Search

Aim: To improve the accuracy percentage of predicting misinformation about COVID-19 using SVM algorithm. Materials and methods: Support Vector Machine (SVM) with sample size = 20 and Decision Tree classification with sample size = 20 was iterated at different times for predicting the accuracy percentage of misinformation about COVID19. The Novel Poly kernel function used in SVM maps the dataset into higher dimensional space which helps to improve accuracy percentage. Results and Discussion: SVM has significantly better accuracy (94.48%) compared to Decision Tree accuracy (93%). There was a statistical significance between SVM and the Decision Tree (p=0.000) (p<0.05 Independent Sample T-test). Conclusion: SVM with Novel Poly kernel helps in predicting with more accuracy the percentage of misinformation about COVID-19. [ABSTRACT FROM AUTHOR]

Aim: To predict the accuracy percentage of At - risk students based on High withdrawal and Failure rate. Materials and methods: Logistic Regression with sample size = 20 and Generalised Linear Model (GLM) with sample size = 20 was iterated different times for predicting accuracy percentage of At - risk students. The Novel sigmoid function used in Logistic Regression maps prediction to probabilities which helps to improve the prediction of accuracy percentage. Results and Discussion: Logistic Regression has significantly better accuracy (94.48 %) compared to GLM accuracy (92.76 %). There was a statistical significance between Logistic regression and GLM (p=0.000) (p<0.05). Conclusion: Logistic Regression with Novel Sigmoid function helps in predicting with more accuracy percentage of At - risk students. [ABSTRACT FROM AUTHOR]