Human Emotion Prediction Analysis on Post-COVID-19 Crisis in Digital Media Using Deep Learning

The COVID-19 pandemic has produced a significant impact on society. Apart from its deadliest attack on human health and economy, it has also been affecting the mental stability of human being at a larger scale. Though vaccination has been partially successful to prevent further virus outreach, it is leaving behind typical health-related complications even after surviving from the disease. This research work mainly focuses on human emotion prediction analysis in post-COVID-19 period. In this work, a considerable amount of data collection has been performed from various digital sources, viz. Facebook, e-newspapers, and digital news houses. Three distinct classes of emotion, i.e., analytical, depressed, and angry, have been considered. Finally, the predictive analysis is performed using four deep learning models, viz. CNN, RNN, LSTM, and Bi-LSTM, based on digital media responses. Maximum accuracy of 97% is obtained from LSTM model. It has been observed that the post-COVID-19 crisis has mostly depressed the human being. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Secured GUI based Flexible Automated System for Face Recognition and Suspicious Behaviour Prediction

Protection of each organization or campus is vital in recent times, as the crime rate in modern days is growing every day. Unauthorized entry of people will affect the secrecy of the data along with adverse economic effects. As the world is transforming towards a complicated technological style, computerized systems have been designed and implemented in many sectors. The rise in the crime rate necessitated the development of automated systems that can replace manual involvement which is tedious and time-consuming. However, these systems should be accurate unless of which the complete objective will go in vain. Much work has been carried out that makes use of biometric strategies and fingerprint-based for getting admission to structures. But these approaches have the constraints of getting mimicked or affecting the decision accuracy of decision making. In this pandemic situation, contactless design development is getting important. As the people are becoming negligible in following Covid precautions affecting the surrounding community, face mask detection systems are introduced that can yield a better solution. The current work proposes a novel system that serves the multiple functionalities of automated security with face recognition for attendance monitoring and secured entry as well as mask detection under Covid pandemic prevailing situations. The novelty of the current work is that it can also be used for identifying emotions and for automated attendance monitoring. © 2022 IEEE.