The confidence curve of a binary image. by Dingming Yang (8505990)

The confidence change of the binary image after iteration. by Dingming Yang (8505990)

Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. by Tehnan I. A. Mohamed (16846175)

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

“…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …”

Confusion matrix for binary classification. by Nicodemus Songose Awarayi (18414494)

“…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …”

A* Path-Finding Algorithm to Determine Cell Connections by Max Weng (22327159)

“…Future work aims to generalize this algorithm for broader biological applications by training additional Cellpose models and adapting the A* framework.…”

Robustness of pix H-score to spatial sampling. by Sripad Ram (10987346)

Robustness to noisy perturbations. by Paolo Muratore (8331585)

Improved LMedS-based for sag measurement accuracy of transmission lines via PSO method by Xingzhi Ren (21400870)

Empirical approach to assess robustness of pix H-score to spatial sampling. by Sripad Ram (10987346)

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

“…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …”

tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

Summary of existing CNN models. by Nicodemus Songose Awarayi (18414494)

“…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …”

Robustness of 2-NNCRNs against random node removal. by Yuka Fujiki (12269596)

Data_Sheet_1_RFG-TVIU: robust factor graph for tightly coupled vision/IMU/UWB integration.docx by Gongjun Fan (18458502)

“…Aiming at the dynamic change of sensor availability in multi-sensor integrated navigation system and the serious problem of traditional factor graph in the weight distribution of observation information, this study proposes an adaptive robust factor graph model. Based on redundant measurement information, we propose a novel adaptive estimation model for UWB ranging covariance, which does not rely on prior information of the system and can adaptively estimate real-time covariance changes of UWB ranging. …”

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

One variant per line data for precision study. by Wenshu Li (762627)

One variant per line data for concordance study. by Wenshu Li (762627)

One variant per line data for LoD study. by Wenshu Li (762627)