Intelligent data analysis for COVID-19 pandemic / / M. Niranjanamurthy, Siddhartha Bhattacharyya, Neeraj Kumar, editors

• Pubblicazione: Singapore : , : Springer, , [2021]; ©2021
• Descrizione fisica: 1 online resource (377 pages)
• Disciplina: 362.1962414
• Soggetto topico: COVID-19 (Disease) - Economic aspects; COVID-19 (Disease) - Health aspects
• Persona (resp. second.): NiranjanamurthyM; BhattacharyyaSiddhartha; KumarNeeraj
• Nota di contenuto: Intro -- Preface -- Contents -- Editors and Contributors -- Machine Learning-Based Ensemble Approach for Predicting the Mortality Risk of COVID-19 Patients: A Case Study -- 1 Introduction -- 2 Literature Review -- 3 Dataset and Methodology Used -- 3.1 Dataset Description and Preparation -- 3.2 Data Preprocessing -- 3.3 Classification and Ensembling Approaches -- 4 Ensembling Approaches -- 4.1 Boosting -- 4.2 Bagging -- 5 Experiments and Results -- 5.1 Feature Selection of Patient Attributes -- 5.2 Performance of Individual Classifiers -- 6 Conclusion -- References -- Role of Internet of Health Things (IoHTs) and Innovative Internet of 5G Medical Robotic Things (IIo-5GMRTs) in COVID-19 Global Health Risk Management and Logistics Planning -- 1 Introduction -- 1.1 Background of the Study -- 1.2 Aims and Objective of the Study -- 2 Literature Review -- 3 Research Design and Implementation -- 3.1 Research Analysis -- 3.2 Research Discussion -- 4 Future Research Focus -- 5 Recommendation -- 6 Conclusion -- References -- Battling COVID-19 with Process Model of Integrated Digital Technology: An Analysis of Qualitative Data -- 1 Introduction -- 2 Research Design and Structure -- 3 Digital Technology to Combat COVID-19 -- 3.1 Mobile Applications and COVID-19 -- 3.2 Artificial Intelligence, Internet of Things (IoT), Big Data Analytics, and COVID 19 -- 3.3 Social Media and COVID -- 4 Summarization of Digital Technology Applications and COVID-19 -- 5 Process Model of Integrated Digital Technology -- 6 Conclusion -- References -- High-Fidelity Intelligence Ventilator to Help Infect with COVID-19 Based on Artificial Intelligence -- 1 Introduction -- 2 Operating and Revision Modes -- 3 Design and Condition of the Instrument -- 4 Materials and Technology -- 4.1 Typical Parts Needed -- 4.2 Arduino Nano Compatible V3.0 ATmega328. ; 4.3 Electronic Motor Speed Controller -- 4.4 Wi-Fi Module -- 5 Results and Discussion -- 6 Conclusion -- References -- Boon of Artificial Intelligence in Diagnosis of COVID-19 -- 1 Introduction -- 2 Novel Coronavirus (SARS-CoV-2) -- 3 Evolution of Artificial Intelligence -- 3.1 Strong AI -- 3.2 Weak AI -- 4 Applications of Computational Techniques -- 4.1 Speed Up Diagnosis -- 4.2 Computerized Tracking -- 4.3 Tracking of Infected Individual -- 4.4 Prediction of Incidence Rate and Mortality Rate -- 4.5 Designing and Development of New Drugs and Vaccines -- 4.6 Lowering the Work Load -- 4.7 Prevention of Infectious Disease -- 5 Traditional Diagnostic Methodology -- 5.1 Lateral Flow Immunoassay (LFIA) -- 5.2 Chemiluminescent Immunoassay (CLIA) -- 5.3 Neutralization Assay -- 6 Machine Learning -- 6.1 Algorithms -- 6.2 Random Forest -- 7 Contact Tracing -- 8 Detection Through Smell -- 9 Conclusion -- References -- Artificial Intelligence and Big Data Solutions for COVID-19 -- 1 Introduction -- 2 The COVID-19 Pandemic -- 3 AI and Big Data Techniques for COVID-19 -- 4 AI and Big Data Applications for COVID-19 -- 4.1 Early Detecting and Finding COVID-19 Cases -- 4.2 Early Detecting and Finding COVID-19 Cases -- 4.3 Following Up Contacts -- 4.4 Projection of Cases and Moralities -- 4.5 Reducing the Workload on Healthcare Workers -- 4.6 Prevention of the Infections -- 5 A Proposed Model of AI and Big Data for COVID-19: Smartphone for Surveillance -- 6 Discussions -- 7 Future Insights -- 8 Conclusions -- References -- Emerging Trends in Higher Education During Pandemic Covid-19: An Impact Study from West Bengal -- 1 Introduction -- 2 Research Background Literature -- 3 Methodology -- 3.1 Research Gaps -- 3.2 Research Objectives -- 3.3 Sample Design -- 3.4 Research Approach -- 3.5 Research Tools Usage in Current Research -- 3.6 SXUK Case Study Process. ; 4 Research Findings and Discussion -- 4.1 Teaching-Learning Context During Covid-19 -- 4.2 Content Development Orientation -- 4.3 ICT Technology Strategies in HEIs -- 4.4 "Big Five" Strategies -- 4.5 Technology-CI Adapted Teaching-Learning Strategies -- 4.6 Higher Educational Institutes Wise -- 4.7 CI Awareness and Application in HEIs -- 4.8 Computational Intelligence-ICT Factors Confluence -- 4.9 CI-Based HEIs Cluster Membership -- 5 Case Organization: SXUK -- 5.1 Introduction -- 5.2 Historical Millstones of SXUK -- 5.3 SXUK Organogram -- 5.4 AI-CI Interface Strategies for SXUK -- 6 Conclusion -- References -- COVID-19: Virology, Epidemiology, Diagnostics and Predictive Modeling -- 1 Introduction -- 2 Virology of SARS-CoV-2 -- 3 Diagnostics and Current Line of Treatment of Coronavirus Disease-2019 (COVID-19) -- 4 Comparison of Population Distribution of India, USA and Spain -- 5 Mathematical Modeling -- 6 Concluding Remarks -- References -- Improved Estimation in Logistic Regression Through Quadratic Bootstrap Approach: An Application in Indian Agricultural E-learning System During COVID-19 Pandemic -- 1 Introduction -- 2 Logistic Regression Model -- 2.1 Preliminaries -- 2.2 Identification of the Most Influential Variable -- 2.3 Estimation in Logistic Regression Model -- 2.4 Goodness of Fit -- 2.5 Predictive Ability -- 2.6 Comparison Measures -- 3 Empirical Results -- 3.1 Data and Implementation -- 3.2 Comparative Assessment Between MLE and Quadratic Bootstrap Estimation -- 3.3 Outcomes of the Simulation Study -- 4 Conclusion -- References -- COVID-19 and Stock Markets: Deaths and Strict Policies -- 1 Introduction -- 2 COVID-19 and Its Macroeconomic Effects -- 3 COVID-19 and Stock Markets -- 4 Data and Econometric Model -- 4.1 Diagnostic Statistics and Correlation Analysis -- 4.2 Analysis Results -- 5 Conclusion -- References. ; Artificial Intelligence Techniques in Medical Imaging for Detection of Coronavirus (COVID-19/SARS-COV-2): A Brief Survey -- 1 Introduction -- 2 Literature Survey -- 3 Artificial Intelligence -- 4 Machine Learning -- 5 Neural Networks -- 5.1 Deep Learning -- 5.2 Transfer Learning -- 5.3 Convolutional Neural Networks -- 6 CNN Algorithms and Methods Used in the Survey -- 6.1 Inception V3 -- 6.2 ResNet-50 -- 6.3 Inception-ResNet-v2 -- 6.4 VGG-19 -- 6.5 MobileNet -- 7 Materials and Methods -- 7.1 Dataset -- 7.2 Performance Analysis Parameters -- 8 Results and Discussions -- 9 Conclusion and Future Challenges -- References -- A Travelling Disinfection-Man Problem (TDP) for COVID-19: A Nonlinear Binary Constrained Gaining-Sharing Knowledge-Based Optimization Algorithm -- 1 Coronavirus (COVID-19): An Overview -- 2 Coronavirus Decontamination Planning Process -- 3 Coronavirus Travelling Disinfection-Man Problem (TDP) -- 4 The Travelling Salesman Problem (TSP) and Its Variations -- 5 Mathematical Model Formulation for the Travelling Disinfection-Man Problem -- 6 Real Application Case Study Application: Ain Shams University, Cairo -- 7 Artificial Intelligence Techniques in Optimization -- 8 Proposed Methodology -- 8.1 Overview of Gaining-Sharing Knowledge-Based Optimization Algorithm (GSK) -- 8.2 Discrete Binary Gaining-Sharing Knowledge-Based Optimization Algorithm (DBGSK) -- 9 Experimental Results -- 10 Conclusions -- 11 Points for Future Researches -- References -- COVID-19 Lock Down Impact on Mental Health: A Cross-Sectional Online Survey from Kerala, India -- 1 Introduction -- 1.1 Motivation for Doing the Research -- 2 Review of Literature -- 3 Methods -- 4 Results and Discussions -- 4.1 Types of Activities -- 4.2 Mental Health Issues and Eating Behaviour -- 4.3 Awareness Among People -- 5 Conclusion -- References. ; Analysis, Modelling and Prediction of COVID-19 Outbreaks Using Machine Learning Algorithms -- 1 Introduction -- 2 COVID-19 Around the Global -- 3 Machine Learning and Its Types -- 3.1 Supervised Learning -- 4 Implementation -- 4.1 Evaluation Metrics -- 5 Time Series Data set -- 5.1 Analysis, Modelling and Prediction of COVID-19 -- 5.2 Confirmed Cases and Death Cases as on 20 July 2020-World -- 5.3 Confirmed Cases and Death Cases as on 20th July 2020-India -- 5.4 Model of Machine Learning Algorithm -- 5.5 Predicting the Outgrowth in the Next 3 Months-India -- 6 Conclusion -- References -- Tracking and Analysis of Corona Disease Using Intelligent Data Analysis -- 1 Introduction -- 2 AI Versus COVID-19 -- 2.1 Prediction and Data Sharing -- 2.2 R& -- D Sector -- 2.3 Deception -- 2.4 Monitoring -- 2.5 Data Overload -- 2.6 Arrangement of Automated Vehicles -- 2.7 Variances Between the AI Techniques [1] -- 3 Using AI to Detect, Respond, and Recover from COVID-19 -- 3.1 Computer-Based Intelligence for COVID-19 Medical Response -- 4 AI for COVID-19 Social Control -- 4.1 Man-Made Reasoning in the Battle Against COVID-19 -- 4.2 Information Access -- 4.3 Security Ensuring Applications -- 5 How Artificial Intelligence Applications can Contain Coronavirus COVID-19 -- 5.1 Man-Made Reasoning in the Battle Against COVID-19 -- 5.2 Information Access -- 5.3 Security Ensuring Applications -- 6 Conclusion -- References -- Index.
• Titolo autorizzato: Intelligent data analysis for COVID-19 pandemic
• ISBN: 981-16-1574-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910488705703321
• Lo trovi qui: Univ. Federico II
• Serie: Algorithms for Intelligent Systems