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| 008 | 240724s2021 xx o ||||0 eng d | ||
| 020 |
_a9781000794793 _q(electronic bk.) |
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| 020 | _z9788770224437 | ||
| 035 | _a(MiAaPQ)EBC6641375 | ||
| 035 | _a(Au-PeEL)EBL6641375 | ||
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| 050 | 4 | _aRA644.C67 | |
| 082 | 0 | _a614.592414 | |
| 100 | 1 | _aJain, Arpit. | |
| 245 | 1 | 0 | _aUse of AI, Robotics and Modelling Tools to Fight Covid-19. |
| 250 | _a1st ed. | ||
| 264 | 1 |
_aMilton : _bRiver Publishers, _c2021. |
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| 264 | 4 | _c©2021. | |
| 300 | _a1 online resource (246 pages) | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 505 | 0 | _aCover -- Half Title -- Series Page -- Title Page -- Copyrights Page -- Table of Contents -- Preface -- Acknowledgement -- List of Contributors -- List of Figures -- List of Tables -- List of Notations and Abbreviations -- 1: The History of Pandemics and Evolution So Far -- 1.1 Introduction -- 1.2 Definition of Pandemics -- 1.3 History of Pandemics -- 1.3.1 Prehistoric Epidemic -- 1.3.2 Modern Epidemics -- 1.4 Attributes of a Pandemic -- 1.5 Origin of The Coronavirus or Covid-19 -- 1.5.1 Pathophysiology -- 1.5.2 Signs, Symptoms, and Transmission -- 1.5.3 Diagnosis -- 1.5.4 Prevention -- 1.5.5 Management -- 1.6 Types of Covid-19 -- 1.7 Vaccine -- 1.8 Pandemic Impacts -- 1.9 Conclusion -- References -- 2: Tracing the Origins of COVID-19 -- 2.1 Introduction -- 2.2 History of the Virus -- 2.2.1 Influenza -- 2.2.2 Seasonal Flu -- 2.2.3 2002-2004: Severe Acute Respiratory Syndrome -- 2.2.4 2009 (H1N1) Flu Pandemic -- 2.2.5 Middle East Respiratory Syndrome Coronavirus (MERS-COV) - 2012 -- 2.2.6 2014-2016 Ebola -- 2.3 Genetic Sequence of Sars-Cov-2 -- 2.4 Transmission and Diagnosis -- 2.5 Conclusion -- 2.6 Acknowledgment -- References -- 3: AI for COVID-19: The Journey So Far -- 3.1 Introduction -- 3.2 Artificial Intelligence -- 3.3 Potential Contribution of Ai Against Covid-19 -- 3.3.1 Diagnosis of Disease -- 3.3.2 Discovery of Drug and Vaccine -- 3.3.3 Prediction of Mortality and Survival Rate -- 3.3.4 Contact Tracing -- 3.3.5 Robotics and Health Care -- 3.3.6 COVID-19 Chatbots -- 3.3.7 Prevent Further Spread of Disease -- 3.4 Conclusion -- References -- 4: Technological Opportunities to Fight COVID-19 for Indian Scenario -- 4.1 Introduction -- 4.2 Technological Interventions -- 4.2.1 Robotic Technologies in COVID-19 -- 4.2.2 Smart Surveillance Systems -- 4.2.3 Artificial Intelligence and Machine Learning -- 4.2.4 Computational Fluid Dynamics. | |
| 505 | 8 | _a4.2.5 Unmanned Aerial Vehicles -- 4.3 Conclusion -- References -- 5: Mobile Robots in COVID-19 -- 5.1 Introduction -- 5.1.1 What is Mobile Robot? -- 5.1.2 Components of Mobile Robots -- 5.1.3 Mobile Robots and COVID-19 -- 5.2 Requirements of Mobile Robots in Pandemic Situation -- 5.3 Innovation and Classification of Mobile Robots -- 5.4 Future Scope and Challenges -- 5.4.1 Challenges During Development Phase -- 5.4.2 Challenges During Deployment Phase -- 5.5 Conclusion -- References -- 6: Predictor System for Tracing COVID-19 Spread -- 6.1 Introduction -- 6.2 Various Prediction Methods -- 6.3 Case Study - Prediction of Effective Reproductive Number for India -- 6.4 Results and Discussions -- 6.5 Conclusion -- References -- 7: Discovery of Robust Distributions of COVID-19 Spread -- 7.1 Introduction -- 7.2 Methodology -- 7.2.1 Data Preprocessing -- 7.2.2 Temporal Analysis -- 7.2.3 Distribution Detection -- 7.2.4 Outlier Detection -- 7.3 Experimental Results -- 7.3.1 Geospatial Context of the Data -- 7.3.2 Results -- 7.4 Conclusion -- References -- 8: Toward Smart Hospital: An Intelligent Personnel Scheduling Using Evolutionary Algorithms -- 8.1 Introduction -- 8.2 Methodology -- 8.2.1 Data Collection -- 8.2.2 Mathematical Model Development -- 8.2.3 Discrete GWO with a Novel Neighborhood Search Operator -- 8.3 Computational Results -- 8.4 Conclusion -- References -- 9: Role of Artificial Intelligence Based Wireless Sensor Network for Pandemic Control: A Case Study Using CupCarbon -- 9.1 Introduction -- 9.2 Proposed Analysis -- 9.3 Modeling and Simulation -- 9.4 Results and Discussions -- 9.4.1 Application in Pandemic Control -- 9.5 Conclusion -- References -- 10: Peculiarities of Technical Measures During the COVID-19 Pandemic -- 10.1 Introduction -- 10.2 Application of Tbt Measures By Wto Members. | |
| 505 | 8 | _a10.3 Peculiarities of Application of Standardization Tools During the Pandemic -- 10.3.1 Standard Accessibility in Response to the COVID-19 Pandemic -- 10.3.2 Development of Standardization Documents in Response to the COVID-19 Pandemic -- 10.4 Main Conclusions and Recommendations from the Analysis of TBT Measures During the Covid-19 Pandemic -- 10.5 Acknowledgements -- References -- 11: Climate Change and COVID-19: An Interplay -- 11.1 Introduction -- 11.2 Comparison of Two Disruptors, The Climate Risk, and Covid-19 -- 11.2.1 Short-Term and Long-Term Effects of Climate Change and COVID-19 -- 11.2.2 Short-Term Effects of the Current Pandemic -- 11.2.3 Long-Term Effects of the Pandemic -- 11.2.4 Short-Term Effects of Climate Change -- 11.2.5 Long-Term Effects of Climate Change -- 11.2.6 Searching Ways to Mitigate -- 11.2.7 Common Features -- 11.2.8 Features that Make Them Different -- 11.2.9 Mitigating the Risk by Avoiding its Multiplication -- 11.3 Trends in CO2 and GHG Emission Levels -- 11.4 Effect of Covid-19 on Emission Levels and on Energy Demand -- 11.5 How to Move Forward -- 11.5.1 Responses Helpful in Saving the Environment -- 11.5.2 Pitfalls in Road that Can Blur the Focus of Stakeholders for Reducing Emissions -- 11.5.3 Road Map for the Planners -- 11.6 Conclusion -- 11.7 Acknowledgements -- References -- 12: COVID-19 Pandemic: A New Era in Higher Education -- 12.1 Introduction -- 12.2 Covid-19 Impact on Higher Education -- 12.2.1 All Educational Activities are Disrupted -- 12.2.2 Turndown in Employment Opportunities -- 12.2.3 Impact on Academic Research and Professional Development -- 12.2.4 Attendance of Students May Slow Down -- 12.2.5 National and International Student Mobility for Higher Study May Be Reduced -- 12.3 Challenges of India for Higher Education During Covid-19. | |
| 505 | 8 | _a12.3.1 Virtual Platforms in Higher Education at Times of COVID-19 -- 12.4 Challenges Undertaken for Digitalizing Sector in Higher Education -- 12.4.1 Resource and Internet Connectivity -- 12.4.2 Shortage of Trained Teachers -- 12.4.3 Content-Related Challenges -- 12.4.4 Poor Maintenance and Upgradation of Digital Equipment -- 12.4.5 Inadequate Funds -- 12.5 Post-Covid-19 (Digitalization: A New Phenomenon in Higher Education -- 12.5.1 Urge for Distance Learning and Online Learning May Grow -- 12.5.2 Blending Teaching and Learning with Technology -- 12.5.3 New Design in Assessment System -- 12.5.4 Online Learning Helped Us to Tackle the Crisis -- 12.6 Conclusion -- 12.7 Acknowledgment -- References -- 13: Virtual Reality: Solution to Reduce the Impact of COVID-19 on Global Economy -- 13.1 Introduction -- 13.2 Impact of Covid-19 on the Economy -- 13.2.1 Tourism Industry -- 13.2.2 Automobile Industry -- 13.2.3 Agriculture -- 13.2.4 Aviation Industry -- 13.2.5 Oil Industry -- 13.2.6 Construction Industry -- 13.2.7 Food Industry -- 13.2.8 Healthcare and Medical Industry -- 13.3 Domain Moving Toward Virtual Reality for Survival -- 13.3.1 Education -- 13.3.2 Hospital -- 13.3.3 Agriculture -- 13.3.4 Sports -- 13.3.5 Businesses -- 13.3.6 Government -- 13.4 Challenges During Implementation of Virtual Reality -- 13.4.1 Lack of Familiarity -- 13.4.2 Network Load -- 13.4.3 Bottleneck Communication -- 13.4.4 Cost -- 13.4.5 Internet -- 13.4.6 User Experience Issue -- 13.4.7 Security -- 13.4.8 Powerful Computers -- 13.5 Road Map Toward Normal During Covid-19 -- 13.6 Implications for Research -- 13.7 Conclusion -- References -- Index -- About the Editors. | |
| 520 | _aThis includes confirmed cases, active cases, cured cases and deaths in each country. This data set can be used for predicting the active cases across different regions of the world so that appropriate amount of health infrastructure can be made available to these places. | ||
| 588 | _aDescription based on publisher supplied metadata and other sources. | ||
| 590 | _aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. | ||
| 650 | 0 | _aArtificial intelligence--Medical applications. | |
| 655 | 4 | _aElectronic books. | |
| 700 | 1 | _aSharma, Abhinav. | |
| 700 | 1 | _aWang, Jianwu. | |
| 700 | 1 | _aRam, Mangey. | |
| 776 | 0 | 8 |
_iPrint version: _aJain, Arpit _tUse of AI, Robotics and Modelling Tools to Fight Covid-19 _dMilton : River Publishers,c2021 _z9788770224437 |
| 797 | 2 | _aProQuest (Firm) | |
| 856 | 4 | 0 |
_uhttps://ebookcentral.proquest.com/lib/orpp/detail.action?docID=6641375 _zClick to View |
| 999 |
_c27449 _d27449 |
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