ROC curves of the proposed method for binary class classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning. حسب Md. Sabbir Hossain (9958939)

Computing the centerline from a binary segmentation. حسب Patrick D. McClanahan (2385076)

ROC curves of the proposed method for multiclass classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning. حسب Md. Sabbir Hossain (9958939)

Normalized confusion matrix of the proposed method for multiclass classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning. حسب Md. Sabbir Hossain (9958939)

Normalized confusion matrix of the proposed method for multiclass classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, (F) Ensemble LEARNING. حسب Md. Sabbir Hossain (9958939)

Fig 1 - حسب Dillion M. Fox (4043159)

Raw Data for "Development of Decision Support Systems Based on Fuzzy and Binary Logic for the FOREX Foreign Exchange Market" حسب Serhii Hetsko (22413559)

"…This would also allow a reasonable approach to the choice of a specific method.</p><p dir="ltr"><b>Research objective.</b>The aim of this work is to develop multi-timeframe hybrid DSS for algorithmic trading based on fuzzy and classical binary logic with probabilistic elements. …"

Four kinds of Hebbian and anti-Hebbian learning using Ca²⁺. حسب Toviah Moldwin (10866708)

The SHAP (Shapley Additive exPlanations) values of observed variables. حسب Xin Zhang (35492)

Detecting an insect with background subtraction. حسب Malika Nisal Ratnayake (10131402)

Possible plasticity rules for presynaptic input- and spike-dependent- calcium. حسب Toviah Moldwin (10866708)

<b>Multimodal MRI radiomics</b><b> based on </b><b>habitat subregions of the tumor microenvironment</b><b> for predicting risk stratification in glioblastoma</b> حسب Han Wang (21457334)

Pan-cancer machine learning predictions of MEKi response. حسب John P. Lloyd (10196288)

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

"…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…"

Flowchart scheme of the ML-based model. حسب Noshaba Qasmi (20405009)

Twitter dataset حسب mehdi khalil (20153943)

"…</li><li><b>Labeling</b>: Each post is annotated with binary labels indicating its authenticity (real or fake).…"

Objective performance of fully-automated SLAVV software. حسب Samuel A. Mihelic (11539996)

Table_1_Machine learning models identify micronutrient intake as predictors of undiagnosed hypertension among rural community-dwelling older adults in Thailand: a cross-sectional s... حسب Niruwan Turnbull (11506910)

"…Objective<p>To develop a predictive model for undiagnosed hypertension (UHTN) in older adults based on five modifiable factors [eating behaviors, emotion, exercise, stopping smoking, and stopping drinking alcohol (3E2S) using machine learning (ML) algorithms.</p>Methods<p>The supervised ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB)] with SHapley Additive exPlanations (SHAP) prioritization and conventional statistics (χ² and binary logistic regression) were employed to predict UHTN from 5,288 health records of older adults from ten primary care hospitals in Thailand.…"

Table_2_Machine learning models identify micronutrient intake as predictors of undiagnosed hypertension among rural community-dwelling older adults in Thailand: a cross-sectional s... حسب Niruwan Turnbull (11506910)

"…Objective<p>To develop a predictive model for undiagnosed hypertension (UHTN) in older adults based on five modifiable factors [eating behaviors, emotion, exercise, stopping smoking, and stopping drinking alcohol (3E2S) using machine learning (ML) algorithms.</p>Methods<p>The supervised ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB)] with SHapley Additive exPlanations (SHAP) prioritization and conventional statistics (χ² and binary logistic regression) were employed to predict UHTN from 5,288 health records of older adults from ten primary care hospitals in Thailand.…"

Data_Sheet_1_Machine learning models identify micronutrient intake as predictors of undiagnosed hypertension among rural community-dwelling older adults in Thailand: a cross-sectio... حسب Niruwan Turnbull (11506910)

"…Objective<p>To develop a predictive model for undiagnosed hypertension (UHTN) in older adults based on five modifiable factors [eating behaviors, emotion, exercise, stopping smoking, and stopping drinking alcohol (3E2S) using machine learning (ML) algorithms.</p>Methods<p>The supervised ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB)] with SHapley Additive exPlanations (SHAP) prioritization and conventional statistics (χ² and binary logistic regression) were employed to predict UHTN from 5,288 health records of older adults from ten primary care hospitals in Thailand.…"