Managing emergency crises using secure information through educational awareness: COVID-19 case study.

Social networks are increasingly taking over daily life, creating a volume of unsecured data and making it very difficult to capture safe data, especially in times of crisis. This study aims to use a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM)-based hybrid model for health monitoring and health crisis forecasting. It consists of efficiently retrieving safe content from multiple social media sources.

Enhancing Warning, Situational Awareness, Assessment and Education in Managing Emergency: Case Study of COVID-19.

The volume of network and Internet traffic is increasing extraordinarily fast daily, creating huge data. With this volume, variety, speed, and precision of data, it is hard to collect crisis information in such a massive data environment. This paper proposes a hybrid of deep convolutional neural network (CNN)-long short-term memory (LSTM)-based model to efficiently retrieve crisis information.