Machine learning deployment strategies and schematic illustration of the proposed generative adversarial algorithm for domain adaptation. حسب Aly A. Valliani (13251484)

"…<p><b>(A)</b> There are four primary methods by which machine learning models can be deployed in a context with distinct data domains: 1) train a model on one domain and deploy it across multiple distinct domains, 2) train multiple bespoke models that are optimized for deployment on individual domains, 3) train and deploy a single global model on all domains, and 4) train a model on one domain and adapt it through technical means to make it performant on a distinct domain. …"

Multiple sequence alignment plot generated by Unipro UGENE [32]. حسب Rui Wang (52434)

Pipeline for synthetic population generation. حسب Philip Cherian (11172138)

Source datasets for generating the synthetic population. حسب Philip Cherian (11172138)

Computational complexity of various algorithms in the pipeline. حسب Ali M. S. Alfosool (12936678)

Root mean square fluctuation (RMSF) of the AF-generated conformations and the MD-generated ensembles. حسب Samuel Eriksson Lidbrink (21616146)

Bash script for downloading records using Feature Generator API. حسب Paul J. Albert (10515299)

Optimizer response against three convex machines. حسب Ijaz Ahmed (1545961)

Optimizer response against three non-convex machine. حسب Ijaz Ahmed (1545961)

image1_Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study.jpeg حسب Cyril Esnault (9182435)

image2_Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study.jpeg حسب Cyril Esnault (9182435)

datasheet1_Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study.pdf حسب Cyril Esnault (9182435)

image1_Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study.jpeg حسب Cyril Esnault (9182435)

datasheet1_Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study.pdf حسب Cyril Esnault (9182435)

image2_Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study.jpeg حسب Cyril Esnault (9182435)

Results of the machine learning models, trained on real or synthetic datasets. حسب Anmol Arora (9674334)

Optimizer power output response against twelve convex machines. حسب Ijaz Ahmed (1545961)

Optimizer fitness level response against twelve convex machine. حسب Ijaz Ahmed (1545961)

Optimizer power output response against six convex machines. حسب Ijaz Ahmed (1545961)

Optimizer power output response against twenty convex machines. حسب Ijaz Ahmed (1545961)