AUC score comparison table for all the deep learning algorithms. by Khandaker Mohammad Mohi Uddin (13124356)

Comparative overview of all DL and ML algorithms parameters in used in this study. by Khandaker Mohammad Mohi Uddin (13124356)

Visual representation of the combination of feature extraction approaches employed and classification algorithms to identify stomata. by Alexandre H. Aono (11603934)

Super.Complex identifies likely protein complexes within a PPI network using a distributed supervised AutoML method. by Meghana Venkata Palukuri (11890672)

An exemplar procedure for activity flow modeling based on the direct and shortest paths at a network density of 15%. by Zhengdong Wang (35299)

“…FC, functional connectivity; SPL_wei, shortest path length based on weighted network; SPL_bin, shortest path length based on binary network; Aud, auditory network; CON, cingulo-opercular network; DAN, dorsal attention network; DMN, default mode network; FPN, frontoparietal network; SM, sensorimotor network; SN, salience network; Sub, subcortical network; VAN, ventral attention network; Vis, visual network.…”

Partial dependence plots (A – G) and the resulting clustered feature importance (H) for each feature and trained model. by Daniel Walke (21680915)

“…For each feature, we plotted the average predictions (average ratio of sepsis classification) made by the trained models across different feature values (i.e., grid values). Tree-based algorithms (i.e., Decision Tree, Random Forest, XGBoost, and RUSBoost) are visualized as dashed lines and non-tree-based algorithms (i.e., Logistic Regression, the neural network, the homogeneous GNN, and heterogeneous GNN) as solid lines. …”

Data_Sheet_1_Deep Learning-Based Classification of GAD67-Positive Neurons Without the Immunosignal.pdf by Kotaro Yamashiro (10502753)

Table_1_A toolbox for decoding BCI commands based on event-related potentials.XLSX by Christoph Reichert (8962835)

Table_1_A toolbox for decoding BCI commands based on event-related potentials.XLSX by Christoph Reichert (8962835)

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

DataSheet_1_Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer.pdf by Shahira Abousamra (9417853)

“…Specifically, this training dataset contains TIL positive and negative patches from cancers in additional organ sites and curated data to help improve algorithmic performance by decreasing known false positives and false negatives. …”

The RETA Benchmark for Retinal Vascular Tree Analysis by Xingzheng Lyu (3849409)