Data Science for COVID-19 presents leading-edge research on data science techniques for the detection, mitigation, treatment and elimination of COVID-19. Sections provide an introduction to data science for COVID-19 research, considering past and future pandemics, as well as related Coronavirus variations. Other chapters cover a wide range of Data Science applications concerning COVID-19 research, including Image Analysis and Data Processing, Geoprocessing and tracking, Predictive Systems, Design Cognition, mobile technology, and telemedicine solutions. The book then covers Artificial Intelligence-based solutions, innovative treatment methods, and public safety. Finally, readers will learn about applications of Big Data and new data models for mitigation.
1. Data Science for COVID-19 - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xxi 7. Foreword - p. xxxi 8. Preface - p. xxxiii 9. Chapter 1 Predictive models to the COVID-19 - p. 1 10. Chapter 2 An artificial intelligence–based decision support and resource management system for COVID-19 pandemic - p. 25 11. Chapter 3 Normalizing images is good to improve computer-assisted COVID-19 diagnosis - p. 51 12. Chapter 4 Detection and screening of COVID-19 through chest computed tomography radiographs using deep neural networks. - p. 63 13. Chapter 5 Differential evolution to estimate the parameters of a SEIAR model with dynamic social distancing - p. 75 14. Chapter 6 Limitations and challenges on the diagnosis of COVID-19 using radiology images and deep learning - p. 91 15. Chapter 7 Deep convolutional neural network–based image classification for COVID-19 diagnosis - p. 117 16. Chapter 8 Statistical machine learning forecasting simulation for discipline prediction and cost estimation of COVID-19 pandemic - p. 147 17. Chapter 9 Application of machine learning for the diagnosis of COVID-19 - p. 175 18. Chapter 10 PwCOV in cluster-based web server - p. 195 19. Chapter 11 COVID-19–affected medical image analysis using DenserNet - p. 213 20. Chapter 12 uTakeCare - p. 231 21. Chapter 13 COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks - p. 255 22. Chapter 14 Lexicon-based sentiment analysis using Twitter data - p. 275 23. Chapter 15 Real-time social distance alerting and contact tracing using image processing - p. 297 24. Chapter 16 Machine-learning models for predicting survivability in COVID-19 patients - p. 317 25. Chapter 17 Robust and secured telehealth system for COVID-19 patients - p. 337 26. Chapter 18 A novel approach to predict COVID-19 using support vector machine - p. 351 27. Chapter 19 An ensemble predictive analytics of COVID-19 infodemic tweets using bag of words - p. 365 28. Chapter 20 Forecast and prediction of COVID-19 using machine learning - p. 381 29. Chapter 21 Time series analysis of the COVID-19 pandemic in Australia using genetic programming - p. 399 30. Chapter 22 Image analysis and data processing for COVID-19 - p. 413 31. Chapter 23 A demystifying convolutional neural networks using Grad-CAM for prediction of coronavirus disease (COVID-19) on X-ray images - p. 429 32. Chapter 24 Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images - p. 451 33. Chapter 25 Computational modeling of the pharmacological actions of some antiviral agents against SARS-CoV-2 - p. 467 34. Chapter 26 Received signal strength indication—based COVID-19 mobile application to comply with social distancing using bluetooth signals from smartphones - p. 483 35. Chapter 27 COVID-19 pandemic in India - p. 503 36. Chapter 28 Mathematical recipe for curbing coronavirus (COVID-19) transmition dynamics - p. 527 37. Chapter 29 Sliding window time series forecasting with multilayer perceptron and multiregression of COVID-19 outbreak in Malaysia - p. 547 38. Chapter 30 A two-level deterministic reasoning pattern to curb the spread of COVID-19 in Africa - p. 565 39. Chapter 31 Data-driven approach to COVID-19 infection forecast for Nigeria using negative binomial regression model - p. 583 40. Chapter 32 A novel machine learning–based detection and diagnosis model for coronavirus disease (COVID-19) using discrete wavelet transform with rough neural network - p. 597 41. Chapter 33 Artificial intelligence–based solutions for early identification and classification of COVID-19 and acute respiratory distress syndrome - p. 613 42. Chapter 34 Internet of Medical Things (IoMT) with machine learning–based COVID-19 diagnosis model using chest X-ray images - p. 627 43. Chapter 35 The growth of COVID-19 in Spain. A view based on time-series forecasting methods - p. 643 44. Chapter 36 On privacy enhancement using u-indistinguishability to COVID-19 contact tracing approach in Korea - p. 661 45. Chapter 37 Scheduling shuttle ambulance vehicles for COVID-19 quarantine cases, a multi-objective multiple 0–1 knapsack model with a novel Discrete Binary Gaining-Sharing knowledge-based optimization algorithm - p. 675 46. Index - p. 699
Data Science for COVID-19 presents leading-edge research on data science techniques for the detection, mitigation, treatment and elimination of COVID-19. Sections provide an introduction to data science for COVID-19 research, considering past and future pandemics, as well as related Coronavirus variations. Other chapters cover a wide range of Data Science applications concerning COVID-19 research, including Image Analysis and Data Processing, Geoprocessing and tracking, Predictive Systems, Design Cognition, mobile technology, and telemedicine solutions. The book then covers Artificial Intelligence-based solutions, innovative treatment methods, and public safety. Finally, readers will learn about applications of Big Data and new data models for mitigation.
1. Data Science for COVID-19 - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xxi 7. Foreword - p. xxxi 8. Preface - p. xxxiii 9. Chapter 1 Predictive models to the COVID-19 - p. 1 10. Chapter 2 An artificial intelligence–based decision support and resource management system for COVID-19 pandemic - p. 25 11. Chapter 3 Normalizing images is good to improve computer-assisted COVID-19 diagnosis - p. 51 12. Chapter 4 Detection and screening of COVID-19 through chest computed tomography radiographs using deep neural networks. - p. 63 13. Chapter 5 Differential evolution to estimate the parameters of a SEIAR model with dynamic social distancing - p. 75 14. Chapter 6 Limitations and challenges on the diagnosis of COVID-19 using radiology images and deep learning - p. 91 15. Chapter 7 Deep convolutional neural network–based image classification for COVID-19 diagnosis - p. 117 16. Chapter 8 Statistical machine learning forecasting simulation for discipline prediction and cost estimation of COVID-19 pandemic - p. 147 17. Chapter 9 Application of machine learning for the diagnosis of COVID-19 - p. 175 18. Chapter 10 PwCOV in cluster-based web server - p. 195 19. Chapter 11 COVID-19–affected medical image analysis using DenserNet - p. 213 20. Chapter 12 uTakeCare - p. 231 21. Chapter 13 COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks - p. 255 22. Chapter 14 Lexicon-based sentiment analysis using Twitter data - p. 275 23. Chapter 15 Real-time social distance alerting and contact tracing using image processing - p. 297 24. Chapter 16 Machine-learning models for predicting survivability in COVID-19 patients - p. 317 25. Chapter 17 Robust and secured telehealth system for COVID-19 patients - p. 337 26. Chapter 18 A novel approach to predict COVID-19 using support vector machine - p. 351 27. Chapter 19 An ensemble predictive analytics of COVID-19 infodemic tweets using bag of words - p. 365 28. Chapter 20 Forecast and prediction of COVID-19 using machine learning - p. 381 29. Chapter 21 Time series analysis of the COVID-19 pandemic in Australia using genetic programming - p. 399 30. Chapter 22 Image analysis and data processing for COVID-19 - p. 413 31. Chapter 23 A demystifying convolutional neural networks using Grad-CAM for prediction of coronavirus disease (COVID-19) on X-ray images - p. 429 32. Chapter 24 Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images - p. 451 33. Chapter 25 Computational modeling of the pharmacological actions of some antiviral agents against SARS-CoV-2 - p. 467 34. Chapter 26 Received signal strength indication—based COVID-19 mobile application to comply with social distancing using bluetooth signals from smartphones - p. 483 35. Chapter 27 COVID-19 pandemic in India - p. 503 36. Chapter 28 Mathematical recipe for curbing coronavirus (COVID-19) transmition dynamics - p. 527 37. Chapter 29 Sliding window time series forecasting with multilayer perceptron and multiregression of COVID-19 outbreak in Malaysia - p. 547 38. Chapter 30 A two-level deterministic reasoning pattern to curb the spread of COVID-19 in Africa - p. 565 39. Chapter 31 Data-driven approach to COVID-19 infection forecast for Nigeria using negative binomial regression model - p. 583 40. Chapter 32 A novel machine learning–based detection and diagnosis model for coronavirus disease (COVID-19) using discrete wavelet transform with rough neural network - p. 597 41. Chapter 33 Artificial intelligence–based solutions for early identification and classification of COVID-19 and acute respiratory distress syndrome - p. 613 42. Chapter 34 Internet of Medical Things (IoMT) with machine learning–based COVID-19 diagnosis model using chest X-ray images - p. 627 43. Chapter 35 The growth of COVID-19 in Spain. A view based on time-series forecasting methods - p. 643 44. Chapter 36 On privacy enhancement using u-indistinguishability to COVID-19 contact tracing approach in Korea - p. 661 45. Chapter 37 Scheduling shuttle ambulance vehicles for COVID-19 quarantine cases, a multi-objective multiple 0–1 knapsack model with a novel Discrete Binary Gaining-Sharing knowledge-based optimization algorithm - p. 675 46. Index - p. 699
