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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Machine and deep learning algorithms have increasingly been applied to solveproblems in various areas of knowledge. Among these areas, Chemometrics hasbeen benefited from the application of these algorithms in spectral dataanalysis. Commonly, algorithms such as Support Vector Machines and PartialLeast Squares are applied to spectral datasets to perform classification andregression tasks. In this paper, we present a 1D convolutional neural networks(1D-CNN) to evaluate the effectiveness on spectral data obtained fromspectroscopy. In most cases, the spectrum signals are noisy and present overlapamong classes. Firstly, we perform extensive experiments including 1D-CNNcompared to machine learning algorithms and standard algorithms used inChemometrics on spectral data classification for the most known datasetsavailable in the literature. Next, spectral samples of the SARS-COV2 virus,which causes the COVID-19, have recently been collected via spectroscopy wasused as a case study. Experimental results indicate superior performance of1D-CNN over machine learning algorithms and standard algorithms, obtaining anaverage accuracy of 96.5%, specificity of 98%, and sensitivity of 94%. Thepromissing obtained results indicate the feasibility to use 1D-CNN in automatedsystems to diagnose COVID-19 and other viral diseases in the future.

1D Convolutional neural networks and machine learning algorithms for spectral data classification with a case study for Covid-19