Process flow diagram of CBFD. by 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. by 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. by Jiyong Moon (16819302)

DataSheet_2_Predicting non-native seaweeds global distributions: The importance of tuning individual algorithms in ensembles to obtain biologically meaningful results.docx by Samuel Sainz-Villegas (14137968)

“…Inspecting the number of predictors to include in final ensembles and the selection of algorithms and its complexity have been demonstrated to be crucial for this purpose. …”

DataSheet_3_Predicting non-native seaweeds global distributions: The importance of tuning individual algorithms in ensembles to obtain biologically meaningful results.zip by Samuel Sainz-Villegas (14137968)

“…Inspecting the number of predictors to include in final ensembles and the selection of algorithms and its complexity have been demonstrated to be crucial for this purpose. …”

DataSheet_1_Predicting non-native seaweeds global distributions: The importance of tuning individual algorithms in ensembles to obtain biologically meaningful results.docx by Samuel Sainz-Villegas (14137968)

“…Inspecting the number of predictors to include in final ensembles and the selection of algorithms and its complexity have been demonstrated to be crucial for this purpose. …”

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by 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 - by Nisha Yadav (366131)

Table 1_Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles.docx by Cândida H. L. Alves (21838364)

“…In addition, using variance, skewness, and kurtosis centered on the median allowed us to achieve 100% sensitivity, specificity, and accuracy in our analyses, even using less complex algorithms, due to selecting these optimal characteristics. …”

Presentation_1_Finite Element-Based Personalized Simulation of Duodenal Hydrogel Spacer: Spacer Location Dependent Duodenal Sparing and a Decision Support System for Spacer-Enabled... by Hamed Hooshangnejad (12282518)

“…To provide a preoperative simulation of hydrogel spacers, we presented a patient-specific spacer simulator algorithm and used it to create a decision support system (DSS) to provide a preoperative optimal spacer location to maximize the spacer benefits.…”

DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx by 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. …”

Data_Sheet_1_Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking.PDF by Daniel F. B. Haeufle (5831594)

“…We therefore propose a novel algorithm based on the pattern search approach specifically designed to solve this constraint optimization problem. …”

Data_Sheet_2_Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking.pdf by Daniel F. B. Haeufle (5831594)

“…We therefore propose a novel algorithm based on the pattern search approach specifically designed to solve this constraint optimization problem. …”

Logical Fault Detection Approach for Mixed Control Flipping Faults in Reversible Circuits by Mousum Handique (21385838)

“…Experimental results are evaluated based on the MixCFF detection and the MixCFF fault coverage range with the help of different benchmark circuits using the suggested ATPG algorithm.…”

Flow diagram of the automatic animal detection and background reconstruction. by David Tadres (9120564)

“…(E) The threshold value is calculated based on the histogram: it is the mean of the image subtracted by 4 (optimal value defined by trial and error). …”

Data_Sheet_1_Multivariate Brain Functional Connectivity Through Regularized Estimators.DOCX by Raymond Salvador (813880)

“…Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. …”

Data_Sheet_2_Multivariate Brain Functional Connectivity Through Regularized Estimators.DOCX by Raymond Salvador (813880)

“…Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. …”

Data_Sheet_3_Multivariate Brain Functional Connectivity Through Regularized Estimators.DOCX by Raymond Salvador (813880)

“…Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. …”

Successful Closure of an Aortic Pseudoaneurysm and Mitral Paravalvular Leak Via a Hybrid Transapical Approach by Adam Carroll (475017)

“…Additionally, for the treatment of complex pathology, a procedural algorithm of approach is essential. …”

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... by Uttam Khatri (12689072)

“…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”