Lack of accuracy in envisioning the spread of COVID-19 over online social networks based on geographical location identification using novel naive bayes algorithm comparing support vector machine algorithm
Arani Girish, A. Shri Vindhya · 2024
Aim: The goal of this research work is to intensify the efficiency percentage of geographical location identification to alleviate impact of covid using machine learning classifiers by comparing novel Naive Bayes (NB) algorithm and support vector machine (SVM) algorithm. Materials and Methods: NB algorithm with sample size = 10, G-power (value = 0.8) and SVM algorithm with sample size = 10 were predicted many times to evaluate the efficiency percentage. NB is evaluated by using its weights and configurations. Results and Discussion: NB algorithm has better efficiency (55%) when compared to SVM algorithm efficiency (36%). The results achieved with significance value p = 0.889 (p > 0.05) shows that two groups are statistically insignificant. Conclusion: NB algorithm performed significantly better than the SVM algorithm.