Accurate estimation, diagnosis, and prevention of COVID-19 is a global challenge for healthcare organizations. Innovative measures can introduce and implement AI, and Mathematical Modeling applications. This book provides insight into the recent advances of applications, statistical methods, and mathematical modeling for the healthcare industry.
This book covers the state-of-the-art applications of AI and Machine Learning in past epidemics, pandemics, and COVID-19. It offers recent global case studies, and discusses how AI and statistical methods, initiatives, and applications such as Machine Learning, Deep Learning, Correlation and Regression Analysis play a major role in the prediction, diagnosis, and prevention of a pandemic. It will also focus on how AI and statistical applications can facilitate and restructure the healthcare system.
This book is written for Researchers, Students, Professionals, Executives, and the general public.
Deep Learning for COVID-19 Infection’s Diagnosis, Prevention, and Treatment
Artificial Intelligence in Coronavirus Detection—Recent Findings and Future Perspectives
Solutions of Differential Equations for Prediction of COVID-19 Cases by Homotopy Perturbation Method
Predictive Models of Hospital Readmission Rate Using the Improved AdaBoost in COVID-19
Nigerian Medical Laboratory Diagnosis of COVID-19; from Grass to Grace
COVID-19 CT Image Segmentation and Detection: Review
Interactive Medical Chatbot for Assisting with COVID-related Queries
COVID-19 Outbreak Prediction After Lockdown, Based on Current Data Analytics
A Deep Learning CNN Model for Genome Sequence Classification
The Impact of Lockdown Strategies on COVID-19 Cases with a Confined Sentiment Analysis of COVID-19 Tweets
A Mathematical Model and Forecasting of COVID-19 Outbreak in India
Automatic Lung Infection Segmentation of COVID-19 in CT Scan Images
A Review of Feature Selection Algorithms in Determining the Factors Affecting COVID-19
Industry 4.0 Technology-based Diagnosis for COVID-19