A* Path-Finding Algorithm to Determine Cell Connections حسب Max Weng (22327159)

"…Pixel paths were classified using a z-score brightness threshold of 1.21, optimized for noise reduction and accuracy. …"

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)

DataSheet1_Quantum-inspired optimization for wavelength assignment.PDF حسب Aleksey S. Boev (14441805)

PathOlOgics_RBCs Python Scripts.zip حسب Ahmed Elsafty (16943883)

"…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …"

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. …"

MCLP_quantum_annealer_V0.5 حسب Anonymous Anonymous (4854526)

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.…"