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A Survey on Machine Learning in Chemical Spectral Analysis

by Dongfang Yu, Jinwei Wang

School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Jinwei Wang. Email: email

Journal of Information Hiding and Privacy Protection 2020, 2(4), 165-174. https://doi.org/10.32604/jihpp.2020.010466

Abstract

Chemical spectral analysis is contemporarily undergoing a revolution and drawing much attention of scientists owing to machine learning algorithms, in particular convolutional networks. Hence, this paper outlines the major machine learning and especially deep learning methods contributed to interpret chemical images, and overviews the current application, development and breakthrough in different spectral characterization. Brief categorization of reviewed literatures is provided for studies per application apparatus: X-Ray spectra, UV-Vis-IR spectra, Micro-scope, Raman spectra, Photoluminescence spectrum. End with the overview of existing circumstances in this research area, we provide unique insight and promising directions for the chemical imaging field to fully couple machine learning subsequently.

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APA Style
Yu, D., Wang, J. (2020). A survey on machine learning in chemical spectral analysis. Journal of Information Hiding and Privacy Protection, 2(4), 165-174. https://doi.org/10.32604/jihpp.2020.010466
Vancouver Style
Yu D, Wang J. A survey on machine learning in chemical spectral analysis. J Inf Hiding Privacy Protection . 2020;2(4):165-174 https://doi.org/10.32604/jihpp.2020.010466
IEEE Style
D. Yu and J. Wang, “A Survey on Machine Learning in Chemical Spectral Analysis,” J. Inf. Hiding Privacy Protection , vol. 2, no. 4, pp. 165-174, 2020. https://doi.org/10.32604/jihpp.2020.010466



cc Copyright © 2020 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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