Cyber-physical systems : AI and Covid-19 / editors, Ramesh Chandra Poonia [et al.]

Format:

Book

Contributor:

Poonia, Ramesh C., 1979- editor.

Language:

English

Subjects (All):

Cooperating objects (Computer systems).

COVID-19 Pandemic, 2020-2023--Research.

COVID-19 Pandemic, 2020-2023.

Physical Description:

1 online resource (278 pages) : illustrations

Other Title:

AI and Covid-19

Artificial intelligence and Covid-19

Place of Publication:

London : Academic Press, [2022]

Summary:

"Cyber-physical systems: AI and COVID-19 highlights original research which addresses current data challenges in terms of the development of mathematical models, cyber-physical systems-based tools and techniques, and the design and development of algorithmic solutions, etc. It reviews the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS) and reviews tools and techniques that can be used. This book will act as a resource to guide COVID researchers as they move forward with clinical and epidemiological studies on this outbreak, including the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS). The major problem in the identification of COVID-19 is detection and diagnosis due to non-availability of medicine. In this situation, only one method, Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been widely adopted and used for diagnosis. With the evolution of COVID-19, the global research community has implemented many machine learning and deep learning-based approaches with incremental datasets. However, finding more accurate identification and prediction methods are crucial at this juncture."-- Title details screen.

Contents:

Front Cover

Cyber-Physical Systems: AI and COVID-19

Copyright Page

Contents

List of contributors

1 AI-based implementation of decisive technology for prevention and fight with COVID-19

1.1 Introduction

1.2 Related work

1.3 Proposed work

1.3.1 Face mask detection

1.3.2 Detection of COVID from CT images

1.4 Results and analysis

1.4.1 Face mask detection

1.4.2 CT scan image-based COVID-19 patient identification

1.5 Conclusion

References

2 Internet of Things-based smart helmet to detect possible COVID-19 infections

2.1 Introduction

2.1.1 Epidemiology

2.1.2 Treatment

2.1.3 Prevention

2.1.4 Symptoms

2.1.5 Stages of COVID-19

2.1.6 Key merits of IoT for COVID-19 pandemic

2.1.7 Internet of Things process required for COVID-19

2.1.8 IoT applications for COVID-19

2.2 Related work

2.3 IoT-based smart helmet to detect the infection of COVID-19

2.3.1 Objective

2.3.2 Methodology

2.3.2.1 Efficiency of smart helmet

2.3.2.2 Components of smart helmet

2.3.2.2.1 Thermal camera

2.3.2.2.2 Optical camera

2.3.2.2.3 Arduino Integrated Development Environment (IDE)

2.3.2.2.4 Proteus software

2.3.2.2.5 Google Location History

2.4 Conclusion

3 Role of mobile health in the situation of COVID-19 pandemics: pros and cons

3.1 Introduction

3.2 Implementation of a training module for the mHealth care worker

3.3 Government policies for the scale-up of the mHealth services

3.4 Popular models of mHealth serving for pandemic COVID-19

3.5 Ethical consideration

3.6 Superiority of mHealth services over other available services

3.7 Probability of conflict of interest between user and service provider

3.8 Legal consideration

3.9 Protection of privacy of end-users

3.10 Conclusion

3.11 Future prospects

References.

4 Combating COVID-19 using object detection techniques for next-generation autonomous systems

4.1 Introduction

4.2 Need for object detection

4.3 Object detection techniques

4.3.1 R-CNN family

4.3.1.1 R-CNN

4.3.1.1.1 Network architecture

4.3.1.1.2 Advantages

4.3.1.1.3 Disadvantages

4.3.1.2 Fast R-CNN

4.3.1.2.1 Network architecture

4.3.1.2.2 The RoI pooling layer

4.3.1.2.3 Advantages

4.3.1.2.4 Disadvantages

4.3.1.3 Faster R-CNN

4.3.1.3.1 Network architecture

4.3.1.3.2 Advantages

4.3.1.3.3 Disadvantages

4.3.2 YOLO family

4.3.2.1 YOLOv1

4.3.2.1.1 Network architecture

4.3.2.1.2 Advantages

4.3.2.1.3 Disadvantages

4.3.2.2 YOLOv2

4.3.2.2.1 Improvements made over YOLOv1

4.3.2.2.2 Network architecture

4.3.2.2.3 Advantages

4.3.2.2.4 Disadvantages

4.3.2.3 YOLOv3

4.3.2.3.1 Improvements made over YOLOv2

4.3.2.3.2 Network architecture

4.3.2.3.3 Advantages

4.3.2.3.4 Disadvantages

4.4 Applications of objection detection during COVID-19 crisis

4.4.1 Module for autonomous systems (pothole detection)

4.4.1.1 Architecture

4.4.1.2 Results

4.4.2 Social distancing detector

4.4.2.1 Results

4.4.3 COVID-19 detector based on X-rays

4.4.3.1 Architecture

4.4.3.1.1 Results

4.4.4 Face mask detector

4.4.4.1 Architecture

4.4.4.1.1 Results

4.5 Conclusion

5 Non-contact measurement system for COVID-19 vital signs to aid mass screening-An alternate approach

5.1 Introduction

5.2 COVID-19 global scenarios

5.2.1 Infections, recovery and mortality rate

5.2.2 Economy and environmental impacts

5.3 Measurement and testing protocols of COVID-19

5.3.1 Measurement methods

5.3.1.1 Pathophysiological tools

5.3.1.1.1 Nucleic acid amplification tests

5.3.1.1.2 Serological testing

5.3.1.2 Physiological assessment tools.

5.3.2 COVID-19 innovations

5.4 Non-contact approaches to physiological measurement

5.4.1 Need for non-contact measurement

5.4.2 State of the art to prior work

5.4.3 Proposed approach

5.4.4 Methodology

5.4.5 Preliminary experimental results

5.4.5.1 Face detection and region of interest selection

5.5 Conclusion

Acknowledgment

6 Evolving uncertainty in healthcare service interactions during COVID-19: Artificial Intelligence - a threat or support to...

6.1 Introduction

6.2 Service dominant logic in marketing

6.3 Service interactions and cocreated wellbeing

6.4 Uncertainty due to pandemic

6.5 Uncertainty in healthcare

6.5.1 Impact of pandemic-led uncertainty on a patient's mind

6.5.2 Impact of pandemic-led uncertainty on service interactions

6.6 The emerging role of Artificial Intelligence

6.7 AI combating uncertainty and supporting value cocreation in healthcare interactions

6.8 The spill-over effect of Artificial Intelligence

6.9 Conclusion and future work

7 The COVID-19 outbreak: social media sentiment analysis of public reactions with a multidimensional perspective

7.1 Introduction

7.2 Data collection

7.3 Sentiment analysis of the tweets collected worldwide

7.4 Sentiment analysis of Tweets for India

7.4.1 COVID-19 analysis for individual city of India-Mumbai

7.4.1.1 Sentiment analysis of tweets in Mumbai

7.5 Analysis of few most trending hashtags

7.5.1 Opinion analysis for the hashtag &amp

num

WorkFromHome

7.5.1.1 Sentiment analysis of &amp

7.5.2 Sentiment analysis of &amp

MigrantWorkers

7.6 Conclusion

8 A new approach to predict COVID-19 using artificial neural networks

8.1 Introduction

8.2 Related studies.

8.3 Fundamental symptoms and conditions responsible for COVID-19 infection

8.4 Proposed COVID-19 detection methodology

8.5 Brief description of artificial neural networks

8.5.1 Principles of artificial neural network

8.6 Parameter settings for the proposed ANN model

8.7 Experimental results and discussion

8.8 Performance comparison between ANN and other classification algorithms

8.9 Conclusion

Appendix

9 Rapid medical guideline systems for COVID-19 using database-centric modeling and validation of cyber-physical systems

9.1 Introduction

9.2 Global pandemic of COVID-19

9.3 Database-centric cyber-physical systems for COVID-19

9.3.1 Cyber-physical systems

9.3.2 Flow of rapid database-centric cyber-physical system

9.4 Modeling and validation of rapid medical guideline systems

9.5 Conclusion

10 Machine learning and security in Cyber Physical Systems

10.1 Introduction

10.2 Related work

10.2.1 Phishing

10.2.2 Intrusion detection for networks

10.2.3 Key stroke elements validation

10.2.4 Breaking human collaboration proofs (CAPTHAs)

10.2.5 Cryptography

10.2.6 Spam detection for social networking

10.3 Motivation

10.4 Importance of cyber security and machine learning

10.5 Machine learning for CPS applications

10.6 Future for CPS technology

10.6.1 Cyber physical systems and human

10.6.2 CPS and artificial intelligence

10.6.3 Trustworthy

10.6.4 Cyber physical systems of systems

10.7 Challenges and opportunities in CPS

10.8 Conclusion

11 Impact analysis of COVID-19 news headlines on global economy

11.1 Introduction

11.2 Related work

11.3 Proposed methodology

11.3.1 Data and data preprocessing

11.3.2 Sentiment analysis

11.3.2.1 Machine learning

11.3.2.2 Deep learning

11.3.2.3 Lexicon method.

11.3.3 Prediction of Nifty score

11.3.3.1 Linear regression

11.3.3.2 Polynomial regression

11.3.3.3 Random forest

11.3.3.4 Gradient boost regressor

11.4 Results and experimental framework

11.4.1 Linear regression

11.4.2 Polynomial regression with degree 3

11.4.3 Random forest regression

11.4.4 Gradient boost regressor

11.5 Conclusion

Further reading

12 Impact of COVID-19: a particular focus on Indian education system

12.1 Introduction

12.2 Impact of COVID-19 on education

12.2.1 Effect of home confinement on children and teachers

12.2.2 A multidimensional impact of uncertainty

12.3 Sustaining the education industry during COVID-19

12.4 Conclusion

13 Designing of Latent Dirichlet Allocation Based Prediction Model to Detect Midlife Crisis of Losing Jobs due to Prolonged...

13.1 Introduction

13.2 Literature survey

13.3 Methodology

13.3.1 Distinguishing midlife crisis symptoms

13.3.2 Designing of the prediction model

13.3.3 Application of LDA and statistical comparison

13.3.3.1 Formulation of Dirichlet distribution

13.3.3.2 Categorization in Bayesian model

13.3.3.3 Concept of topic modeling

13.4 Result and discussion

13.5 Conclusion and future scope

14 Autonomous robotic system for ultraviolet disinfection

14.1 Introduction

14.2 Background

14.2.1 Ultraviolet light for disinfection

14.2.2 Exposure time for deactivation of the bacteria

14.2.3 Flow chart of UV bot control logic

14.2.4 Calculations related to the time for disinfection

14.3 Implementation

14.4 Model topology

14.4.1 UV-C light robotic vehicle

14.5 Conclusion

15 Emerging health start-ups for economic feasibility: opportunities during COVID-19

15.1 Introduction

15.2 Health-tech verticals for start-ups.

15.3 Research gap.

Notes:

Description based on print version record.

ISBN:

9780323853576

0323853579

OCLC:

1285169406