Random forest binary classification of the colorized Corona image for the RSK region, Morocco (1972). حسب Safia Loulad (16913691)

Random forest algorithm: Method and example results. حسب Natalie Weed (7345124)

"…(<b>D</b>) Schematic illustration of arrays input into Random Forest algorithm. Columns correspond to gene, rows to pixels in the top projection data set. …"

Creation of top projections from coronal images of gene expression. حسب Natalie Weed (7345124)

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx حسب Veera Narayana Balabathina (22518524)

"…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …"

ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... حسب G. Christopher Marais (21492084)

Analysis of geo-spatiotemporal data using machine learning algorithms and reliability enhancement for urbanization decision support حسب Kwame O. Hackman (9289505)

"…<p>We present systematic analyses of the temporal dynamics of the growth of Kumasi, the fastest growing city in Ghana using 20-year Landsat time-series data from 2000 to 2020 (with 1986 Landsat image as a baseline). Two classification algorithms – random forest (RF) and support vector machines (SVM) – were used to produce binary (built-up / non-built up) maps for all years within the temporal span. …"

Image_1_A predictive model based on random forest for shoulder-hand syndrome.JPEG حسب Suli Yu (14947807)

Table_1_Angiotensin System Autoantibodies Correlate With Routine Prognostic Indicators for COVID-19 Severity.docx حسب Carmen M. Labandeira (12217994)

DataSheet_1_Histopathology image classification: highlighting the gap between manual analysis and AI automation.pdf حسب Refika Sultan Doğan (17799677)

"…Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. …"

The steps of proposed method of RWMA detection using echocardiography images for RWMA detection, consisting with cardiac structure segmentation using a trained U-Net on both A4C an... حسب Sazzli Kasim (11218558)

Imaging parameters. حسب Reihaneh Hassanzadeh (11986041)

"…We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). …"

DataSheet_1_Preoperatively Estimating the Malignant Potential of Mediastinal Lymph Nodes: A Pilot Study Toward Establishing a Robust Radiomics Model Based on Contrast-Enhanced CT I... حسب Mengshi Dong (5181833)

"…The performance of the model was assessed and compared to that of five other machine learning algorithms (support vector machine, naive Bayes, random forest, decision tree, K-nearest neighbor) using receiver operating characteristic (ROC) curves. …"

Participants’ demographic characteristics. حسب Reihaneh Hassanzadeh (11986041)

"…We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). …"

Image_1_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.JPEG حسب Kirsty E. Waddington (5754545)

Image_2_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.JPEG حسب Kirsty E. Waddington (5754545)

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... حسب Uttam Khatri (12689072)

"…The accuracy obtained by the proposed method was reported for binary classification. More importantly, the classification results of the less commonly reported MCIs vs. …"

DataSheet_1_Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer.docx حسب Shima Sepehri (11574997)

"…Logistic regression (LR), random forest (RF), and support vector machine (SVM), as well as their consensus through averaging the output probabilities, were considered for feature selection and modeling for overall survival (OS) prediction as a binary classification (either median OS or 6 months OS). …"