Cyber-physical systems AI and Covid-19

"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 techn...

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Detalles Bibliográficos
Otros Autores:	Poonia, Ramesh C., 1979- editor (editor)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	London : Academic Press [2022]
Materias:	Cooperating objects (Computer systems) COVID-19 Pandemic, 2020- > Research.
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009835434406719

Tabla de Contenidos:

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
References
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
References
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
References
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
References
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
num
WorkFromHome
7.5.2 Sentiment analysis of &amp
num
MigrantWorkers
7.6 Conclusion
References
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
References
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
References
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
References
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
References
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
References
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
References
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
References
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.

Cyber-physical systems AI and Covid-19

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