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Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis

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Cover of 'Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis'

Table of Contents

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    Book Overview
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    Chapter 1 An Introduction to Computational Intelligence in COVID-19: Surveillance, Prevention, Prediction, and Diagnosis
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    Chapter 2 Role of Computational Intelligence Against COVID-19
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    Chapter 3 Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks
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    Chapter 4 Social Network Analysis for the Identification of Key Spreaders During COVID-19
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    Chapter 5 Mobile Technology Solution for COVID-19: Surveillance and Prevention
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    Chapter 6 The Role of Internet of Things (IoT) in the Containment and Spread of the Novel COVID-19 Pandemic
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    Chapter 7 A Review on Predictive Systems and Data Models for COVID-19
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    Chapter 8 A Comparative Study of the SIR Prediction Models and Disease Control Strategies: A Case Study of the State of Kerala, India
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    Chapter 9 Computational Intelligence Approach for Prediction of COVID-19 Using Particle Swarm Optimization
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    Chapter 10 COVID-19 Insightful Data Visualization and Forecasting Using Elasticsearch
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    Chapter 11 Computational Intelligence Methods for the Diagnosis of COVID-19
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    Chapter 12 Rapid Computer Diagnosis for the Deadly Zoonotic COVID-19 Infection
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    Chapter 13 Computational Intelligence Methods in Medical Image-Based Diagnosis of COVID-19 Infections
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    Chapter 14 Computational Intelligence in Drug Repurposing for COVID-19
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    Chapter 15 COVID-19: Hard Road to Find Integrated Computational Drug and Repurposing Pipeline
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    Chapter 16 Computational Intelligence in Vaccine Design Against COVID-19
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    Chapter 17 Big Data Analytics for Understanding and Fighting COVID-19
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    Chapter 18 IoMT Potential Impact in COVID-19: Combating a Pandemic with Innovation
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    Chapter 19 Advances in Intelligent Based Internet of Medical Things (IoMT) for COVID-19: Olfactory Disorders
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    Chapter 20 Integrating M-Health with IoMT to Counter COVID-19
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    Chapter 21 Digital Image Analysis Is a Silver Bullet to COVID-19 Pandemic
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    Chapter 22 Non Linear Tensor Diffusion Based Unsharp Masking for Filtering of COVID-19 CT Images
Attention for Chapter 18: IoMT Potential Impact in COVID-19: Combating a Pandemic with Innovation
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Chapter title
IoMT Potential Impact in COVID-19: Combating a Pandemic with Innovation
Chapter number 18
Book title
Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis
Published by
Springer, Singapore, October 2020
DOI 10.1007/978-981-15-8534-0_18
Book ISBNs
978-9-81-158533-3, 978-9-81-158534-0
Authors

Mohd Faizan Siddiqui, Siddiqui, Mohd Faizan

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 8%
Lecturer 2 8%
Student > Doctoral Student 2 8%
Professor > Associate Professor 2 8%
Student > Master 2 8%
Other 4 17%
Unknown 10 42%
Readers by discipline Count As %
Engineering 3 13%
Medicine and Dentistry 3 13%
Biochemistry, Genetics and Molecular Biology 2 8%
Environmental Science 1 4%
Mathematics 1 4%
Other 5 21%
Unknown 9 38%