Several loop patterns found in binary code. by Alba Pedro-Zapater (8600382)

The main workflow of the proposed algorithm. by Xin Li (51274)

Comparison of images before and after pre-processing: the original image (left) and after applying top-hat and bottom-hat filters (right). by Rinrada Tanthanathewin (18611171)

An example of a histogram of the normalized intensity of an OCT image. by Rinrada Tanthanathewin (18611171)

Examples of results from two different OCT images shown in the left and right columns. by Rinrada Tanthanathewin (18611171)

ILM (pink line) and RPE (green line) layers in a normal OCT image. by Rinrada Tanthanathewin (18611171)

Proposed Algorithm. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”

An example of an OCT image of a DR case (left) and illustration of relevant parts (right): EX-AN blobs (yellow blobs) in the area of interest (a blue region), the ILM (a red line)... by Rinrada Tanthanathewin (18611171)

Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”

Qualitative performance examples of <i>CosegPP</i>’s Buckwheat with three cosegmentation methods (DeepCO³, MIG, Subdiscover) and one plant phenotyping segmentation metho... by Rubi Quiñones (11378540)

Qualitative performance examples of <i>CosegPP</i>’s Sunflower with three cosegmentation methods (DeepCO³, MIG, Subdiscover) and one plant phenotyping segmentation metho... by Rubi Quiñones (11378540)

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Segmentation, trace denoising and spike extraction framework. by Changjia Cai (10647521)

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Visualization of the image segmentation and labeling pipeline for the first frame of an example movie. by Miriam Schnitzerlein (21446136)

Algorithm framework of this paper. by Xiaoqin Wu (470428)

“…This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. …”

Objective performance of fully-automated SLAVV software. by Samuel A. Mihelic (11539996)

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…<p dir="ltr">The first algorithm for segmentation and localization (see PathOlOgics_script_1; segment & localize using a pen) relied on manually tracing the borders of each cell using a digital pen tool on a big touchscreen display showing source images/patches. …”

Table_1_Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.docx by Chenglin Wang (430151)

“…Instead of traditional convolution, Ghostconv is utilized by the neck network of the YOLO-CIT model. The fruit segment of citrus in the original citrus images processed by the R-LBP algorithm is combined with the background segment of the citrus images after grayscale processing to construct synthetic images, which are subsequently added to the training dataset. …”