Automated Online Exam Proctoring System Using Computer Vision and Hybrid ML Classifier

Importance of online education can be seen especially during the ongoing Covid-19 when going to schools or colleges is not possible. So validity of online exams should also be maintained with respect to traditional pen-paper examinations. However, absence of invigilator makes it easy for the examinees to cheat during the exam. Though there are already many systems for online proctoring, not all educational institutes can afford them as the systems are very expensive. In this paper, we have used eye gaze and head pose estimation as the main features to design our online proctoring system. Therefore, the purpose of this paper is to use these features to create an online proctoring system using computer vision and machine learning and stop cheating attempts in exams. © 2021 IEEE.

Covidaroundus: Social distancing made easier

To reduce the dispersion of COVID - 19, people need to maintain safe distance from each other. This paper proposes a mobile application solution that keeps track of the COVID - 19 positive individuals in a certain area. With the help of the infected person's position uploaded in the cloud system, a location-based recommendation (i.e. informing people about a danger zone) is provided to the related users. Taking the pandemic into consideration, a proper visualization of users' location is made on the map using geospatial hotspot and location-based services. This paper describes the development of the mobile application that uses GPS data to pinpoint the infected person's location and create a danger zone based on the information. The accuracy of the services (provided by the application) was tested and confirmed through experiments. © 2021 IEEE.

Automatic Detection of COVID-19 Disease in Chest X-Ray Images using Deep Neural Networks

The worldwide spread of COVID-19 has marked a devastating impact on the global economy and public health. One of the significant steps of COVID-19 affected patient's treatment is the faster and accurate detection of the symptoms which is the motivational center of this study. In this paper, we have analyzed the performances of six artificial deep neural networks (2-D CNN, ResNet-50, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2) for COVID-19 detection from the chest X-rays. Our dataset consists of 2905 chest X-rays of three categories: COVID-19 affected (219 cases), Viral Pneumonia affected (1345 cases), and Normal Chest X-rays (1341 cases). Among the implemented neural networks, ResNet-50 demonstrated reasonable performance in classifying different cases with an overall accuracy of 96.91%. Most importantly, the model has shown a significantly good performance in detecting the COVID-19 cases in the test dataset (Precision = 1.00, Sensitivity = 1.00, Specificity = 1.00, and F1-score = 1.00). Therefore, among the deep neural networks presented in this paper, ResNet-50 can be adapted as a reliable method for faster and accurate COVID-19 affected case detection. © 2020 IEEE.