Performance on GradEva. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

The considered test problems. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

Performance on FunEva. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

Performance on Iter. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

Continuation of Table 2. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

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

“…<p>It shows the step-by-step transformation for two examples on the left and right sides: a) a binary mask of the lower dental arch is shown, which is compared with the binarized CT images to obtain the best slice containing the lower jaw, as shown in b). …”

PathOlOgics_RBCs Python Scripts.zip by 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). …”

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. …”

Table_1_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.pdf by Wenchao Zhang (1324959)

Table_2_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.pdf by Wenchao Zhang (1324959)

Presentation_1_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.pdf by Wenchao Zhang (1324959)

Table_3_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.pdf by Wenchao Zhang (1324959)

Data_Sheet_1_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.zip by Wenchao Zhang (1324959)

Image_1_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.tif by Wenchao Zhang (1324959)

Data_Sheet_3_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.zip by Wenchao Zhang (1324959)

Data_Sheet_2_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.zip by Wenchao Zhang (1324959)