| Description |
1 online resource (226 p.) |
| Series |
Intelligent Data-Centric Systems Series |
|
Intelligent Data-Centric Systems Series
|
| Contents |
Front Cover -- Artificial Intelligence in Healthcare and COVID-19 -- Copyright Page -- Contents -- List of contributors -- Preface -- 1 Improvement of mental health of frontline healthcare workers during COVID-19 pandemic using artificial intelligence -- Other notes -- 1.1 Introduction -- 1.2 Background -- 1.3 Main content -- 1.4 Methodologies and implementation -- 1.5 Discussion -- 1.5.1 Connection to artificial intelligence -- 1.5.2 Strengths -- 1.5.3 Weaknesses -- 1.6 Conclusion -- References -- 2 Effective algorithms for solving statistical problems posed by COVID-19 pandemic |
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2.1 Introduction -- 2.2 Forecasting the epidemic curves of coronavirus -- 2.2.1 Forecasting models for the COVID-19 outbreak -- 2.3 Nonparametric tests used for forecasting models estimation -- 2.3.1 Nonparametric tests for homogeneity -- 2.3.2 Exact nonparametric test for homogeneity -- 2.4 Comparison of forecast models -- 2.5 Conclusion and scope for the future work -- References -- 3 Reconsideration of drug repurposing through artificial intelligence program for the treatment of the novel coronavirus -- 3.1 Introduction -- 3.2 Viral morphology -- 3.2.1 Structured proteins |
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3.2.1.1 Spike protein/spike membrane -- 3.2.1.2 Membranous proteins -- 3.2.1.3 Nucleic acid-protein/nucleocapsid -- 3.2.1.4 Enveloped protein -- 3.2.2 Nonstructured proteins -- 3.2.2.1 Proteases -- 3.2.2.2 RNA-dependent polymerase -- 3.2.2.3 Helicase -- 3.3 Virus lifecycle -- 3.3.1 Life process of severe acute respiratory syndrome 2 -- 3.3.1.1 Attachment and entry -- 3.3.1.2 Replication and transcription -- 3.3.1.3 Assembly and release -- 3.4 Currently available viral targeting drug candidates at various stages of life cycle -- 3.5 Different drug repurposing approaches -- 3.5.1 Target approach |
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3.5.2 Knowledge-dependent approach -- 3.5.3 Molecular docking-based approach -- 3.5.4 Machine learning approaches -- 3.5.5 Pathway-based approaches -- 3.5.6 Artificial neuronal network approaches -- 3.5.7 Deep learning machine approaches -- 3.5.8 Network modeling approach -- 3.5.8.1 Autoencoder approaches -- 3.5.8.2 Text mining approaches -- 3.6 Artificial intelligence algorithms for drug repurposing -- 3.7 Computational intelligence-based approaches to identify therapeutic candidates for repurposing against coronavirus -- 3.7.1 Network-based model -- 3.7.2 Structure-based approaches |
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3.7.3 Artificial intelligence approaches -- 3.8 Challenges in drug repurposing -- 3.9 Future perspectives of artificial intelligence-informed drug repurposing -- 3.10 Conclusion -- References -- 4 COVID-19: artificial intelligence solutions, prediction with country cluster analysis, and time-series forecasting -- 4.1 Introduction -- 4.1.1 Motivation for this study -- 4.1.2 Adverse impacts of COVID-19 outbreak -- 4.1.3 Chapter organization -- 4.1.4 Table of acronyms used in this chapter -- 4.2 Review of literature on COVID-19 pandemic -- 4.3 K-means clustering for COVID-19 country analysis |
| Notes |
Description based upon print version of record |
|
4.3.1 Cluster analysis: an overview |
| Form |
Electronic book
|
| Author |
Esposito, Massimo
|
| ISBN |
9780323905732 |
|
0323905730 |
|
