Table 2_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx حسب Jingna Tao (20997176)

"…We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. Receiver operating characteristic (ROC) curves were plotted using Python 3.12.4. …"

Table 1_Artificial intelligence in nursing: an integrative review of clinical and operational impacts.pdf حسب Salwa Hassanein (20843468)

"…However, despite these benefits, ethical challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. …"

Table 2_Artificial intelligence in nursing: an integrative review of clinical and operational impacts.pdf حسب Salwa Hassanein (20843468)

"…However, despite these benefits, ethical challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. …"

Table 3_Artificial intelligence in nursing: an integrative review of clinical and operational impacts.pdf حسب Salwa Hassanein (20843468)

"…However, despite these benefits, ethical challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. …"

Image 3_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff حسب Jingna Tao (20997176)

"…We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. Receiver operating characteristic (ROC) curves were plotted using Python 3.12.4. …"

Image 2_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff حسب Jingna Tao (20997176)

"…We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. Receiver operating characteristic (ROC) curves were plotted using Python 3.12.4. …"

Image 4_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff حسب Jingna Tao (20997176)

"…We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. Receiver operating characteristic (ROC) curves were plotted using Python 3.12.4. …"

Image 1_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff حسب Jingna Tao (20997176)

"…We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. Receiver operating characteristic (ROC) curves were plotted using Python 3.12.4. …"

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

"…<p dir="ltr">This CSV file contains a comprehensively curated dataset comprising physicochemical descriptors and biological assay data for engineered metal oxide nanoparticles. This dataset was specifically developed to support machine learning model training for toxicity prediction and represents the result of an extensive multi-stage data extraction and curation pipeline. …"

Integrating urinary metabolomics and clinical datasets for multi-cancer detection حسب Dongyong Lee (18786694)

"…</p><p><br></p><p dir="ltr">## Data format</p><p><br></p><p dir="ltr">- Each CSV file contains **two columns** without a header:</p><p dir="ltr"> 1. …"

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows حسب Pierre-Alexis DELAROCHE (22092572)

"…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …"

Patient Demographics. حسب Yiye Zhang (9028589)

"…We developed and validated a machine learning model to predict 72-hour RVA using electronic health records (EHR) data. Study data were extracted from EHR data in 2019 from three urban EDs. …"

Characteristics of RVA visits. حسب Yiye Zhang (9028589)

"…We developed and validated a machine learning model to predict 72-hour RVA using electronic health records (EHR) data. Study data were extracted from EHR data in 2019 from three urban EDs. …"

72-hr RVA Predictive Performance. حسب Yiye Zhang (9028589)

"…We developed and validated a machine learning model to predict 72-hour RVA using electronic health records (EHR) data. Study data were extracted from EHR data in 2019 from three urban EDs. …"

SHAP visualization of 72-hour RVA features. حسب Yiye Zhang (9028589)

"…We developed and validated a machine learning model to predict 72-hour RVA using electronic health records (EHR) data. Study data were extracted from EHR data in 2019 from three urban EDs. …"

Figures and Tables حسب Divya C D (22799186)

"…Robots Comput. Vision XXXI: Algorithms and Techniques, Burlingame, CA, USA, Jan. 23–24, 2012.…"