A Real-Time Approach with Deep Learning for Pandemic Management

Title

A Real-Time Approach with Deep Learning for Pandemic Management

Creator

Raghavendra Rao A.; Samanta D.

Description

It has never been so critical to managing pandemic situations created by a virus like COVID-19, which has brought the world almost to a standstill, claiming millions of lives. Learning from all earlier viruses and building a quick tackling mechanism is a need of the hour. There is a greater need for technology to collaborate with healthcare and leverage each of the domains expertise. With less time in hand, this collaboration must happen in a short time. There is a need to study the exiting progression in technology and the healthcare landscape to bring them to a common path for practical solutions. In the chapter, an attempt was made to put together some thoughts in both fields to relate them to pandemic managements frequent subject. Caution is drawn towards some crucial aspects, such as security and transparency, that cannot be compromised in this journey. Artificial intelligence (AI), being at the forefront of the technology supporting lives, provides a greater hope in this direction. Some of the prominent approaches can be looked at from a pandemic management point of view, which can start a more in-depth discussion on AI and healthcare going hand in hand in managing this pandemic situation. Essential areas of pandemic management, such as building on the knowledge gathered over a period, plugging in the real-time data from the society, building efficient data management systems and building transparent and interpretable solutions are the focus areas of exploration in this chapter. 2022, Springer Nature Switzerland AG.

Source

EAI/Springer Innovations in Communication and Computing, pp. 113-139.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

BERT; Convolutional neural network; COVID-19; Deep learning; Generative adversarial network; Graph theory; Long short-term memory; Magnetic resonance imaging; Medicine; Pandemic management; Recurrent neural network; Social network

Coverage

Raghavendra Rao A., Data Science Department, CHRIST (Deemed to be) University and Specialist-QMS, First American India Private Ltd., Bengaluru, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 25228595

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-3-030-72752-9_6

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114423860&doi=10.1007%2f978-3-030-72752-9_6&partnerID=40&md5=6007a57278bc438450a9c3e5fec6126b