dc.contributor.author |
Rasheed, J
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dc.contributor.author |
Wardak, AB
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dc.contributor.author |
Abu-Mahfouz, Adnan MI
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dc.contributor.author |
Umer, T
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dc.contributor.author |
Yesiltepe, M
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dc.contributor.author |
Waziry, S
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dc.date.accessioned |
2023-02-26T20:31:11Z |
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dc.date.available |
2023-02-26T20:31:11Z |
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dc.date.issued |
2022-10 |
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dc.identifier.citation |
Rasheed, J., Wardak, A., Abu-Mahfouz, A.M., Umer, T., Yesiltepe, M. & Waziry, S. 2022. An efficient machine learning-based model to effectively classify the type of noises in QR code: A hybrid approach. <i>Symmetry, 14(10).</i> http://hdl.handle.net/10204/12637 |
en_ZA |
dc.identifier.issn |
2073-8994 |
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dc.identifier.uri |
https://doi.org/10.3390/sym14102098
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dc.identifier.uri |
http://hdl.handle.net/10204/12637
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dc.description.abstract |
Granting smart device consumers with information, simply and quickly, is what drives quick response (QR) codes and mobile marketing to go hand in hand. It boosts marketing campaigns and objectives and allows one to approach, engage, influence, and transform a wider target audience by connecting from offline to online platforms. However, restricted printing technology and flexibility in surfaces introduce noise while printing QR code images. Moreover, noise is often unavoidable during the gathering and transmission of digital images. Therefore, this paper proposed an automatic and accurate noise detector to identify the type of noise present in QR code images. For this, the paper first generates a new dataset comprising 10,000 original QR code images of varying sizes and later introduces several noises, including salt and pepper, pepper, speckle, Poisson, salt, local var, and Gaussian to form a dataset of 80,000 images. We perform extensive experiments by reshaping the generated images to uniform size for exploiting Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Logistic Regression (LG) to classify the original and noisy images. Later, the analysis is further widened by incorporating histogram density analysis to trace and target highly important features by transforming images of varying sizes to obtain 256 features, followed by SVM, LG, and Artificial Neural Network (ANN) to identify the noise type. Moreover, to understand the impact of symmetry of noises in QR code images, we trained the models with combinations of 3-, 5-, and 7-noise types and analyzed the classification performance. From comparative analyses, it is noted that the Gaussian and Localvar noises possess symmetrical characteristics, as all the classifiers did not perform well to segregate these two noises. The results prove that histogram analysis significantly improves classification accuracy with all exploited models, especially when combined with SVM, it achieved maximum accuracy for 4- and 6-class classification problems. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.mdpi.com/2073-8994/14/10/2098 |
en_US |
dc.source |
Symmetry, 14(10) |
en_US |
dc.subject |
Noisy images |
en_US |
dc.subject |
Noise classification |
en_US |
dc.subject |
Histogram analysis |
en_US |
dc.subject |
Convolutional neural network |
en_US |
dc.subject |
CNN |
en_US |
dc.subject |
Support vector machine |
en_US |
dc.subject |
SVM |
en_US |
dc.subject |
Logistic regression |
en_US |
dc.subject |
LG |
en_US |
dc.title |
An efficient machine learning-based model to effectively classify the type of noises in QR code: A hybrid approach |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
21 |
en_US |
dc.description.note |
Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Rasheed, J., Wardak, A., Abu-Mahfouz, A. M., Umer, T., Yesiltepe, M., & Waziry, S. (2022). An efficient machine learning-based model to effectively classify the type of noises in QR code: A hybrid approach. <i>Symmetry, 14(10)</i>, http://hdl.handle.net/10204/12637 |
en_ZA |
dc.identifier.chicagocitation |
Rasheed, J, AB Wardak, Adnan MI Abu-Mahfouz, T Umer, M Yesiltepe, and S Waziry "An efficient machine learning-based model to effectively classify the type of noises in QR code: A hybrid approach." <i>Symmetry, 14(10)</i> (2022) http://hdl.handle.net/10204/12637 |
en_ZA |
dc.identifier.vancouvercitation |
Rasheed J, Wardak A, Abu-Mahfouz AM, Umer T, Yesiltepe M, Waziry S. An efficient machine learning-based model to effectively classify the type of noises in QR code: A hybrid approach. Symmetry, 14(10). 2022; http://hdl.handle.net/10204/12637. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Rasheed, J
AU - Wardak, AB
AU - Abu-Mahfouz, Adnan MI
AU - Umer, T
AU - Yesiltepe, M
AU - Waziry, S
AB - Granting smart device consumers with information, simply and quickly, is what drives quick response (QR) codes and mobile marketing to go hand in hand. It boosts marketing campaigns and objectives and allows one to approach, engage, influence, and transform a wider target audience by connecting from offline to online platforms. However, restricted printing technology and flexibility in surfaces introduce noise while printing QR code images. Moreover, noise is often unavoidable during the gathering and transmission of digital images. Therefore, this paper proposed an automatic and accurate noise detector to identify the type of noise present in QR code images. For this, the paper first generates a new dataset comprising 10,000 original QR code images of varying sizes and later introduces several noises, including salt and pepper, pepper, speckle, Poisson, salt, local var, and Gaussian to form a dataset of 80,000 images. We perform extensive experiments by reshaping the generated images to uniform size for exploiting Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Logistic Regression (LG) to classify the original and noisy images. Later, the analysis is further widened by incorporating histogram density analysis to trace and target highly important features by transforming images of varying sizes to obtain 256 features, followed by SVM, LG, and Artificial Neural Network (ANN) to identify the noise type. Moreover, to understand the impact of symmetry of noises in QR code images, we trained the models with combinations of 3-, 5-, and 7-noise types and analyzed the classification performance. From comparative analyses, it is noted that the Gaussian and Localvar noises possess symmetrical characteristics, as all the classifiers did not perform well to segregate these two noises. The results prove that histogram analysis significantly improves classification accuracy with all exploited models, especially when combined with SVM, it achieved maximum accuracy for 4- and 6-class classification problems.
DA - 2022-10
DB - ResearchSpace
DP - CSIR
J1 - Symmetry, 14(10)
KW - Noisy images
KW - Noise classification
KW - Histogram analysis
KW - Convolutional neural network
KW - CNN
KW - Support vector machine
KW - SVM
KW - Logistic regression
KW - LG
LK - https://researchspace.csir.co.za
PY - 2022
SM - 2073-8994
T1 - An efficient machine learning-based model to effectively classify the type of noises in QR code: A hybrid approach
TI - An efficient machine learning-based model to effectively classify the type of noises in QR code: A hybrid approach
UR - http://hdl.handle.net/10204/12637
ER -
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en_ZA |
dc.identifier.worklist |
26507 |
en_US |