Proposed Algorithm. by Hend Bayoumi (22693738)

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

Comparison between Bisection and Newton-Raphson in terms of (A) total time consumed, and (B) maximum computation rate. by Hend Bayoumi (22693738)

Comparisons of computation rate performance for different offloading algorithms.for N = 10, 20, 30. by Hend Bayoumi (22693738)

Comparison of total time consumed for different offloading algorithms.for N = 10, 20, 30. by Hend Bayoumi (22693738)

The data processing and analysis pipeline. by Xin Zhang (35492)

The workflow diagram for the gene expression biomarker study which involve using principal component analysis (PCA) to reduce the number of features (principal components), thus re... by Aninda Astuti (21736334)

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

CDF of task latency, approximated as the inverse of the achieved computation rate. by Hend Bayoumi (22693738)

The evolution of the Wireless Power Transfer (WPT) time fraction β over simulation frames. by Hend Bayoumi (22693738)

An Example of a WPT-MEC Network. by Hend Bayoumi (22693738)

Related Work Summary. by Hend Bayoumi (22693738)

Simulation parameters. by Hend Bayoumi (22693738)

Training losses for N = 10. by Hend Bayoumi (22693738)

Normalized computation rate for N = 10. by Hend Bayoumi (22693738)

Summary of Notations Used in this paper. by Hend Bayoumi (22693738)

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

tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

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

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”