Service Quality Of Educational Websites: An Empirical Study

This paper employs a measurement of the service quality of educational Websites as perceived by students, following a conceptual model developed by Santos (2003).  Perceived service quality of educational Websites is conceptualized as dependent on the following variables—ease of use, content, reliability, privacy, and interaction. An empirical test with university students validates the overall model.  As for the individual independent variables, all of them except privacy are found to have a significant effect on perceived service quality

AFMB-Net: DeepFake Detection Network Using Heart Rate Analysis

With advances in deepfake generating technology, it is getting increasingly difficult to detect deepfakes. Deepfakes can be used for many malpractices such as blackmail, politics, social media, etc. These can lead to widespread misinformation and can be harmful to an individual or an institution’s reputation. It has become important to be able to identify deepfakes effectively, while there exist many machine learning techniques to identify them, these methods are not able to cope up with the rapidly improving GAN technology which is used to generate deepfakes. Our project aims to identify deepfakes successfully using machine learning along with Heart Rate Analysis. The heart rate identified by our model is unique to each individual and cannot be spoofed or imitated by a GAN and is thus susceptible to improving GAN technology. To solve the deepfake detection problem we employ various machine learning models along with heart rate analysis to detect deepfakes