Experimental characterization of an X-ray photon counting detector

Wang, X.; Wei, Tingcha; Deng, Zhi

doi:10.1088/1748-0221/17/07/c07005

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Lixiang and his team members [12] suggested that CVS during the SARS-CoV-2 Outbreak in University Students: A Comparison between Online Courses and Classroom Lectures. The main aim of the paper is to compare the wide spread of CVS among students taking online classes and Chinese students going to school after lockdown relaxation.…”

Section: Literature Surveymentioning

confidence: 99%

Predicting Smartphone Vision Syndrome: A Feasible Approach using Machine Learning Algorithms

Annapurna,

Rajeswari,

Likitha

et al. 2023

E3S Web Conf.

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Smartphone Vision Syndrome (SVS) is an evitable problem for people who spend a great deal of time watching digital screens. It is a major concern for rapid growth in technology where the burden is significantly greater due to factors such as limited access to and use of personal protective equipment, as well as lesser break time. The objective of the model is to achieve a feasible and higher level of eye health for people who are working long hours with digital screens. The dataset is obtained through an online survey form containing metrics that contribute to the occurrence of SVS. After applying Machine Learning algorithms, namely Logistic Regression, Random Forest Classifier, Naïve Bayes and Support Vector Machine (SVM), the model’s overall performance is assessed using the test sample. Accuracies obtained by Random Forest, Support Vector Machine, Logistic Regression, Naïve Bayes, and Gaussian Naïve Bayes are 98.75%, 97.5%, 77.5%, 95% and 96.25%.

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Section: Literature Surveymentioning

confidence: 99%

Predicting Smartphone Vision Syndrome: A Feasible Approach using Machine Learning Algorithms

Annapurna,

Rajeswari,

Likitha

et al. 2023

E3S Web Conf.

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“…It is possible to have greater precision. Kumar [12] adopted methodology used in this model is GPS location. Students' whereabouts are determined using GPS on their phones.…”

Section: Literature Surveymentioning

confidence: 99%

Smart Attendance Automation System

Shamila,

Prakash,

Ahmed

et al. 2023

E3S Web Conf.

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The smart attendance marking system, which is based on face recognition technology, is a modern solution that may aid in the automation of the attendance management process in a variety of organizations, including schools, colleges, and corporations. Using facial recognition technology, the research team hopes to create an efficient system for tracking and recording attendance. The system will be developed by gathering and analysing data in order to construct a database of faces that will be used to train deep learning algorithms to recognise faces effectively. Compared to traditional attendance tracking systems, the smart attendance marking system based on face recognition technology has various advantages. For starters, it saves time and lowers manual labour because attendance is automatically tracked. Second, it enhances precision, lowering the possibility of errors. Third, it delivers real-time attendance updates, making it simple for instructors or administration to monitor attendance and take appropriate action. The aim of the work is expected to improve attendance management in various organisations by optimising attendance monitoring methods while decreasing manual effort and errors. Because the system may be connected with existing attendance management systems or utilised as a standalone solution, it is a versatile and adaptable option for a variety of organisations. The paper’s effort is to design, develop, and implement an efficient system that can track and record attendance accurately using facial recognition technology, giving benefits such as time savings, accuracy, real-time updates, and better security.

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