Computational Intelligence, Machine Learning and Deep Learning Techniques for Effective Future Predictions of COVID-19: A Review

Abstract

As per the World Health Organization (WHO), Coronaviruses represents a huge virus family that creates diseases in humans/animals. The newly discovered coronavirus is known as Covid-19 (Cov-19). In December 2019, this virus broke out in Wuhan, China causing massive havoc worldwide. The design, development, theory and application of standards related to computation form the Computational Intelligence (CI) methods. Conventionally, the 3 key components of CI are the Artificial Neural Networks (ANN), Fuzzy System (FS), & Computation related to Evolution (EC). Lately, techniques like chaotic systems, support vector machines (SVM), etc. have been included into the CI techniques. Machine Learning (ML) enables systems to automatically learn without being programmed explicitly. Deep Learning (DL) represents a family of ML techniques based on ANN. A great potential has been observed while applying CI, ML, and DL techniques for predicting Cov-19. In this regard, the key objective of this chapter is to present an extensive review to the readers on how CI, ML, and DL techniques can be utilized to effectively predict Cov-19. The chapter deals with the review of the different CI, ML, and DL techniques such as ANN, FS, and EC that have been applied for Cov-19 prediction. The application and suitability of CI, ML, and DL techniques for screening and treating the patients, tracing the contacts along with Cov-19 forecasting is discussed in detail. A discussion of why certain CI, ML, and DL techniques are useful for the Cov-19 prediction is also presented.