Revolutionizing Healthcare: NLP, Deep Learning, and WSN Solutions for Managing the COVID-19 Crisis

Abstract

The COVID-19 outbreak in 2020 catalyzed a global socio-economic upheaval, compelling nations to embrace digital technologies as a means of countering economic downturns and ensuring efficient communication systems. This paper delves into the role of Natural Language Processing (NLP) in harnessing wireless connectivity during the pandemic. The examination assesses how wireless networks have affected various facets of crisis management, including virus tracking, optimizing healthcare, facilitating remote education, and enabling unified communications. Additionally, the article underscores the importance of digital inclusion in mitigating disease outbreaks and reconnecting marginalized communities. To address these challenges, a Dual CNN-based BERT model is proposed. BERT model is used to extract the text features, the internal layers of BERT excel at capturing intricate contextual details concerning words and phrases, rendering them highly valuable as features for a wide array of text analysis tasks. The significance of dual CNN is capturing the unique capability to seamlessly integrate both character-level and word-level information. This fusion of insights from different levels of textual analysis proves especially valuable in handling text data that is noisy, complex, or presents challenges related to misspellings and domain-specific terminology. The proposed model is evaluated using the simulated WSN-based text data for crisis management.