QR codes are widely used in applications such as information sharing,advertising,and digital payments.However,their growing adoption has made them attractive targets for malicious activities,including malware distribu...QR codes are widely used in applications such as information sharing,advertising,and digital payments.However,their growing adoption has made them attractive targets for malicious activities,including malware distribution and phishing attacks.Traditional detection approaches rely on URL analysis or image-based feature extraction,whichmay introduce significant computational overhead and limit real-time applicability,and their performance often depends on the quality of extracted features.Previous studies in malicious detection do not fully focus on QR code securitywhen combining convolutional neural networks(CNNs)with recurrent neural networks(RNNs).This research proposes a deep learning model that integrates AlexNet for feature extraction,principal component analysis(PCA)for dimensionality reduction,and RNNs to detect malicious activity in QR code images.The proposed model achieves both efficiency and accuracy by transforming image data into a compact one-dimensional sequence.Experimental results,including five-fold cross-validation,demonstrate that the model using gated recurrent units(GRU)achieved an accuracy of 99.81%on the first dataset and 99.59%in the second dataset with a computation time of only 7.433 ms per sample.A real-time prototype was also developed to demonstrate deployment feasibility.These results highlight the potential of the proposed approach for practical,real-time QR code threat detection.展开更多
Objective: to study and try to apply Internet medical technology to clinical nursing work, so as to improve clinical nursing service. Methods: a total of 100 patients in Department of general surgery and Department of...Objective: to study and try to apply Internet medical technology to clinical nursing work, so as to improve clinical nursing service. Methods: a total of 100 patients in Department of general surgery and Department of orthopedics were collected. The data of temperature, blood pressure, pulse and respiration were collected and uploaded to the cloud database. Make the website QR code as the only identification mark of the patient. The QR code corresponds to the home page of the patient's vital signs browsing, and automatically refresh the home page to search the real-time vital signs data in the cloud. The QR code is posted on the bedside card for scanning or forwarding. Then investigate the satisfaction of family members and medical staff. Results: to realize the integration of Internet technology and clinical nursing service, and make the patient's condition information public to the family members, so as to explore a new model of clinical nursing based on Internet technology.展开更多
为解决一维条码使用依靠数据库、受到极大限制的问题 ,设计的 QR Code码作为一种矩阵式二维码 ,具有超高速识读、全方位识读 ,能够有效地表示中国汉字、日本汉字等其他二维码所没有的特点。 QR Code用于印刷地图上同一位置的直接输入、...为解决一维条码使用依靠数据库、受到极大限制的问题 ,设计的 QR Code码作为一种矩阵式二维码 ,具有超高速识读、全方位识读 ,能够有效地表示中国汉字、日本汉字等其他二维码所没有的特点。 QR Code用于印刷地图上同一位置的直接输入、检索系统 ,有很强的使用价值。展开更多
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University Jeddah,under grant no.(GPIP:1168-611-2024)The authors acknowledge the DSR for financial and technical support.
文摘QR codes are widely used in applications such as information sharing,advertising,and digital payments.However,their growing adoption has made them attractive targets for malicious activities,including malware distribution and phishing attacks.Traditional detection approaches rely on URL analysis or image-based feature extraction,whichmay introduce significant computational overhead and limit real-time applicability,and their performance often depends on the quality of extracted features.Previous studies in malicious detection do not fully focus on QR code securitywhen combining convolutional neural networks(CNNs)with recurrent neural networks(RNNs).This research proposes a deep learning model that integrates AlexNet for feature extraction,principal component analysis(PCA)for dimensionality reduction,and RNNs to detect malicious activity in QR code images.The proposed model achieves both efficiency and accuracy by transforming image data into a compact one-dimensional sequence.Experimental results,including five-fold cross-validation,demonstrate that the model using gated recurrent units(GRU)achieved an accuracy of 99.81%on the first dataset and 99.59%in the second dataset with a computation time of only 7.433 ms per sample.A real-time prototype was also developed to demonstrate deployment feasibility.These results highlight the potential of the proposed approach for practical,real-time QR code threat detection.
文摘Objective: to study and try to apply Internet medical technology to clinical nursing work, so as to improve clinical nursing service. Methods: a total of 100 patients in Department of general surgery and Department of orthopedics were collected. The data of temperature, blood pressure, pulse and respiration were collected and uploaded to the cloud database. Make the website QR code as the only identification mark of the patient. The QR code corresponds to the home page of the patient's vital signs browsing, and automatically refresh the home page to search the real-time vital signs data in the cloud. The QR code is posted on the bedside card for scanning or forwarding. Then investigate the satisfaction of family members and medical staff. Results: to realize the integration of Internet technology and clinical nursing service, and make the patient's condition information public to the family members, so as to explore a new model of clinical nursing based on Internet technology.
