Matching percentages of pairwise comparisons within repeats of the same scan settings for D (default setting) and C (chosen optimal setting). by Melvin Geubbelmans (19565593)

Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping by P. Vangeenderhuysen (15854812)

“…Such analyses should combine a wide physicochemical range of molecules with a minimal amount of sample and resources and downstream data processing workflows that are as automated and time efficient as possible. …”

S2 Data - Text intelligent correction in English translation: A study on integrating models with dependency attention mechanism by Yutong Liu (4027994)

S1 Data - Text intelligent correction in English translation: A study on integrating models with dependency attention mechanism by Yutong Liu (4027994)

Number of wavelengths selected by successive projections algorithm (SPA) (a) and the wavelengths selected by successive projections algorithm (SPA) (b). by Xiao Liu (65413)

Subjects: “…successive projections algorithm…”

Improved random forest algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

K-means++ clustering algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

AUROC values from different algorithms for variables in sequencing data. by Pei-Yau Lung (5090393)

The ANFIS algorithm details. by Mohammadmahdi Taheri (21722285)

“…Illustrative results show that the proposed approach is effective for portfolio selection and optimization in the presence of uncertain data.</p></div>…”

Parallel plot showing hyperparameter exploration. by Ramona Leenings (11167294)

The accuracy of all employed learning algorithms. by Quan Shao (20148209)

Sample data of TSs. by Jiaxin Li (441199)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…A Python-based algorithm was developed for estimating the nonrandom two-liquid (NRTL) model parameters of aqueous binary systems in a straightforward manner from simplified molecular-input line-entry specification (SMILES) strings of substances in a system. …”

Summary by recording: Annotations, training features, and optimal parameters. by Rytis Paškauskas (20404817)

Example of the specific process for generating . by Ming Gao (115719)

All trial data from [16]. by Spencer Caplan (10188380)

Subjects:

First-block trial data from [16]. by Spencer Caplan (10188380)

Subjects:

Second-block trial data from [16]. by Spencer Caplan (10188380)

Subjects:

Super parameter optimization result. by Guomei Cui (20721578)

“…The dataset covers multiple tests of multiple athletes, ensuring the diversity of samples. Secondly, an optimized machine learning algorithm based on decision tree is adopted. …”

Here are the samples prediction of cattle weight using our proposed best 3Conv3Dense model. by Md Junayed Hossain (22615268)