Performance Comparison of State-of-the-Art WPT-MEC Offloading Methods. by Hend Bayoumi (22693738)

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

Image processing workflow. by Denis Tamiev (7404980)

“…<p>Raw fluorescent microscope images (a) were processed with a binary segmentation algorithm, and clusters of bacterial cells were manually annotated. …”

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

“…</p><p dir="ltr">Astrocytes were dissociated from E18 mouse cortical tissue, and image data were processed using a Cellpose 2.0 model to mask nuclei. …”

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

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

tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

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

“…This strategy </p><p dir="ltr">not only improves detection efficiency and accuracy but also supports early diagnosis and treatment planning, </p><p dir="ltr">leading to better patient outcomes. By leveraging the binary GWO algorithm to optimize the feature selection </p><p dir="ltr">process and CNNs for image classification, the proposed approach reduces computational costs while increasing </p><p dir="ltr">classification accuracy. …”

Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)

datasheet1_Graph Neural Networks for Maximum Constraint Satisfaction.pdf by Jan Tönshoff (10192709)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

Process flow diagram of CBFD. by Indhumathi S. (19173013)

Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

Experimental observation of gastruloids and shape quantification using LOCO-EFA. by Martijn A. de Jong (17896732)

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). …”

Classification baseline performance. by Doaa Sami Khafaga (21463870)

“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”

Feature selection results. by Doaa Sami Khafaga (21463870)

“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”

ANOVA test result. by Doaa Sami Khafaga (21463870)

“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”

Summary of literature review. by Doaa Sami Khafaga (21463870)

“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)