Sharmila Karumuri, Lori Graham-Brady and Somdatta Goswami. In this work, we introduce a novel random sampling technique for training DeepONet, designed to enhance model generalization and reduce ...

Compressed Sampling (CS) overcomes the oversam-pling trap caused by using Shannon's sampling theorem in sparse ultra-wideband signals. The Random Demodulator (RD) is the infrastructure for compressive ...

Inverse-Random-Under-Sampling Inverse Random Under Sampling ( IRUS) on Stroke data Imbalanced classifications pose a challenge for predictive modeling as most of the machine learning algorithms used ...

A statistically designed random sampling scheme, based on as few as 100 people, would give a very high probability of detecting if there are any COVID-19 cases and highlight at-risk hotspots.

In a recently published news story, we learn about a young doctor, Jake Deutsch, and his personal experience with coronavirus disease 2019. Deutsch tested positive for Covid-19 and checked himself ...

Systematic sampling is low risk, controllable and easy, but this statistical sampling method could lead to sampling errors and data manipulation.