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_a10.1007/978-981-16-1574-0 _2doi |
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_aRA644.C67 _b2021 EB |
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_aIntelligent Data Analysis for COVID-19 Pandemic _cedited by M. Niranjanamurthy, Siddhartha Bhattacharyya, Neeraj Kumar. |
| 250 | _aFirst edition 2021 | ||
| 264 | 1 |
_aSingapore _bSpringer International Pulishing _c2021 |
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| 300 |
_a1 recurso en línea (XIX, 370 páginas) _b156 ilustraciones, 105 ilustraciones a color |
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_2rdacontent _aTexto _btxt |
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_2rdamedia _aelectrónico _bc |
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_2rdacarrier _arecurso electrónico _bcr |
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| 347 |
_aarchivo de texto _bPDF |
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| 490 | 0 |
_aAlgorithms for Intelligent Systems _x2524-7573 |
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| 490 | 0 | _aIntelligent Technologies and Robotics (SpringerNature-42732) | |
| 490 | 0 | _aIntelligent Technologies and Robotics (R0) (SpringerNature-43728) | |
| 505 | 0 | _aChapter 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. | |
| 520 | 3 | _aThis 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. | |
| 988 | _aSpringer_Robotics_2021 | ||
| 650 | 7 |
_2embne _9676716 _aCentros de proceso de datos |
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| 650 | 7 |
_2embne _9141180 _aProceso de datos |
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| 650 | 7 |
_2embne _9495511 _aDatos masivos |
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| 700 | 1 |
_aNiranjanamurthy, M. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9682638 |
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| 700 | 1 |
_aBhattacharyya, Siddhartha, _d1975- _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9100498 |
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| 700 |
_aKumar, Neeraj _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9682639 |
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| 776 | 0 | 8 |
_iPrinted edition: _z9789811615733 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811615757 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811615764 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-1574-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
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