Efficient QR code authentication mechanism based on Sudoku

Huang, Peng; Li, Yung‐Hui; Chang, Chin Chen; Liu, Yanjun

doi:10.1007/s11042-019-07795-8

Cited by 14 publications

(4 citation statements)

References 17 publications

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“…The main objective is to make the sum of our residuals close to 0 or minimum and predicted data close to actual data [29]. Denso-Wave, a Japanese company, developed the Quick Response (QR) code, a two-dimensional barcode or matrix code, in 1994 [30] [31]. It is developed to quickly encrypt or decrypt data which is encoding data in both horizontal and vertical directions.…”

Section: Support Vector Classifiermentioning

confidence: 99%

“…Due of their advantages in quick readability and high capacity, QR codes are widely used in daily lives as a means of transmitting information [31] [33]. They can be used for tracking automotive parts in factories, navigation, advertising, mobile marketing, mobile payments, electronic ticket/coupon, identification, academics, mobile devices, information security, access control, OMR sheet tampering detection [32] [34].…”

Section: Support Vector Classifiermentioning

confidence: 99%

“…The QR structure (version 2) with level of error correction (L) is shown in Figure10. each QR code includes functional patterns like alignment patterns, position detection patterns, timing patterns, version patterns, and error correction codewords[31].…”

mentioning

confidence: 99%

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An Analytical Predictive Models and Secure Web-Based Personalized Diabetes Monitoring System

2022

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Diabetes, in all of its types, costs countries of all income levels unacceptably enormous personal, societal, and economic expenses. To predict type-2 diabetes, this work aimed to develop an analytical predictive model based on machine learning techniques and a web-based personalized diabetes monitoring system. The history of a patient will be collected and ready for analysis purposes based on machine learning techniques by continuously monitoring the patient's vital data. A diabetes monitoring system is proposed by utilizing the patient's QR card that allows the patients and doctors to be connected to the Internet of Things. So, they can deliver real-time information (such as insulin records) about their health status and can visit different healthcare institutions. The proposed system can help doctors to make data-driven decisions and enhance patients' treatment. Several machine learning algorithms that are Decision Tree, Support Vector Classifier, Random Forest, Gradient Boosting, Multi-layer Perceptron's, Artificial Neural Network, k-Nearest Neighbors, Logistic Regression, and Naive Bayes are used. The proposed analytical model is evaluated based on two different datasets a synthetic dataset and PIMA Diabetes Dataset. The performance of the classification models was analyzed in terms of accuracy, recall, and precision based on the cross-validation strategy. The findings are found that the ANN model has better prediction accuracy than other models. The evaluation findings are analyzed and compared with other existing models. The system has dashboard graphs displaying the number of patients in Saudi Arabia cities. It also contains visualized graphs that include more detailed classifications for patients' states (Normal, Pre-Diabetes, and Diabetes).

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Section: Support Vector Classifiermentioning

confidence: 99%

Section: Support Vector Classifiermentioning

confidence: 99%

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An Analytical Predictive Models and Secure Web-Based Personalized Diabetes Monitoring System

2022

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“…As an essential entrance to the Internet of Things, two-dimensional (2D) barcodes are more deeply connected to people's lives, because of low cost, convenient portability and sharing, and high recognition rate [8]. In financial payment [9,10], anti-counterfeiting authentication [11,12], electronic bills [13], and other smart IoT applications [14][15][16] are becoming more widely used. Meanwhile, anti-counterfeiting methods based on the application of 2D barcode sensing technology have become a research hotspot to solve the above problems.…”

Section: Introductionmentioning

confidence: 99%

Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding

Chen

et al. 2020

Sensors

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With the development of commodity economy, the emergence of fake and shoddy products has seriously harmed the interests of consumers and enterprises. To tackle this challenge, customized 2D barcode is proposed to satisfy the requirements of the enterprise anti-counterfeiting certification. Based on information hiding technology, the proposed approach can solve these challenging problems and provide a low-cost, difficult to forge, and easy to identify solution, while achieving the function of conventional 2D barcodes. By weighting between the perceptual quality and decoding robustness in sensing recognition, the customized 2D barcode can maintain a better aesthetic appearance for anti-counterfeiting and achieve fast encoding. A new picture-embedding scheme was designed to consider 2D barcode, within a unit image block as a basic encoding unit, where the 2D barcode finder patterns were embedded after encoding. Experimental results demonstrated that the proposed customized barcode could provide better encoding characteristics, while maintaining better decoding robustness than several state-of-the-art methods. Additionally, as a closed source 2D barcode that could be visually anti-counterfeit, the customized 2D barcode could effectively prevent counterfeiting that replicate physical labels. Benefitting from the high-security, high information capacity, and low-cost, the proposed customized 2D barcode with sensing recognition scheme provide a highly practical, valuable in terms of marketing, and anti-counterfeiting traceable solution for future smart IoT applications.

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Enhancing the Cryptography Security of Message Communication by Modified Secure IDEA Algorithm

Haldar,

Sinha,

Paul

et al. 2024

Proceedings of 4th International Conference on Frontiers in Computing and Systems

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Efficient QR code authentication mechanism based on Sudoku

Cited by 14 publications

References 17 publications

An Analytical Predictive Models and Secure Web-Based Personalized Diabetes Monitoring System

An Analytical Predictive Models and Secure Web-Based Personalized Diabetes Monitoring System

Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding

Enhancing the Cryptography Security of Message Communication by Modified Secure IDEA Algorithm

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