Bayesian analysis for varying coefficient autoregressive models حسب Ying Wu (19057)

"…The corresponding MCMC algorithms are provided to compute the posterior distributions. …"

Examples of seed sentiment words. حسب Jinghua Wu (436901)

"…Positive and negative seed words were extracted through BERT-based Term Frequency (TF) analysis, and the SO-PMI algorithm was applied to calculate sentiment scores for candidate words. …"

Comment preprocessing workflow. حسب Jinghua Wu (436901)

"…Positive and negative seed words were extracted through BERT-based Term Frequency (TF) analysis, and the SO-PMI algorithm was applied to calculate sentiment scores for candidate words. …"

Comparison of experimental results. حسب Jinghua Wu (436901)

"…Positive and negative seed words were extracted through BERT-based Term Frequency (TF) analysis, and the SO-PMI algorithm was applied to calculate sentiment scores for candidate words. …"

ROC mean curves of different lexicons. حسب Jinghua Wu (436901)

"…Positive and negative seed words were extracted through BERT-based Term Frequency (TF) analysis, and the SO-PMI algorithm was applied to calculate sentiment scores for candidate words. …"

Partial experimental data. حسب Jinghua Wu (436901)

"…Positive and negative seed words were extracted through BERT-based Term Frequency (TF) analysis, and the SO-PMI algorithm was applied to calculate sentiment scores for candidate words. …"

Comparison results of different thresholds. حسب Jinghua Wu (436901)

"…Positive and negative seed words were extracted through BERT-based Term Frequency (TF) analysis, and the SO-PMI algorithm was applied to calculate sentiment scores for candidate words. …"

Taobao experimental data results. حسب Jinghua Wu (436901)

"…Positive and negative seed words were extracted through BERT-based Term Frequency (TF) analysis, and the SO-PMI algorithm was applied to calculate sentiment scores for candidate words. …"

Commonly used sentiment lexicons. حسب Jinghua Wu (436901)

"…Positive and negative seed words were extracted through BERT-based Term Frequency (TF) analysis, and the SO-PMI algorithm was applied to calculate sentiment scores for candidate words. …"

Correlation matrix. حسب Jinghua Wu (436901)

"…Positive and negative seed words were extracted through BERT-based Term Frequency (TF) analysis, and the SO-PMI algorithm was applied to calculate sentiment scores for candidate words. …"

Data Sheet 1_Autonomic nervous system development-related signature as a novel predictive biomarker for immunotherapy in pan-cancers.docx حسب Cunen Wu (12503014)

"…A pan-cancer predictive model for ICI prognosis based on ANSDR.Sig was constructed, with the random forest algorithm yielding the most robust performance. …"

Data Sheet 1_AI-enabled workforce integration: blended human resource contribution rate in Chinese companies.pdf حسب Kexin Zhang (301752)

Improving Machine Learning Classification Predictions through SHAP and Features Analysis Interpretation حسب Leonardo Bernal (22461790)

"…Tree-based machine learning (ML) algorithms, such as Extra Trees (ET), Random Forest (RF), Gradient Boosting Machine (GBM), and XGBoost (XGB) are among the most widely used in early drug discovery, given their versatility and performance. …"

An Automated Intermolecular Reaction Discovery Approach Relying on Heuristic Atom-Partitioned Frontier Orbital Features حسب Ying Chen (9697)

"…The algorithm is based on atomistic features derived from inexpensive electronic structure theory calculations. …"

Flowchart of the method. حسب Jianghong Yuan (6082580)

"…Experimental results demonstrate that FHCDSR achieves superior performance on both datasets, with AUC values of 90.20% (Hermiston) and 95.39% (Yancheng), outperforming six state-of-the-art comparison methods by 3.39–14.78% in detection accuracy. Remarkably, the algorithm maintains high computational efficiency, completing analyses in 9.76 seconds (Hermiston) and 10.90 seconds (Yancheng), representing up to 94.05% reduction in processing time compared to conventional methods. …"

Development and validation of machine learning models for predicting acute kidney injury in acute-on-chronic liver failure: a multimodel comparative study حسب Jing Zhang (23775)

"…Therefore, this study aimed to develop prediction models for AKI in ACLF patients based on machine learning (ML) algorithms.…"

Table 1_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx حسب XinPei Liu (16560699)

"…These features informed the development of machine learning models, including logistic regression, linear and radial basis function support vector machines, XGBoost, decision trees, and random forests. …"

Table 3_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx حسب XinPei Liu (16560699)

"…These features informed the development of machine learning models, including logistic regression, linear and radial basis function support vector machines, XGBoost, decision trees, and random forests. …"

Table 2_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx حسب XinPei Liu (16560699)

"…These features informed the development of machine learning models, including logistic regression, linear and radial basis function support vector machines, XGBoost, decision trees, and random forests. …"

Table 4_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx حسب XinPei Liu (16560699)

"…These features informed the development of machine learning models, including logistic regression, linear and radial basis function support vector machines, XGBoost, decision trees, and random forests. …"