English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Neural network training entails heavy computation with obvious bottlenecks.The Compute Unified Device Architecture (CUDA) programming model allows us toaccelerate computation by passing the processing workload from the CPU to thegraphics processing unit (GPU). In this paper, we leveraged the power of NvidiaGPUs to parallelize all of the computation involved in training, to acceleratea backpropagation feed-forward neural network with one hidden layer using CUDAand C++. This optimized neural network was tasked with predicting the level ofglycated hemoglobin (HbA1c) from non-invasive markers. The rate of increase inthe prevalence of Diabetes Mellitus has resulted in an urgent need for earlydetection and accurate diagnosis. However, due to the invasiveness andlimitations of conventional tests, alternate means are being considered.Limited Joint Mobility (LJM) has been reported as an indicator for poorglycemic control. LJM of the fingers is quantified and its link to HbA1c isinvestigated along with other potential non-invasive markers of HbA1c. Wecollected readings of 33 potential markers from 120 participants at a clinic insouth Trinidad. Our neural network achieved 95.65% accuracy on the training and86.67% accuracy on the testing set for male participants and 97.73% and 66.67%accuracy on the training and testing sets for female participants. Using 960CUDA cores from a Nvidia GeForce GTX 660, our parallelized neural network wastrained 50 times faster on both subsets, than its corresponding CPUimplementation on an Intel Core (TM) i7-3630QM 2.40 GHz CPU.

CUDA optimized Neural Network predicts blood glucose control from quantified joint mobility and anthropometrics