The virus that arises from Wuhan, popularly called as “coronavirus” has been spread all over the world in a short period. India has also taken preventive measures to control this threatening virus. In addition to precautions, it is necessary to analyze the risk factors of COVID-19 in overpopulated countries to reduce the impact of the virus. As India is the second-populated country, analyzing the risk factor of COVID-19 helps in categorizing the likely and non-likely people affect by the virus. The work manages the fuzziness through intuitionistic fuzzy sets combine with the VIKOR decision-making process to find the most influencing risk factors of COVID-19. The objective weights of the criteria are evaluated by entropy as it measures the randomness in discrete distribution. Moreover, sensitivity analysis is conducted to verify the robustness of the results of the proposed method.
Unmanned Aerial Vehicles (UAV) was introduced
after World War II. In 1980’s UAV consider as important weapon
system. Initially UAV needs initial position and target position. In
this paper bat algorithm is proposed with mixed objective
constraints which helps in directing the UAV. The process is
initialized by generating the initial population of bat. Then by
updating the population size and generation of bat the fitness
value with minimum frequency is found that helps to avoid
convergence among UAV. Finally the evaluation which gives
minimum frequency is considered as optimal solution.
Since 2019, COVID disease threats the world. It originated in China, and due to its easy spread characteristics, it spreads quickly among all the countries. As the world faced a new kind of virus, the government could not control its spread, and the fatality rate had also hugely increased. In 2021, the vaccine for the virus was introduced by various countries, which aids in controlling the spread and mortality rate. The mutant form of this virus exhibits high efficiency. So, this study analyzes the effective of COVID vaccines released after 2020 against all COVID variants through the fuzzy superiority and inferiority ranking (SIR) decision-making method. The vaccines and variants are analyzed by the [Formula: see text] matrix pattern under the fuzzy SIR-integrated weight method. The disease variants are taken as criteria and that are assigned integrated weight based on subjective, objective weight approaches. The intuitionistic fuzzy set-double parameter (IFS-DP) handles the vagueness by strictly including only membership and non-membership functions with the condition [Formula: see text]. As a result, the vaccines Moderna and Covishield are attained top rank and have a high likelihood to combat all the COVID variants compared to other vaccines. The reached outcomes are corroborated through sensitivity and comparative analysis. The sensitivity and comparative studies are measured by increasing the order of IFS-DP and extant fuzzy MCDM methods, respectively. The comparison of these results with those obtained from the proposed method is similar to the sensitivity study, along with minor variations observed in the comparative study. Therefore, the proposed method generates an efficient outcome. Moreover, the testing hypothesis technique is applied to check the efficiency of proposed method.
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