The Identification Nanoparticle Sensor Using Back Propagation Neural Network Optimized by Genetic Algorithm

This study draws attention towards the application of identification nanoparticle (NPs) sensor based on back propagation (BP) neural network optimized by genetic algorithm (GA) in the early diagnosis of cancer cells. In this study, the traditional and optimized BP neural networks are compared in ter...

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Autores principales:	Yiwen Hu, Ashutosh Sharma, Gaurav Dhiman, Mohammad Shabaz
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/46cb018369124377b77eb7a76cca8800
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spelling	oai:doaj.org-article:46cb018369124377b77eb7a76cca88002021-11-29T00:56:06ZThe Identification Nanoparticle Sensor Using Back Propagation Neural Network Optimized by Genetic Algorithm1687-726810.1155/2021/7548329https://doaj.org/article/46cb018369124377b77eb7a76cca88002021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7548329https://doaj.org/toc/1687-7268This study draws attention towards the application of identification nanoparticle (NPs) sensor based on back propagation (BP) neural network optimized by genetic algorithm (GA) in the early diagnosis of cancer cells. In this study, the traditional and optimized BP neural networks are compared in terms of error between the actual value and the predictive value, and they are further applied to the NP sensor for early diagnosis of cancer cells. The results show that the root mean square (RMS) and mean absolute error (MAE) of the optimized BP neural network are comparatively much smaller than the traditional ones. The particle size of silicon-coated fluorescent NPs is about 105 nm, and the relative fluorescence intensity of silicon-coated fluorescent NPs decreases slightly, maintaining the accuracy value above 80%. In the fluorescence imaging, it is found that there is obvious green fluorescence on the surface of the cancer cells, and the cancer cells still emit bright green fluorescence under the dark-field conditions. In this study, a phenolic resin polymer CMK-2 with a large surface area is successfully combined with Au. NPs with good dielectric property and bioaffinity are selectively bonded to the modified electrode through a sulfur-gold bond to prepare NP sensor. The sensor shows good stability, selectivity, and anti-interference property, providing a new method for the detection of early cancer cells.Yiwen HuAshutosh SharmaGaurav DhimanMohammad ShabazHindawi LimitedarticleTechnology (General)T1-995ENJournal of Sensors, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995
spellingShingle	Technology (General) T1-995 Yiwen Hu Ashutosh Sharma Gaurav Dhiman Mohammad Shabaz The Identification Nanoparticle Sensor Using Back Propagation Neural Network Optimized by Genetic Algorithm
description	This study draws attention towards the application of identification nanoparticle (NPs) sensor based on back propagation (BP) neural network optimized by genetic algorithm (GA) in the early diagnosis of cancer cells. In this study, the traditional and optimized BP neural networks are compared in terms of error between the actual value and the predictive value, and they are further applied to the NP sensor for early diagnosis of cancer cells. The results show that the root mean square (RMS) and mean absolute error (MAE) of the optimized BP neural network are comparatively much smaller than the traditional ones. The particle size of silicon-coated fluorescent NPs is about 105 nm, and the relative fluorescence intensity of silicon-coated fluorescent NPs decreases slightly, maintaining the accuracy value above 80%. In the fluorescence imaging, it is found that there is obvious green fluorescence on the surface of the cancer cells, and the cancer cells still emit bright green fluorescence under the dark-field conditions. In this study, a phenolic resin polymer CMK-2 with a large surface area is successfully combined with Au. NPs with good dielectric property and bioaffinity are selectively bonded to the modified electrode through a sulfur-gold bond to prepare NP sensor. The sensor shows good stability, selectivity, and anti-interference property, providing a new method for the detection of early cancer cells.
format	article
author	Yiwen Hu Ashutosh Sharma Gaurav Dhiman Mohammad Shabaz
author_facet	Yiwen Hu Ashutosh Sharma Gaurav Dhiman Mohammad Shabaz
author_sort	Yiwen Hu
title	The Identification Nanoparticle Sensor Using Back Propagation Neural Network Optimized by Genetic Algorithm
title_short	The Identification Nanoparticle Sensor Using Back Propagation Neural Network Optimized by Genetic Algorithm
title_full	The Identification Nanoparticle Sensor Using Back Propagation Neural Network Optimized by Genetic Algorithm
title_fullStr	The Identification Nanoparticle Sensor Using Back Propagation Neural Network Optimized by Genetic Algorithm
title_full_unstemmed	The Identification Nanoparticle Sensor Using Back Propagation Neural Network Optimized by Genetic Algorithm
title_sort	identification nanoparticle sensor using back propagation neural network optimized by genetic algorithm
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/46cb018369124377b77eb7a76cca8800
work_keys_str_mv	AT yiwenhu theidentificationnanoparticlesensorusingbackpropagationneuralnetworkoptimizedbygeneticalgorithm AT ashutoshsharma theidentificationnanoparticlesensorusingbackpropagationneuralnetworkoptimizedbygeneticalgorithm AT gauravdhiman theidentificationnanoparticlesensorusingbackpropagationneuralnetworkoptimizedbygeneticalgorithm AT mohammadshabaz theidentificationnanoparticlesensorusingbackpropagationneuralnetworkoptimizedbygeneticalgorithm AT yiwenhu identificationnanoparticlesensorusingbackpropagationneuralnetworkoptimizedbygeneticalgorithm AT ashutoshsharma identificationnanoparticlesensorusingbackpropagationneuralnetworkoptimizedbygeneticalgorithm AT gauravdhiman identificationnanoparticlesensorusingbackpropagationneuralnetworkoptimizedbygeneticalgorithm AT mohammadshabaz identificationnanoparticlesensorusingbackpropagationneuralnetworkoptimizedbygeneticalgorithm
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The Identification Nanoparticle Sensor Using Back Propagation Neural Network Optimized by Genetic Algorithm

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