No abstract

COVID-19 had already spread throughout the world, and the novel coronavirus continues to pose a threat to the majority of countries. The current study uses the Susceptible-Exposed- Infectious-Recovered idea to assess the effects of social and economic factors, particularly the use of a medical mask, on the spread of COVID-19 in Andhra Pradesh. The influence of environmental parameters such as temperature and relative humidity on the number of COVID-19 cases per day is also investigated using numerical methods such as the Response surface methodology model. We provide the results of the curfew lockdown started by the Government of Andhra Pradesh for COVID19, as compared to a total lockdown scenario. As a result of the irresponsibility and crowded gatherings, the number of cases increases, stretching the mitigation period of the second wave COVID-19 spread, prolonging the curve's straightening. The Susceptible-Exposed- Infectious-Recovered model's predictions have been put to the test in a number of real-world scenarios. The fast spread of second-wave COVID-19 cases in Indian cities is similarly connected to temperature, as indicated by the well function of higher temperatures in breaking the lipid layer of coronavirus, but is severely inhibited by the critical component of social distancing, leading to uncertainty. As a result, it's critical to incorporate environmental factors into epidemiological models like Susceptible-ExposedInfectious-Recovered, as well as methodically design managed laboratory tests and modeling experiments to catch conclusive findings, assisting decision-makers and investors in developing comprehensive action plans to combat COVID-19's second wave

Wireless sensor networks (WSNs) have recently acquired prominence in a variety of applications such as remote monitoring and tracking. Since it is virtually hard to recharge the nodes in their remote deployment, also, the transmission of data from nodes to the base station requires a significant amount of energy. Thus, our research proposes a routing protocol, namely hybrid falcon-improved ACO Nature-Inspired Optimization using a deep learning model to reduce energy consumption while increases the network lifetime. In the developed model, initially, the falcon optimization technique is utilized to locate the best possible cluster heads in the quickest possible time. Furthermore, to improve the quality of service in routing optimization a new improved ACO has been proposed in which linear flexible operator and the premier operator are used to increasing the iteration speed. Finally, the optimum route is obtained through DBNN based on predicted energy. As a result, our proposed model gives a lifetime as 121 s and energy consumption as 0.041 J at 500 rounds when compared to the baseline approaches. Therefore, our proposed approaches provides better routing and improves the QoS as well as the energy consumption which increases the longevity of mobile nodes.