rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Intelligent Data Analysis for COVID-19 Pandemic
  name:
    name part: Niranjanamurthy, M.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Bhattacharyya, Siddhartha.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Kumar, Neeraj.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: si
    date issued: 2021
    edition: 1st ed. 2021.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XIX, 370 pages 156 illustrations, 105 illustrations in color.)
  abstract: This book presents intelligent data analysis as a tool to fight against COVID-19 pandemic. The intelligent data analysis includes machine learning, natural language processing, and computer vision applications to teach computers to use big data-based models for pattern recognition, explanation, and prediction. These functions are discussed in detail in the book to recognize (diagnose), predict, and explain (treat) COVID-19 infections, and help manage socio-economic impacts. It also discusses primary warnings and alerts; tracking and prediction; data dashboards; diagnosis and prognosis; treatments and cures; and social control by the use of intelligent data analysis. It provides analysis reports, solutions using real-time data, and solution through web applications details.
  table of contents: 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) and 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.
  note: edited by M. Niranjanamurthy, Siddhartha Bhattacharyya, Neeraj Kumar.
  subject:
    topic: Computational intelligence
  subject:
    topic: Artificial intelligence
  subject:
    topic: Statistics
  subject:
    topic: Health informatics
  subject:
    topic: Computational Intelligence
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Statistics, general
  subject:
    topic: Health Informatics
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Algorithms for Intelligent Systems
  identifier: 9789811615740
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 210622
    record change date: 20230330184929.6
    record identifier: in00004375900