In 2020, Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) Coronavirus, unforeseen pandemic put humanity at big risk and health professionals are facing several kinds of problem due to rapid growth of confirmed cases. That is why some prediction methods are required to estimate the magnitude of infected cases and masses of studies on distinct methods of forecasting are represented so far. In this study, we proposed a hybrid machine learning model that is not only predicted with good accuracy but also takes care of uncertainty of predictions. The model is formulated using Bayesian Ridge Regression hybridized with an n-degree Polynomial and uses probabilistic distribution to estimate the value of the dependent variable instead of using traditional methods. This is a completely mathematical model in which we have successfully incorporated with prior knowledge and posterior distribution enables us to incorporate more upcoming data without storing previous data. Also, L² (Ridge) Regularization is used to overcome the problem of overfitting. To justify our results, we have presented case studies of three countries, −the United States, Italy, and Spain. In each of the cases, we fitted the model and estimate the number of possible causes for the upcoming weeks. Our forecast in this study is based on the public datasets provided by John Hopkins University available until 11th May 2020. We are concluding with further evolution and scope of the proposed model.

In the past decade, the number of malware variants has increased rapidly. Many researchers have proposed to detect malware using intelligent techniques, such as Machine Learning (ML) and Deep Learning (DL), which have high accuracy and precision. These methods, however, suffer from being opaque in the decision-making process. Therefore, we need Artificial Intelligence (AI)-based models to be explainable, interpretable, and transparent to be reliable and trustworthy. In this survey, we reviewed articles related to Explainable AI (XAI) and their application to the significant scope of malware detection. The article encompasses a comprehensive examination of various XAI algorithms employed in malware analysis. Moreover, we have addressed the characteristics, challenges, and requirements in malware analysis that cannot be accommodated by standard XAI methods. We discussed that even though Explainable Malware Detection (EMD) models provide explainability, they make an AI-based model more vulnerable to adversarial attacks. We also propose a framework that assigns a level of explainability to each XAI malware analysis model, based on the security features involved in each method. In summary, the proposed project focuses on combining XAI and malware analysis to apply XAI models for scrutinizing the opaque nature of AI systems and their applications to malware analysis.