Application of artificial intelligence in Covid-19 / / Sachi Nandan Mohanty [and three others], editors

• Pubblicazione: Gateway East, Singapore : , : Springer, , [2021]; ©2021
• Descrizione fisica: 1 online resource (593 pages)
• Disciplina: 610.285
• Soggetto topico: Artificial intelligence - Medical applications; COVID-19; Intel·ligència artificial en medicina
• Soggetto genere / forma: Llibres electrònics
• Persona (resp. second.): MohantySachi Nandan
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- Foreword 1 -- Foreword 2 -- Preface -- Acknowledgements -- Contents -- About the Editors -- Part I: AI as a Source of Prides for Healthcare -- 1: Comprehensive Claims of AI for Healthcare Applications-Coherence Towards COVID-19 -- 1.1 Orientation of Artificial Intelligence in Healthcare Research -- 1.2 Correlated Investigational Analysis of AI Appliances in Healthcare System and Various Clinical Diseases -- 1.2.1 Disease Detection and Diagnosis -- 1.2.2 Automated Robert Treatment and Drug Design, Discovery -- 1.2.3 Healthcare Data Management Supported by Digital Managerial Application -- 1.2.4 AI in Public and Clinical Health -- 1.3 Motivational AI Devices for Healthcare -- 1.3.1 AI-Administered Devices with Machine Learning and Deep Learning -- 1.3.2 AI Attributed Devices with IOT -- 1.3.3 AI Supervised Devices with Big Data and Data Science -- 1.3.4 AI-Based Mining and NLP -- 1.3.5 AI-Enabled Expert System -- 1.4 Demand of AI for COVID-19 -- 1.4.1 Prior Alert Generation -- 1.4.2 Continuous Tracing and Following COVID-19 Symptoms -- 1.4.3 Diagnosis and Prognosis -- 1.4.4 Treatment and Possible Drug Design and Discovery -- 1.4.5 Control over Society and People with Guidelines -- 1.5 Conclusions and Future Work -- 1.6 Executive Summary -- References -- 2: Artificial Intelligence-Based Systems for Combating COVID-19 -- 2.1 Introduction -- 2.2 How Technology Can Help in Containing the Pandemic? -- 2.3 Technological Approach Vs Non-technological Approach of Treatment of COVID-19 -- 2.4 Existing Technologies to Detect/Diagnose the Virus -- 2.4.1 Non-contact Infrared Thermometers -- 2.5 Thermal Screening via Thermal Cameras -- 2.5.1 Symptom-Based Diagnosis -- 2.5.2 Ventilators -- 2.6 Means of Prevention from COVID-19 -- 2.6.1 Masks -- 2.6.2 Sanitizers/Hand Rub -- 2.6.3 Sanitizing Tunnels for Public Areas. ; 2.6.4 Washing Hands with Soap for 20s -- 2.6.5 Avoiding Handshakes -- 2.7 Use of Modern Technologies for Making Diagnosis Faster, Easier, and Effective -- 2.8 Proposed Techniques to Effectively Control the Rise in Cases of COVID-19 -- 2.8.1 Crowdsource-Based Applications -- 2.9 Conclusion -- References -- Part II: AI Warfare in COVID-19 Diagnosis, Detection, Prediction, Prognosis and Knowledge Representation -- 3: Artificial Intelligence-Mediated Medical Diagnosis of COVID-19 -- 3.1 Introduction -- 3.2 Pathogenesis and Diagnostic Windows -- 3.3 AI Assisted COVID-19 Diagnosis -- 3.3.1 Potential Application for Infection Detection -- 3.3.2 Application of AI on `Omics´ Big-Data -- 3.3.3 Use of AI on Radiology Data -- 3.4 Future Directions -- References -- 4: Artificial Intelligence (AI) Combined with Medical Imaging Enables Rapid Diagnosis for Covid-19 -- 4.1 Introduction -- 4.1.1 Reverse Transcription-Polymerase Chain Reaction -- 4.1.2 Isothermal Amplification Assays -- 4.1.3 Antigen Tests -- 4.1.4 Serological Tests -- 4.1.5 Rapid Diagnostic Tests (RDT) -- 4.1.6 Enzyme-Linked ImmunoSorbent Assay (ELISA) -- 4.1.7 Neutralization Assay -- 4.1.8 Chemiluminescent Immunoassay -- 4.2 AI-Based Diagnosis -- 4.2.1 Chest CT or X-ray CT Scans -- 4.2.2 Chest Radiography -- 4.2.2.1 Limitation -- 4.3 Other Predictive Measures for Covid-19 Diagnosis -- 4.3.1 Pulse Oximetry -- 4.3.2 Thermal Screening -- 4.4 Conclusions -- References -- 5: Role of Artificial Intelligence in COVID-19 Prediction Based on Statistical Methods -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Dataset Description -- 5.4 Experimental Results -- 5.4.1 Combinatorial (Quick) Approach -- 5.4.2 Stepwise Forward Selection Approach -- 5.4.3 Stepwise Mixed Selection Approach -- 5.4.4 GMDH Neural Network Approach -- 5.5 Comparison Between the Algorithms Based on MAE, RMSE, SD, Correlation. ; 5.6 Conclusion -- References -- 6: Data-Driven Symptom Analysis and Location Prediction Model for Clinical Health Data Processing and Knowledgebase Developmen... -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Rudiments of Random Forest Machine Learning Algorithm -- 6.4 Case Study for Symptom Analysis and Its Prediction with Random Forest Using COVID-19 WHO Data Set -- 6.4.1 Step Wise Experimental Result Analysis and Discussions -- 6.4.2 Calculation of Average Baseline Error -- 6.4.3 Classifying Into Zones -- 6.4.3.1 Setting Threshold Value -- 6.4.4 Color Attribute of Map with Zones (Green, Orange, and Red) -- 6.5 Augmented Enhancements to the Detection and Prediction Analysis for COVID 19 -- 6.5.1 Appending a New Drop-Down Menu in the Detection Page -- 6.6 Aligning Output of This Research as a Supplement to Heighten Up Healthcare and Public Health -- 6.7 Conclusions -- 6.8 Future Work -- References -- 7: A Decision Support System Using Rule-Based Expert System for COVID-19 Prediction and Diagnosis -- 7.1 Introduction -- 7.2 Background -- 7.2.1 Machine Learning-Based Data-Oriented Approach -- 7.2.2 Expert System-Based Knowledge-Oriented Approach -- 7.3 Overview of Expert System -- 7.3.1 Fundamentals -- 7.3.2 Expert System Architecture -- 7.3.3 Expert System Design Issues -- 7.4 Case Study: COVID-19 -- 7.4.1 Feasibility of Expert System on COVID-19 -- 7.4.2 Problem Description -- 7.4.3 Proposed Expert System: ESCOVID -- 7.4.3.1 Rule Set and Knowledgebase -- 7.4.3.2 Inference Mechanism -- 7.5 Implementation and Testing -- 7.6 Conclusion -- References -- 8: A Predictive Mechanism to Intimate the Danger of Infection via nCOVID-19 Through Unsupervised Learning -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Methodology -- 8.3.1 Data Collection -- 8.3.2 Relevant Dataset -- 8.3.3 Data Processing -- 8.3.3.1 Algorithm of Clustering -- 8.4 Result Analysis. ; 8.4.1 Overall Behavior of All Unsupervised Learning Model (Figs. 8.9 and 8.10) -- 8.5 Conclusion -- References -- 9: Artificial Intelligence-Enabled Prognosis Technologies for SARS-CoV-2/COVID-19 -- 9.1 Introduction -- 9.1.1 Epidemiology and Phylogeography of Pathogen -- 9.1.2 Human-to-Human Transmission -- 9.1.3 Clinical Phenotype Variations and Pathogenesis -- 9.2 Current Prognosis Practices -- 9.2.1 Diagnosis Services -- 9.2.2 Control Practices -- 9.2.2.1 Sanitization -- 9.2.2.2 Treatment -- 9.3 Challenges of SARS-CoV-2 -- 9.3.1 Phylogeography and Clinical Features -- 9.3.2 Mass Community and Healthcare Management -- 9.3.3 Transmission and Distancing -- 9.3.4 Diagnosis and Treatment -- 9.3.5 Disease Modeling Approaches -- 9.3.6 Data Security Concerns -- 9.4 Advanced Technologies -- 9.4.1 Internet of Things (IoT) -- 9.4.2 Artificial Intelligence (AI) -- 9.4.3 Databases and Analytics -- 9.4.4 Advanced Genomics and proteomics -- 9.4.5 Cloud Computing and Optimization -- 9.4.6 Digital Medicine and Healthcare -- 9.4.7 Biosensor and Bioelectronics -- 9.5 Integrated Technology and Logical Products -- 9.5.1 AI, Cloud, Sensor and IoT -- 9.6 AI-Enabled Prognosis Technology, Product, and Model Description -- 9.6.1 Technology and Product: AI Analysis and Program in Healthcare -- 9.6.2 Product and Technology: AI-Based sanitization Machine Using Cloud computing and Optimization -- 9.6.3 Product and Technology: IOT-Based AI-Enabled Touchless Hand Sanitizer Machine -- 9.6.4 Technology and Model: Prognosis Healthcare Model for Mass Community -- 9.6.4.1 Standard Prognosis Practices -- 9.7 Adaptation of AI-Enabled Technology and Disease Research -- 9.7.1 Hygiene, Distancing, and Virus Control -- 9.7.2 Understanding of Pathogenic Consequences -- 9.8 Conclusion -- 9.9 Future Prospects -- References. ; 10: Intelligent Agent Based Case Base Reasoning Systems Build Knowledge Representation in COVID-19 Analysis of Recovery of Inf... -- 10.1 Introduction -- 10.2 Related Work -- 10.3 COVID-19 -- 10.4 Symptom of COVID-19 -- 10.5 Artificial Intelligence -- 10.6 Machine Learning -- 10.7 Natural Language Processing -- 10.8 Robotics -- 10.9 Autonomous Vehicles -- 10.10 Vision -- 10.11 Clinical Artificial Intelligence -- 10.12 Expert System -- 10.13 Machine Learning -- 10.14 Intelligent Agent -- 10.15 Characteristic Agents -- 10.16 Clinical Intelligent Agent -- 10.17 Multi-Agent System -- 10.18 Java Agent Framework (JADE) -- 10.19 Clinical Multi-Agents -- 10.20 Case Base Reasoning -- 10.21 The CBR Cycle -- 10.22 JCOLIBRI -- 10.23 Clinical Case Base Reasoning Systems -- 10.24 Knowledge Base System -- 10.25 Clinical Knowledge Base System -- 10.26 Amalgamation OF CAI, CIA, CMAS, CCBR Using in KBSCOVID-19 Model -- 10.27 Implementation of MASCBR-Based Knowledge Base Patients Recovery from COVID-19 Pandemic -- 10.28 Conclusion -- 10.29 Future Work -- References -- Part III: Machine Learning Solicitation for COVID 19 -- 11: Epidemic Analysis of COVID-19 Using Machine Learning Techniques -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Pattern Identification for COVID-19 -- 11.4 Experiment Analysis -- 11.4.1 Dataset 1: Based on Geographic Distribution ( https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geogr... -- 11.4.1.1 Description of the Dataset -- 11.4.1.2 Correlation Between the Variables -- 11.4.1.3 Generating Heat Map of the Correlation -- 11.4.2 Dataset 2 ( https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv) -- 11.4.2.1 Snapshot of the dataset -- 11.4.2.2 Generating Pair Plot -- 11.5 Pattern Prediction of Covid-19 Using Machine Learning Approaches -- 11.6 Conclusions -- References. ; 12: Machine Learning Application in COVID-19 Drug Development.
• Titolo autorizzato: Application of artificial intelligence in Covid-19
• ISBN: 981-15-7317-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910502987803321
• Lo trovi qui: Univ. Federico II
• Serie: Medical virology (Springer (Firm))