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COVID-19 is a novel virus that spreads in multiple chains from one person to the next. When a person is infected with this virus, they experience respiratory problems as well as rise in body temperature. Heavy breathlessness is the most severe sign of this COVID-19, which can lead to serious illness in some people. However, not everyone who has been infected with this virus will experience the same symptoms. Some people develop cold and cough, while others suffer from severe headaches and fatigue. This virus freezes the entire world as each country is fighting against COVID-19 and endures vaccination doses. Worldwide epidemic has been caused by this unusual virus. Several researchers use a variety of statistical methodologies to create models that examine the present stage of the pandemic and the losses incurred, as well as considered other factors that vary by location. The obtained statistical models depend on diverse aspects, and the studies are purely based on possible preferences, the pattern in which the virus spreads and infects people. Machine Learning classifiers such as Linear regression, Multi-Layer Perception and Vector Auto Regression are applied in this study to predict the various COVID-19 blowouts. The data comes from the COVID-19 data repository at Johns Hopkins University, and it focuses on the dissemination of different effect patterns of Covid-19 cases throughout Asian countries. CA extern

The aim of the present study is to develop an algorithm by exploring the basic structure and functional operation of convolution neural network's (CNN) design as well as the features of back propagation (BP) and gradient descent (GD). The hierarchical approach employing BP and GD offers a reliable form of an algorithm. Most of the algorithms share weight to minimize the size of parameter and cost function that can be joined with the BP and the GD. Backpropagation provides an opportunity for backward feedback to enhance the reliability with minimizing error, while the gradient descent is used to solve the issues related with deep learning as well as the machine learning algorithms.

Human Biometrics is a rising technology, which has been broadly used in forensics, safe access and top-security prison. A biometric system is primarily a pattern recognition system that recognizes a person by determining the verification by using his different biological features i.e. Fingerprint, retina-scan, iris scan, hand geometry, and face recognition are important physiological biometrics and behavioral trait are Voice recognition, keystroke-scan, and signature-scan. In this paper different biometrics techniques such as Iris scan, retina scan and face recognition techniques are discussed.