Image_3_Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China.TIF حسب Zirui Meng (10671261)

Results of Comprehensive weighting. حسب Hao Yang (328526)

"…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …"

VIF analysis results for hazard-causing factors. حسب Hao Yang (328526)

"…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …"

Benchmark function information. حسب Hao Yang (328526)

"…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …"

Geographical distribution of the study area. حسب Hao Yang (328526)

"…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …"

Flow chart of this study. حسب Hao Yang (328526)

"…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …"

Table_1_Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.pdf حسب Jenish Maharjan (11998331)

"…</p>Methods<p>A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. …"

Image_1_Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.pdf حسب Jenish Maharjan (11998331)

"…</p>Methods<p>A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. …"

Data_Sheet_1_Measuring the impact of monoclonal antibody therapies.pdf حسب Ka Keat Lim (8915009)

Data_Sheet_1_Measuring the impact of monoclonal antibody therapies.pdf حسب Ka Keat Lim (8915009)

SPAM-XAI confusion matrix. حسب Mohd Mustaqeem (19106494)

"…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …"

Illustration of MLP. حسب Mohd Mustaqeem (19106494)

"…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …"

Dataset detail division. حسب Mohd Mustaqeem (19106494)

"…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …"

Software defects types. حسب Mohd Mustaqeem (19106494)

"…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …"

SMOTE representation. حسب Mohd Mustaqeem (19106494)

"…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …"

Demonstration confusion matrix. حسب Mohd Mustaqeem (19106494)

"…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …"

Analysis PC2 AU-ROC curve. حسب Mohd Mustaqeem (19106494)

"…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …"

PROMISE defects prediction attribute aspects. حسب Mohd Mustaqeem (19106494)

"…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …"

SPAM-XAI confusion matrix using PC2 dataset. حسب Mohd Mustaqeem (19106494)

"…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …"

SPAM-XAI using the PC1 dataset. حسب Mohd Mustaqeem (19106494)

"…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …"