Flowchart of software workflow. حسب Gavrielle R. Untracht (11820311)

Sample image for illustration. حسب Indhumathi S. (19173013)

"…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …"

DataSheet_1_Stronger wind, smaller tree: Testing tree growth plasticity through a modeling approach.docx حسب Haoyu Wang (429641)

Data_Sheet_2_Computational Neural Modeling of Auditory Cortical Receptive Fields.pdf حسب Jordan D. Chambers (6732224)

Data_Sheet_1_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF حسب Jordan D. Chambers (6732224)

Data_Sheet_3_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF حسب Jordan D. Chambers (6732224)

Quadratic polynomial in 2D image plane. حسب Indhumathi S. (19173013)

"…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …"

Steps in the extraction of 14 coordinates from the CT slices for the curved MPR. حسب Linus Woitke (22783534)

"…Protruding paths are then eliminated using graph-based optimization algorithms, as demonstrated in f). …"

DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx حسب Yuhong Huang (115702)

"…We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). …"

Presentation_1_Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy.PPTX حسب Umesh C. Sharma (10785063)

"…Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. …"

Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf حسب Muhammad Awais (263096)

"…To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …"

Comparison analysis of computation time. حسب Indhumathi S. (19173013)

"…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …"

Process flow diagram of CBFD. حسب Indhumathi S. (19173013)

"…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …"

Precision recall curve. حسب Indhumathi S. (19173013)

"…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …"

Symmetry test result. حسب Jiyong Moon (16819302)

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). …"

Fig 12 - حسب Nisha Yadav (366131)

DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx حسب Jun Zhang (48506)

"…Utilizing the binary “One-vs-Rest” strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. …"

Table_4_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX حسب Hui Tang (226667)

"…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …"

Table_2_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX حسب Hui Tang (226667)

"…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …"