Rasheed, JWardak, ABAbu-Mahfouz, Adnan MIUmer, TYesiltepe, MWaziry, S2023-02-262023-02-262022-10Rasheed, 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/10204/126372073-8994https://doi.org/10.3390/sym14102098http://hdl.handle.net/10204/12637Granting 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.FulltextenNoisy imagesNoise classificationHistogram analysisConvolutional neural networkCNNSupport vector machineSVMLogistic regressionLGArticleRasheed, 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/10204/12637Rasheed, 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/10204/12637Rasheed 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.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 -26507