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Prediction model for spectroscopy using Python programming

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Main Authors: A. A. M., Ismail, N., Ali, M. S., Amirul, R., Endut, S. A., Aljunid
Other Authors: norshamsuri@unimap.edu.my
Format: Article
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2022
Subjects:
Artificial neural network
Prediction model
Python
Spectroscopy
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/75208
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spelling my.unimap-752082022-05-11T04:04:03Z Prediction model for spectroscopy using Python programming A. A. M., Ismail N., Ali M. S., Amirul R., Endut S. A., Aljunid norshamsuri@unimap.edu.my Artificial neural network Prediction model Python Spectroscopy Link to publisher's homepage at http://ijneam.unimap.edu.my This paper is motivated by searching for the perfect pattern for the spectroscopy spectra using artificial neural networks (ANN) using python programming coding. The pattern from the spectroscopy is based on the absorption and emission of light and other radiation by materials in relation to the wavelength dependence of these processes. Spectral equipment such as spectrometers, spectral analysers, spectrographs, or spectrophotometers is utilised to determine spectrum values. The problem in this spectroscopy is to identify the sample or analyte, which can be solved by a prediction model for spectroscopy using Python. These problems occur when finding the best algorithm of pre-processing techniques that can predict any model accurately into an understandable format for prediction models. Various types of pre-processing techniques have been used, such as Multiplicative Scatter Correction (MSC), Inverse MSC, Extended MSC (EMSC), Extended Inverse MSC, de-trending, Standard Normal Variate (SNV) and normalisation in order to get a better r2 value. In this project, we find the r2 and the root mean square error (RMSE) to evaluate the prediction values and the actual values. First, choosing pre-processing techniques and then finding the best statistical method for constructing predictive models that produce high accuracy. We used ANN in this project as a prediction model. Based on the results, we managed to achieve our objective, which is that the prediction model has more than 90% of accuracy. Furthermore, the results show that our prediction model has 1.0 accuracy at 100 Epoch with a 0.3 learning rate. Finally, we can conclude that our prediction model can be used to predict the spectroscopy-based data format. 2022-05-11T04:04:03Z 2022-05-11T04:04:03Z 2021-12 Article International Journal of Nanoelectronics and Materials, vol.14 (Special Issue), 2021, pages 353-363 1985-5761 (Printed) 1997-4434 (Online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/75208 http://ijneam.unimap.edu.my en Universiti Malaysia Perlis (UniMAP)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Artificial neural network
Prediction model
Python
Spectroscopy
spellingShingle Artificial neural network
Prediction model
Python
Spectroscopy
A. A. M., Ismail
N., Ali
M. S., Amirul
R., Endut
S. A., Aljunid
Prediction model for spectroscopy using Python programming
description Link to publisher's homepage at http://ijneam.unimap.edu.my
author2 norshamsuri@unimap.edu.my
author_facet norshamsuri@unimap.edu.my
A. A. M., Ismail
N., Ali
M. S., Amirul
R., Endut
S. A., Aljunid
format Article
author A. A. M., Ismail
N., Ali
M. S., Amirul
R., Endut
S. A., Aljunid
author_sort A. A. M., Ismail
title Prediction model for spectroscopy using Python programming
title_short Prediction model for spectroscopy using Python programming
title_full Prediction model for spectroscopy using Python programming
title_fullStr Prediction model for spectroscopy using Python programming
title_full_unstemmed Prediction model for spectroscopy using Python programming
title_sort prediction model for spectroscopy using python programming
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2022
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/75208
_version_ 1738511719805747200
score 13.222552

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