In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intellige...In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligentstanding human detection (ISHD) method based on an improved single shot multibox detector to detect thetarget of standing human posture in the scene frame of exam room video surveillance at a specific examinationstage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posturefeature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the trainingstrategy, which greatly reduces the computation amount, improves the detection accuracy, and reduces the trainingdifficulty. The experiment proves that the model proposed in this paper has a better detection ability for the smalland medium-sized standing human body posture in video test scenes on the EMV-2 dataset.展开更多
Introduction: MRI is a rapidly growing technique with more and more indica-tions and requests in the Republic of Guinea. Its correct prescription is a guar-antee for the satisfaction of the actors, both prescribers, r...Introduction: MRI is a rapidly growing technique with more and more indica-tions and requests in the Republic of Guinea. Its correct prescription is a guar-antee for the satisfaction of the actors, both prescribers, radiologists and pa-tients. The main objective of this study was to evaluate the compliance of MRI examination requests at the Diagnostic Center of the National Social Se-curity Fund (CNSS) in Conakry. Material and Methods: This was a descriptive cross-sectional study of MRI prescription forms sent to the MRI unit of the CNSS Diagnostic Center from February 1 to May 1, 2021. The 8 compliance criteria established by the French High Authority for Health were used to evaluate the compliance of the examination requests. Results: A total of 7003 examination forms were sent to the facility, including 7% (n = 468) of MRIs. 56.2% of MRI requests were performed by specialists. We observed an overall compliance of 10%. Administrative and clinical compliance were missing in 24% and 38%, respectively. More specifically, the purpose of the examination was not mentioned in 60%, followed by the requesting department in 48.1% and the patient’s age in 35.1%. Conclusion: This study allowed us to highlight the gaps in establishing MRI requests. It would be important to organize an awareness campaign for prescribers on the usefulness of correctly filling an MRI request and to design templates to be filled out by prescribers.展开更多
The issue related to the risk of identity impersonation, where one person can be replaced by another in online exam surveillance systems, poses challenges. This study focuses on the effectiveness of detecting attempts...The issue related to the risk of identity impersonation, where one person can be replaced by another in online exam surveillance systems, poses challenges. This study focuses on the effectiveness of detecting attempts of identity impersonation through face substitution during online exams, with the aim of ensuring the integrity of assessments. The goal is to develop facial recognition algorithms capable of precisely detecting these impersonations, training them on a tailored database rather than biased generic data. An original database of student faces has been created. An algorithm leveraging advanced deep learning techniques such as depthwise separable convolution has been developed and evaluated on this database. We achieved very high levels of precision, reaching an accuracy rate of 98% in face detection and recognition.展开更多
基金supported by the Natural Science Foundation of China 62102147National Science Foundation of Hunan Province 2022JJ30424,2022JJ50253,and 2022JJ30275+2 种基金Scientific Research Project of Hunan Provincial Department of Education 21B0616 and 21B0738Hunan University of Arts and Sciences Ph.D.Start-Up Project BSQD02,20BSQD13the Construct Program of Applied Characteristic Discipline in Hunan University of Science and Engineering.
文摘In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligentstanding human detection (ISHD) method based on an improved single shot multibox detector to detect thetarget of standing human posture in the scene frame of exam room video surveillance at a specific examinationstage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posturefeature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the trainingstrategy, which greatly reduces the computation amount, improves the detection accuracy, and reduces the trainingdifficulty. The experiment proves that the model proposed in this paper has a better detection ability for the smalland medium-sized standing human body posture in video test scenes on the EMV-2 dataset.
文摘Introduction: MRI is a rapidly growing technique with more and more indica-tions and requests in the Republic of Guinea. Its correct prescription is a guar-antee for the satisfaction of the actors, both prescribers, radiologists and pa-tients. The main objective of this study was to evaluate the compliance of MRI examination requests at the Diagnostic Center of the National Social Se-curity Fund (CNSS) in Conakry. Material and Methods: This was a descriptive cross-sectional study of MRI prescription forms sent to the MRI unit of the CNSS Diagnostic Center from February 1 to May 1, 2021. The 8 compliance criteria established by the French High Authority for Health were used to evaluate the compliance of the examination requests. Results: A total of 7003 examination forms were sent to the facility, including 7% (n = 468) of MRIs. 56.2% of MRI requests were performed by specialists. We observed an overall compliance of 10%. Administrative and clinical compliance were missing in 24% and 38%, respectively. More specifically, the purpose of the examination was not mentioned in 60%, followed by the requesting department in 48.1% and the patient’s age in 35.1%. Conclusion: This study allowed us to highlight the gaps in establishing MRI requests. It would be important to organize an awareness campaign for prescribers on the usefulness of correctly filling an MRI request and to design templates to be filled out by prescribers.
文摘The issue related to the risk of identity impersonation, where one person can be replaced by another in online exam surveillance systems, poses challenges. This study focuses on the effectiveness of detecting attempts of identity impersonation through face substitution during online exams, with the aim of ensuring the integrity of assessments. The goal is to develop facial recognition algorithms capable of precisely detecting these impersonations, training them on a tailored database rather than biased generic data. An original database of student faces has been created. An algorithm leveraging advanced deep learning techniques such as depthwise separable convolution has been developed and evaluated on this database. We achieved very high levels of precision, reaching an accuracy rate of 98% in face detection and recognition.
