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

Abstract

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. Deep CNN is used to extract significant characteristics from multiple sources. LSTM is used to maintain long-term dependencies in extracted characteristics while preventing overfitting on recurring connections. This method has been compared to previous approaches to the performance of a publicly available dataset to demonstrate its highly satisfactory performance. This new approach allows integrating artificial intelligence technologies, deep learning and social media in managing crisis model. It is based on an extension of our previous approach namely long short-term memory-based disaster management and education: this experience forms a background for this model. It combines representation training with situational awareness and education, while retrieving template information by combining various search results from multiple sources. We have extended it to improve our managing disaster model and evaluate it in the case of the coronavirus disease 2019 (COVID-19) while achieving promising results.

Smart Education with Deep Learning and Social Media for Disaster Management in the pursuit of Environmental Sustainability While avoiding Fake News

“…Actually, many networking platforms enable access to their content by Application Programming Interface (API) [37]. Online listening tools Approaches Methods [21] Raising awareness in multicultural societies: Disaster Awareness Game (DAG) approach [19] Synthesis with socio-temporal context [20] Multiscale analysis of Twitter activity before, during, and after Hurricane Sandy [22] Creating a Tweet Aggregation Dataset using Text REtrieval Conference (TREC) Tracks [36] AI-based Semi-automated classifier for disaster response [23] Summary of contextual tweets in crisis events: an extractive-abstractive approach [12] Recurrent Neural Networks (RNN)-based automated learning environment to improve awareness [13,39] LSTM-based ALE to enhance awareness and education [14] Deep CNN-LSTM-based model to improve warning, awareness and education in crisis event Our New Approach…”

“…This mode provides access to the material learned from the course as a reference [3, 12-14, 34, 39]: thereby -supporting example-based online help. Educational messages play a role in raising awareness in times of public health crisis [14,34].…”

“…Education about the Covid-19 [14,34] consists of -advising to always reinforce preventive hygiene, namely + the elimination of physical contact, kisses and handshakes, + coughing and/or sneezing into the crook of the elbow, + using disposable tissues, -respect physical/social distancing, -wear a bib, -stop gatherings, trips and any major event, -promote hand washing and -avoid any social or cultural gatherings. Disaster education consists of constantly rehashing these tips on all information channels, websites and all social and networking media to have maximum awareness [3, 12-14, 34, 39].…”

“…Information security is the most important concept in disaster management, even with social networks. This is ensured here in two sequential ways, namely: First, Security by Retrieving Content from Multiple Sources [2, 3, 11-14, 34, 39] and finally, Security against Fake News [14,32,34].…”

“…These potential attacks can make us very vulnerable. They are of different kinds, namely: social engineering (phishing, ...), network eavesdropping (WireShark), malicious code, Trojan horse, spyware (keyloggers), bots and human bots, ... [14,32,34] Data breaches remain relentless and the size of leaked data sets is steadily increasing. The secure computing concept promises to keep data encrypted, protected at all times and unavailable always to breaching attackers.…”