1. The Corona Virus Disease 2019 (COVID-19) has been declared a worldwide pandemic, and a key method for diagnosing COVID-19 is chest X-ray imaging. Suppose that we have dataset of 448 COVID-19 chest X-rays and 569 normal chest X-rays. Dataset have differences in gender, age, ethnicity, and nationality. Furthermore, the dataset contains chest X-ray images of the same person at various stages of the disease, as symptoms differ at different levels of COVID-19 infection. I want you to propose an algorithm for a general classification framework for COVID-19 chest X-ray images with noisy labels. What classification technique would you use, why? Which technique would you use for the high performance of your big data? What would you use to simulate the noise? What metrics would you use to evaluate the effectiveness of your algorithm? Explain in detail. (50pts) 3. The inability to define reasonably behaved functions, which can exactly transform the entire input image to the base at more than a modest number of landmarks but still maintain reasonable behavior in between the landmark points is the major weakness of the global spatial transformations. So, to overcome this problem what spatial transform we can use? (10 pts) 5. Write a MATLAB code to create two different data vectors. Find mean values. Calculate covariance on the data. Calculate the principal components after getting eigenvector and eigenvalues. Display the data on the original axes. Display PCs as data in rotated space. (20 pts)