COVID-19 is now becoming a global issue and declared as pandemic by world health organization. This virus spread out from China to entire world. This paper performed a descriptive analysis of COVID-19 in special reference of India. Different attributes such as age, gender, travel history, communication type and current status are analyzed. Till now it can be stated from the study that age is not a significant factor that affect a person to be captured by this disease, also age attribute is normally distributed in current dataset. A significant relationship is found between gender (male and female) and Transmission Type (imported from other country or communicated from local) of the patients.

Quantum Cryptography is revolutionary discovery in the field of network security. Quantum cryptography promises to provide sophisticated functionality for security issues but it also leads to unbelievable increment in computational parallelism which is helpful for potential cryptanalytic attacks. Some of the associated properties of Quantum Key Distribution protocol that provide security that is deficient for the shared key to be transmitted securely. The identity verification process attempts to maximize success in interpreting the EPR protocol for distribution of Quantum.

Facts say that practical cryptographic systems are now within the range. Quantum cryptography generally gives the solution which uses the various methods of polarization to leave the transmitted data undisturbed. In this work we try to improve the data security by increase the key size shared between parties involved used in quantum cryptography. Quantum cryptography uses storing the split particles involved and then measuring them and creating what they use, eliminating the problem of unsafe storage.

The whole world is embroiling the pandemic situation caused by COVID-19, which is spreading across all countries. As of mid-May, COVID-19 continues to increase the number of people affected and the number of deaths in each country. Each country’s administrations concerned are making endless efforts to maintain public health, mental health and to regulate the rate of illness of COVID-19. Analysis of COVID-19 data using the machine learning paradigm is becoming a major interest of the researcher in these situations. Several researchers analyzed data from COVID-19 to predict infection, death, cured persons in the future, which may lead to the planning of each country’s regulatory authority to maintain the public health of its people. The machine learning algorithm provides more accurate results when the data size is large due to the lower number of data sets available to COVID-19, making the most accurate predictions a challenging task to implement the machine learning algorithm. This paper was essentially designed to predict the active rate, the death rate, and the cured rate in India by analyzing the data of COVID-19. There are three models of machine learning Support Vector Machine (SVM), Prophet Forecasting Model, and Linear Regression Model for predicting active rate, death rate and cured rate. Prophet Forecasting Model has been shown to be the best predictive method for predicting active rate, death rate and cured rate compared to SVM and Linear Regression when the vast uncertain and small data sets.

Wireless networks are gaining popularity now a days. A Vehicular Ad-hoc network that is able to configure all network devices, which means all devices work as host and as a router in network. For conveying all information only nodes help to each other. Vehicular ad hoc networks mostly formed temporary and comes in less infrastructure networks. Performance unit reduces caused by unstable channel position and network connection and mobility and resource bound. Improving in other performance unit, respective cross-layering techniques which are used where some other layers from protocol stack communications with each other via exchange of information. AODV is all good reactive ad hoc routing protocol. In this work, we recommend moderated version of AODV routing protocol, which all depend on route discovery through physical layers rather the minimum hop count approach that are algorithm of default distance vector. Research will elaborate how recommend model uses the received SINR and RSSI value to find its route. We will send this RSSI value to the network layer to find distance of node which is in range. It will calculate the optimal path. The focus on parameters like traffic rate of transfer, interruption, packet loss, time, link stableness, for increase overall lifetime of a network through optimal usage of battery.