Genetic Algorithm-Based Vaccine Optimization Technique to Control COVID-19

Another way to reduce the effects of COVID-19 is to develop vaccines to build viral immunity. Many contemporary assessments concentrate on the state and promise of this vaccine system, with geneticalgorithms for feature selection for the predictor being used as a replacement for the first data. While computing the distances to the preparation set examples, the predictors utilized in the count are the ones with no missing qualities for that example and no missing qualities in the preparation set. An intricating factor is that COVID-19 changes genetically and the indications are diverse which is developing it harder to focus on the peptides they contain to fit a sacked tree model for every indicator utilizing the preparation set examples. In this proposed method, we have applied the technique of genetic algorithm (GA) which finds the most vulnerable person who needs vaccine to save their lives. This searching process is taken through GA for better decision and applied for vaccine optimization. This study presents an attempt to distribute a genetic algorithm’s population onto the nodes of a complicated network, with crossover and mutation processes limited to the population members. The study given here uses a certain form of genetic algorithm as a tool for balancing the exploration–exploitation trade-off in order to investigate a specific aspect of a network, and it fits into that category. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

The COVID-19 pandemic situation had imposed unexpected changes in our day-to-day life, especially in terms of how we work and how we learn. Before COVID-19, e-learning methodologies were embraced only by inquisitive learners and by few corporate companies and educational institutions to disseminate knowledge through an alternative mode of content delivery. Although e-learning offers great flexibility to the learners to learn at their own convenient time and pace, by accessing the content via any smart device irrespective of their geographical location, it has got some limitations too due to which most of the teachers/students, as well as the educational institutions, prefer the traditional method of teaching/learning over online learning. In this paper, exclusive sentiment analysis has been carried out to assess the perception of the students on the adoption of online learning using a dataset that has been collected during the pre-COVID-19 pandemic period. The results of the analysis demonstrate that online learning was not as popular and widely adopted among the student’s fraternity as it is during this COVID-19 pandemic period. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.