Enabling healthcare 4. 0 for pandemics : a roadmap using AI, machine learning, IoT and cognitive technologies / / edited by Abhinav Juneja [and four others]

• Pubblicazione: Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021
• Descrizione fisica: 1 online resource (352 pages)
• Disciplina: 610.285
• Soggetto topico: Artificial intelligence - Medical applications; Medical technology
• Persona (resp. second.): JunejaAbhinav
• Note generali: Includes index.
• Nota di contenuto: Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: MACHINE LEARNING FOR HANDLINGCOVID-19 -- 1 COVID-19 and Machine Learning Approaches to Deal With the Pandemic -- 1.1 Introduction -- 1.1.1 COVID-19 and its Various Transmission Stages Depending Upon the Severity of the Problem -- 1.2 COVID-19 Diagnosis in Patients Using Machine Learning -- 1.2.1 Machine Learning to Identify the People who are at More Risk of COVID-19 -- 1.2.2 Machine Learning to Speed Up Drug Development -- 1.2.3 Machine Learning for Re-Use of Existing Drugs in Treating COVID-19 -- 1.3 AI and Machine Learning as a Support System for Robotic System and Drones -- 1.3.1 AI-Based Location Tracking of COVID-19 Patients -- 1.3.2 Increased Number of Screenings Using AI Approach -- 1.3.3 Artificial Intelligence in Management of Resources During COVID-19 -- 1.3.4 Influence of AI on Manufacturing Industry During COVID-19 -- 1.3.5 Artificial Intelligence and Mental Health in COVID-19 -- 1.3.6 Can AI Replace the Human Brain Intelligence in COVID-19 Crisis? -- 1.3.7 Advantages and Disadvantages of AI in Post COVID Era -- 1.4 Conclusion -- References -- 2 Healthcare System 4.0 Perspectives on COVID-19 Pandemic -- 2.1 Introduction -- 2.2 Key Techniques of HCS 4.0 for COVID-19 -- 2.2.1 Artificial Intelligence (AI) -- 2.2.2 The Internet of Things (IoT) -- 2.2.3 Big Data -- 2.2.4 Virtual Reality (VR) -- 2.2.5 Holography -- 2.2.6 Cloud Computing -- 2.2.7 Autonomous Robots -- 2.2.8 3D Scanning -- 2.2.9 3D Printing Technology -- 2.2.10 Biosensors -- 2.3 Real World Applications of HCS 4.0 for COVID-19 -- 2.4 Opportunities and Limitations -- 2.5 Future Perspectives -- 2.6 Conclusion -- References -- 3 Analysis and Prediction on COVID-19 Using Machine Learning Techniques -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Types of Machine Learning. ; 3.4 Machine Learning Algorithms -- 3.4.1 Linear Regression -- 3.4.2 Logistic Regression -- 3.4.3 K-NN or K Nearest Neighbor -- 3.4.4 Decision Tree -- 3.4.5 Random Forest -- 3.5 Analysis and Prediction of COVID-19 Data -- 3.5.1 Methodology Adopted -- 3.6 Analysis Using Machine Learning Models -- 3.6.1 Splitting of Data into Training and Testing Data Set -- 3.6.2 Training of Machine Learning Models -- 3.6.3 Calculating the Score -- 3.7 Conclusion & -- Future Scope -- References -- 4 Rapid Forecasting of Pandemic Outbreak Using Machine Learning -- 4.1 Introduction -- 4.2 Effect of COVID-19 on Different Sections of Society -- 4.2.1 Effect of COVID-19 on Mental Health of Elder People -- 4.2.2 Effect of COVID-19 on our Environment -- 4.2.3 Effect of COVID-19 on International Allies and Healthcare -- 4.2.4 Therapeutic Approaches Adopted by Different Countries to Combat COVID-19 -- 4.2.5 Effect of COVID-19 on Labor Migrants -- 4.2.6 Impact of COVID-19 on our Economy -- 4.3 Definition and Types of Machine Learning -- 4.3.1 Machine Learning & -- Its Types -- 4.3.2 Applications of Machine Learning -- 4.4 Machine Learning Approaches for COVID-19 -- 4.4.1 Enabling Organizations to Regulate and Scale -- 4.4.2 Understanding About COVID-19 Infections -- 4.4.3 Gearing Up Study and Finding Treatments -- 4.4.4 Predicting Treatment and Healing Outcomes -- 4.4.5 Testing Patients and Diagnosing COVID-19 -- References -- 5 Rapid Forecasting of Pandemic Outbreak Using Machine Learning: The Case of COVID-19 -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Suggested Methodology -- 5.4 Models in Epidemiology -- 5.4.1 Bayesian Inference Models -- 5.5 Particle Filtering Algorithm -- 5.6 MCM Model Implementation -- 5.6.1 Reproduction Number -- 5.7 Diagnosis of COVID-19 -- 5.7.1 Predicting Outbreaks Through Social Media Analysis -- 5.8 Conclusion -- References. ; Part 2: EMERGING TECHNOLOGIES TO DEAL WITH COVID-19 -- 6 Emerging Technologies for Handling Pandemic Challenges -- 6.1 Introduction -- 6.2 Technological Strategies to Support Society During the Pandemic -- 6.2.1 Online Shopping and Robot Deliveries -- 6.2.2 Digital and Contactless Payments -- 6.2.3 Remote Work -- 6.2.4 Telehealth -- 6.2.5 Online Entertainment -- 6.2.6 Supply Chain 4.0 -- 6.2.7 3D Printing -- 6.2.8 Rapid Detection -- 6.2.9 QRT-PCR -- 6.2.10 Immunodiagnostic Test (Rapid Antibody Test) -- 6.2.11 Work From Home -- 6.2.12 Distance Learning -- 6.2.13 Surveillance -- 6.3 Feasible Prospective Technologies in Controlling the Pandemic -- 6.3.1 Robotics and Drones -- 6.3.2 5G and Information and Communications Technology (ICT) -- 6.3.3 Portable Applications -- 6.4 Coronavirus Pandemic: Emerging Technologies That Tackle Key Challenges -- 6.4.1 Remote Healthcare -- 6.4.2 Prevention Measures -- 6.4.3 Diagnostic Solutions -- 6.4.4 Hospital Care -- 6.4.5 Public Safety During Pandemic -- 6.4.6 Industry Adapting to the Lockdown -- 6.4.7 Cities Adapting to the Lockdown -- 6.4.8 Individuals Adapting to the Lockdown -- 6.5 The Golden Age of Drone Delivery -- 6.5.1 The Early Adopters are Winning -- 6.5.2 The Golden Age Will Require Collaboration and Drive -- 6.5.3 Standardization and Data Sharing Through the Smart City Network -- 6.5.4 The Procedure of AI and Non-AI-Based Applications -- 6.6 Technology Helps Pandemic Management -- 6.6.1 Tracking People With Facial Recognition and Big Data -- 6.6.2 Contactless Movement and Deliveries Through Autonomous Vehicles, Drones, and Robots -- 6.6.3 Technology Supported Temperature Monitoring -- 6.6.4 Remote Working Technologies to Support Social Distancing and Maintain Business Continuity -- 6.7 Conclusion -- References -- 7 Unfolding the Potential of Impactful Emerging Technologies Amid COVID-19. ; 7.1 Introduction -- 7.2 Review of Technologies Used During the Outbreak of Ebola and SARS -- 7.2.1 Technological Strategies and Tools Used at the Time of SARS -- 7.2.2 Technological Strategies and Tools Used at the Time of Ebola -- 7.3 Emerging Technological Solutions to Mitigate the COVID-19 Crisis -- 7.3.1 Artificial Intelligence -- 7.3.2 IoT & -- Robotics -- 7.3.3 Telemedicine -- 7.3.4 Innovative Healthcare -- 7.3.5 Nanotechnology -- 7.4 Conclusion -- References -- 8 Advances in Technology: Preparedness for Handling Pandemic Challenges -- 8.1 Introduction -- 8.2 Issues and Challenges Due to Pandemic -- 8.2.1 Health Effect -- 8.2.2 Economic Impact -- 8.2.3 Social Impact -- 8.3 Digital Technology and Pandemic -- 8.3.1 Digital Healthcare -- 8.3.2 Network and Connectivity -- 8.3.3 Development of Potential Treatment -- 8.3.4 Online Platform for Learning and Interaction -- 8.3.5 Contactless Payment -- 8.3.6 Entertainment -- 8.4 Application of Technology for Handling Pandemic -- 8.4.1 Technology for Preparedness and Response -- 8.4.2 Machine Learning for Pandemic Forecast -- 8.5 Challenges with Digital Healthcare -- 8.6 Conclusion -- References -- 9 Emerging Technologies for COVID-19 -- 9.1 Introduction -- 9.2 Related Work -- 9.3 Technologies to Combat COVID-19 -- 9.3.1 Blockchain -- 9.3.2 Unmanned Aerial Vehicle (UAV) -- 9.3.3 Mobile APK -- 9.3.4 Wearable Sensing -- 9.3.5 Internet of Healthcare Things -- 9.3.6 Artificial Intelligence -- 9.3.7 5G -- 9.3.8 Virtual Reality -- 9.4 Comparison of Various Technologies to Combat COVID-19 -- 9.5 Conclusion -- References -- 10 Emerging Techniques for Handling Pandemic Challenges -- 10.1 Introduction to Pandemic -- 10.1.1 How Pandemic Spreads? -- 10.1.2 Background History -- 10.1.3 Corona -- 10.2 Technique Used to Handle Pandemic Challenges -- 10.2.1 Smart Techniques in Cities. ; 10.2.2 Smart Technologies in Western Democracies -- 10.2.3 Technoor Human-Driven Approach -- 10.3 Working Process of Techniques -- 10.4 Data Analysis -- 10.5 Rapid Development Structure -- 10.6 Conclusion & -- Future Scope -- References -- Part 3: ALGORITHMIC TECHNIQUES FOR HANDLING PANDEMIC -- 11 A Hybrid Metaheuristic Algorithm for Intelligent Nurse Scheduling -- 11.1 Introduction -- 11.2 Methodology -- 11.2.1 Data Collection -- 11.2.2 Mathematical Model Development -- 11.2.3 Proposed Hybrid Adaptive PSO-GWO (APGWO) Algorithm -- 11.2.4 Discrete Version of APGWO -- 11.3 Computational Results -- 11.4 Conclusion -- References -- 12 Multi-Purpose Robotic Sensing Device for Healthcare Services -- 12.1 Introduction -- 12.2 Background and Objectives -- 12.3 The Functioning of Multi-Purpose Robot -- 12.4 Discussion and Conclusions -- References -- 13 Prevalence of Internet of Things in Pandemic -- 13.1 Introduction -- 13.2 What is IoT? -- 13.2.1 History of IoT -- 13.2.2 Background of IoT for COVID-19 Pandemic -- 13.2.3 Operations Involved in IoT for COVID-19 -- 13.2.4 How is IoT Helping in Overcoming the Difficult Phase of COVID-19? -- 13.3 Various Models Proposed for Managing a Pandemic Like COVID-19 Using IoT -- 13.3.1 Smart Disease Surveillance Based on Internet of Things -- 13.3.2 IoT PCR for Spread Disease Monitoring and Controlling -- 13.4 Global Technological Developments to Overcome Cases of COVID-19 -- 13.4.1 Noteworthy Applications of IoT for COVID-19 Pandemic -- 13.4.2 Key Benefits of Using IoT in COVID-19 -- 13.4.3 A Last Word About Industrial Maintenance and IoT -- 13.4.4 Issues Faced While Implementing IoT in COVID-19 Pandemic -- 13.5 Results & -- Discussions -- 13.6 Conclusion -- References -- 14 Mathematical Insight of COVID-19 Infection-A Modeling Approach -- 14.1 Introduction -- 14.1.1 A Brief on Coronaviruses. ; 14.2 Epidemiology and Etiology.
• Sommario/riassunto: ENABLING HEALTHCARE 4.0 for PANDEMICS The book explores the role and scope of AI, machine learning and other current technologies to handle pandemics. In this timely book, the editors explore the current state of practice in Healthcare 4.0 and provide a roadmap for harnessing artificial intelligence, machine learning, and Internet of Things, as well as other modern cognitive technologies, to aid in dealing with the various aspects of an emergency pandemic outbreak. There is a need to improvise healthcare systems with the intervention of modern computing and data management platforms to increase the reliability of human processes and life expectancy. There is an urgent need to come up with smart IoT-based systems which can aid in the detection, prevention and cure of these pandemics with more precision. There are a lot of challenges to overcome but this book proposes a new approach to organize the technological warfare for tackling future pandemics. In this book, the reader will find: State-of-the-art technological advancements in pandemic management; AI and ML-based identification and forecasting of pandemic spread; Smart IoT-based ecosystem for pandemic scenario. Audience The book will be used by researchers and practitioners in computer science, artificial intelligence, bioinformatics, data scientists, biomedical statisticians, as well as industry professionals in disaster and pandemic management.
• Titolo autorizzato: Enabling healthcare 4. 0 for pandemics
• ISBN: 1-119-76906-X; 1-119-76908-6; 1-119-76907-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910830744903321
• Lo trovi qui: Univ. Federico II