed tomography (CT) and magnetic resonance imaging (MRI) data.…”