Image 1_Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model.jpeg حسب Jian Zhang (1682)

"…Six machine learning algorithms were employed to construct the prediction models. …"

Data Sheet 1_Bioinformatic analysis identifies LPL as a critical gene in diabetic kidney disease via lipoprotein metabolism.pdf حسب Qian Dong (414788)

"…Hub genes were screened using differential expression analysis, weighted gene co-expression network analysis (WGCNA), LASSO regression, random forest (RF) algorithms, and consensus clustering for DKD patient classification. …"

Data Sheet 1_Machine learning-based coronary heart disease diagnosis model for type 2 diabetes patients.docx حسب Yingxi Chen (599657)

"…Background<p>To establish a classification model for assisting the diagnosis of type 2 diabetes mellitus (T2DM) complicated with coronary heart disease (CHD).…"

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

"…These innovations promise enhanced diagnostic precision, improved operational workflows, and more personalized patient care. …"

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

"…These innovations promise enhanced diagnostic precision, improved operational workflows, and more personalized patient care. …"

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

"…These innovations promise enhanced diagnostic precision, improved operational workflows, and more personalized patient care. …"

Data Sheet 1_Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients—Machine learning models for clinical decision support.do... حسب Yuanguo Xiong (20135991)

"…Thirteen distinct machine learning (ML) algorithms were trained and benchmarked to construct an optimal risk stratification framework. …"

Data Sheet 1_The critical role of inflammation in osteoporosis prediction unveiled by a machine learning framework integrating multi-source data.pdf حسب Bo Liu (127343)

"…Various machine learning algorithms (including RUSBoosted Trees, Bagged Trees, Support Vector Machines, Gaussian Process Regression, etc.) were used to establish classification and regression prediction models, and model performance was evaluated through rigorous five-fold cross-validation and external validation.…"

Table 1_Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.docx حسب Fanhai Bu (22315168)

"…The least absolute shrinkage and selection operator (LASSO) regression selected predictors from clinical/neuroimaging/laboratory variables. Eight ML algorithms (including Logistic Regression, Random Forest, Extreme Gradient Boosting, Multilayer Perceptron, Support Vector Machine, Light Gradient Boosting Machine, Decision Tree, and K-Nearest Neighbors) were trained using 10-fold cross-validation and evaluated on test/external sets via the area under the curve (AUC), accuracy, precision, recall and F1-score. …"

Labeled sensor dataset of beef cattle behavior grazing desert rangelands حسب Andres Perea (21095165)

"…Proprietary onboard processing algorithms summarize the motion data into a one-dimension motion index (MI) aggregated every 1 minute. …"

Data Sheet 1_Clinical characteristics, prognosis, and predictive modeling in class IV ± V lupus nephritis.docx حسب Anjing Wang (17444020)

"…The prognostic model was developed using machine learning algorithms and Cox regression. The model’s performance was evaluated in terms of discrimination, calibration, and risk classification using the concordance index (C-index), integrated brier score (IBS), net reclassification index (NRI), and integrated discrimination improvement (IDI), respectively.…"

Datasheet1_Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.docx حسب Chor Yiu Chloe Chung (19986408)

"…Objective<p>This systematic review aims to evaluate the quality and accuracy of ML algorithms in predicting ATRX and IDH mutation status in patients with glioma through the analysis of radiomic features extracted from medical imaging. …"

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). …"

AP-2α 相关研究 حسب Ya-Hong Wang (21080642)

"…ImageJ software was employed for precise measurement. The experiment was repeated three times. …"