Q-value of comparison of algorithms with WOA. حسب Fizza Shafique (19996292)

p-values of comparison of algorithms with WOA. حسب Fizza Shafique (19996292)

Optimal image settings for object detection of Iba1 immunolabelled cells in DAB. حسب Danish M. Anwer (15383441)

Location of study area and sampling sizes. حسب Ming-Song Zhao (757598)

"…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …"

S1 Data set - حسب Ming-Song Zhao (757598)

"…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …"

The flowchart of this research. حسب Ming-Song Zhao (757598)

"…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …"

SOM modeling results using characteristic bands. حسب Ming-Song Zhao (757598)

"…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …"

Key variables selected by CARS of raw spectra. حسب Ming-Song Zhao (757598)

"…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …"

SOM modeling results using full spectral bands. حسب Ming-Song Zhao (757598)

"…Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …"

DataSheet_1_A Promising Preoperative Prediction Model for Microvascular Invasion in Hepatocellular Carcinoma Based on an Extreme Gradient Boosting Algorithm.docx حسب Weiwei Liu (341566)

"…Preoperative demographic features, imaging characteristics, and laboratory indexes of the patients were collected. Five machine learning algorithms were used: logistic regression, random forest, support vector machine, extreme gradient boosting (XGBoost), and multilayer perception. …"

Severe pediatric brucellosis prediction model in Yunnan Province, China, 2015–2024. حسب Xin Ma (37268)

Local alignment regime and symbols.pdf حسب Paul A. Gagniuc (1818325)

Supplementary Material for: Prediction Model of Cardiac Risk for Dental Extraction in Elderly Patients with Cardiovascular Diseases حسب Tang M. (3707437)

"…Then, a prediction model was constructed based on the RF algorithm by using a 5-fold cross-validation method. …"

Table_1_Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units.XLSX حسب Qin-Yu Zhao (10014626)

"…A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature elimination (RFE) algorithm was used to select key features. …"

Table_2_Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units.DOCX حسب Qin-Yu Zhao (10014626)

"…A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature elimination (RFE) algorithm was used to select key features. …"

Supplementary file 1_Personalized machine learning–based prognostic model for ICU-acquired bloodstream infections.docx حسب Shijun Zhou (11185840)

"…The model incorporated routinely collected, easily obtainable clinical variables, including several representing the average rate of change in laboratory indicators. After comparing multiple algorithms, eXtreme Gradient Boosting (XGBoost) was selected and optimized using cross-validation and grid search.…"

Data_Sheet_1_Constructing machine learning models based on non-contrast CT radiomics to predict hemorrhagic transformation after stoke: a two-center study.docx حسب Yue Zhang (30585)

"…Then, five ML models were established and evaluated, and the optimal ML algorithm was used to construct the clinical, radiomics, and clinical-radiomics models. …"

Data_Sheet_1_Early Prediction of Cardiogenic Shock Using Machine Learning.PDF حسب Yale Chang (11701611)

"…The input variables include demographics, vital signs, laboratory values, some orders, and specific pre-existing diagnoses. …"

Table 1_Risk prediction for gastrointestinal bleeding in pediatric Henoch-Schönlein purpura using an interpretable transformer model.doc حسب Gahao Chen (21688843)

"…GI complications were stratified into three severity tiers: 1) no complications, 2) abdominal pain without bleeding), and 3) documented rectal bleeding or hemorrhage, based on standardized diagnostic criteria. Five machine learning algorithms (Random Forest, XGBoost, LightGBM, CatBoost, and TabPFN-V2) were optimized through nested cross-validation. …"

Image_1_Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation.tif حسب Qi Wang (22418)

"…</p>Methods<p>To this end, this study, by utilizing the transcriptomic as well as single cell data and integrating 20 mainstream machine-learning (ML) algorithms. We optimized an AI-based predictor for GC diagnosis. …"