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Intelligent Data Analysis for COVID-19 Pandemic : Algorithms for Intelligent Systems - M. Niranjanamurthy

Intelligent Data Analysis for COVID-19 Pandemic

By: M. Niranjanamurthy (Editor), Siddhartha Bhattacharyya (Editor), Neeraj Kumar (Editor)

Hardcover | 23 June 2021

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Chapter 1. Machine Learning Based Ensemble Approach for Predicting the Mortality Risk of Covid-19 Patients: A Case Study.- Chapter 2. The Role of Internet of Health Things (IoHTs) & Innovative Internet of 5G Medical Robotic Things (IIo-5GMRTs) in COVID-19 Global Health Risk Management and Logistics Planning.- Chapter 3. Battling COVID-19 with Process Model of Integrated Digital Technology: An Analysis of Qualitative Data.- Chapter 4. High-fidelity intelligence ventilator to help infect with Covid-19 based on artificial intelligence.- Chapter 5. Boon of Artificial Intelligence in Diagnosis of Covid-19.- Chapter 6. Artificial Intelligence and Big Data Solutions for COVID-19.- Chapter 7. Modeling the Transmition Dynamics of COVID-19 Virus Disease in Nigeria.- Chapter 8. Emerging Trends in Higher Education during Pandemic Covid-19: An impact study From West Bengal.- Chapter 9. COVID-19: Virology, Epidemiology, Diagnostics and Predictive modelling.- Chapter 10. Improved Estimation in Logistic Regression through Quadratic Bootstrap Approach: An Application in Indian Agricultural e-learning System during COVID-19 Pandemic.- Chapter 11. COVID-19 and Stock Markets: Deaths and Strict Policies.- Chapter 12. Artificial Intelligence Techniques in Medical Imaging for Detection of Corona Virus (COVID-19 / SARS-COV-2): A Brief Survey.- Chapter 13. A Travelling Disinfection-man Problem (TDP) for COVID-19: A Nonlinear Binary Constrained Gaining-Sharing knowledge-based Optimization Algorithm.- Chapter 14. COVID-19 Lock down Impact on Mental Health: A Cross-sectional Online Survey from Kerala, India.- Chapter 15. Analysis, Modelling and Prediction of COVID-19 Outbreaks using Machine Learning Algorithms.
Industry Reviews
"Each article is self-contained with a detailed description of each method, background information, statistics and mathematical details, theory, and application. Anyone looking for inspiration could find several examples and use cases of available data that can be used for insight generation. ... The strength of this edited book is that the readers would become familiar with several data sources and demonstration of intelligent analysis of data that social scientist routinely collect." (Enayet Raheem, ISCB News, iscb.info, June, 2022)

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