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

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

“…To address this, the research integrates a modified A* pathfinding algorithm with a U-Net convolutional neural network, a custom statistical binary classification method, and a personalized Min-Max connectivity threshold to automate the detection of astrocyte connectivity.…”

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

“…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …”

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

“…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”

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

“…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”

Urgent versus nonurgent binary classification performance. by Kyung Won Kwon (18150416)

Dataset 1: Zip file containing the figures of the presented methods and results in jpeg files by Suchismita Behera (22027316)

“…<p dir="ltr">Figures represented here illustrates the <b>metaheuristic-based band selection framework</b> for hyperspectral image classification using <b>Binary Jaya Algorithm enhanced with a mutation operator</b> to improve population diversity and avoid premature convergence. …”

Feature selection convergence time comparison of various binary metaheuristic algorithms. by Amal H. Alharbi (21755906)

The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

Subjects:

Effect of image resolution on the performance of the binary classifier algorithm in predicting occurrence of a bird target(s) on an individual image using the validation imagery se... by Tsung-Wei Ke (18297551)

Applying the proposed image enhancement algorithm. by Nicodemus Songose Awarayi (18414494)

Dataset 2: Zip file containing the Tables of the presented method and results by Suchismita Behera (22027316)

Effect of training regions on the performance of the Mask R-CNN detection algorithm based on the refined annotation bounding boxes. by Tsung-Wei Ke (18297551)

Accuracy assessment of 2017 binary classification and multi-classification result (OA(Overall accuracy), PA(Producer accuracy), UA(User accuracy), ML(Maximum likelihood)). by Lei Liang (326409)

Performance of the baseline SVM and Mask R-CNN detection algorithms based on the original, manually-derived annotated bounding boxes. by Tsung-Wei Ke (18297551)

Performance of the Mask R-CNN detection algorithm based on the original, manually-derived, annotation bounding boxes, and the refined annotation bounding boxes. by Tsung-Wei Ke (18297551)

The result of the accuracy of tested algorithms. by Nebojsa Bacanin (13932723)

Subjects:

Effect of image resolution on the performance of the Mask R-CNN algorithm in detecting individual bird instances as applied to the validation imagery set. by Tsung-Wei Ke (18297551)

Feature selection ratio results for tested algorithms. by Nebojsa Bacanin (13932723)

Subjects:

Best fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

Subjects: