Heat map of variable correlation. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Comparison of models test results. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Technical route. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Confusion matrix. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

GA-XGBoost feature importance order diagram. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

The summary of the literature review. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Chi-square test for selected features. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

ROC curve of GA-XGBoost. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Distribution of bank customer churn label. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Data Sheet 1_Leveraging automated time-lapse microscopy coupled with deep learning to automate colony forming assay.docx by Anusha Klett (20748443)

“…The detection model accurately identified the majority of objects in the dataset.</p>Results<p>This AI-assisted CFA was successfully applied for density optimization, enabling the determination of seeding densities that maximize plating efficiency (PE), and for IC50 determination, offering an efficient, less labor-intensive method for testing drug concentrations. …”

Table 1_Adverse events in the neonatal intensive care unit identified by triggers.pdf by Fabiana Bragança Albanese (21450395)

“…Objective<p>The main aim of this study was to identify adverse events (AEs) in neonates admitted to a Neonatal Intensive Care Unit (NICU) using a trigger-based approach.…”

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

“…<p>Accurate diagnosis of the initial phase of Alzheimer’s disease (AD) is essential and crucial. The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). …”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…</p><p dir="ltr">These biological metrics were used to define a binary toxicity label: entries were classified as toxic (1) or non-toxic (0) based on thresholds from standardized guidelines (e.g., ISO 10993-5:2009) and literature consensus. …”

Image_4_Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.tif by Ze Long (8568447)

“…Purpose<p>Using an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study’s objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases.…”

Image_7_Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.tif by Ze Long (8568447)

“…Purpose<p>Using an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study’s objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases.…”

Table_1_Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.docx by Ze Long (8568447)

“…Purpose<p>Using an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study’s objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases.…”

Image_5_Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.tif by Ze Long (8568447)

“…Purpose<p>Using an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study’s objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases.…”

Image_6_Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.tif by Ze Long (8568447)

“…Purpose<p>Using an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study’s objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases.…”

Image_3_Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.tif by Ze Long (8568447)

“…Purpose<p>Using an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study’s objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases.…”

Image_1_Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.tif by Ze Long (8568447)

“…Purpose<p>Using an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study’s objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases.…”