An Improved Accuracy in Anticipating the User Nature using a novel logistic regression algorithm throughout the pandemic across online social media based on Indian metrics over Support Vector Clustering Algorithm

This study's primary goal is to apply machine learning classifier techniques to raise the intensity percentage of user nature detection in order to detect the impact of coronavirus on Twitter users by comparing Novel Logistic Regression and Support Vector Clustering algorithms. Materials and Methods: The accuracy percentage with a confidence interval of 95% and G-power (value =0.8) was determined many times using the LR method with test size =10 and the SVC algorithm with test size =10. The likelihood that an item belongs to one category or another is predicted using a LR model. Support Vector Clustering algorithm generates a line or hyperplane that divides the data into categories. Results and Discussion: LR model has greater efficiency (91%) when compared to Support Vector Clustering (59%). Two groups are numerically unimportant, according to the data obtained with a coefficient of determination of p=0.121 (p>0.05). Conclusion: LR performs substantially better than the Support Vector Clustering. © 2022 IEEE.

An Improved Efficiency in Envisioning the Personage Traits over Online Social Media based on Indian Metrics during Pandemic using Novel Naive Bayes Classifier Algorithm Comparing with Logistic Regression Algorithm

Aim: The primary aim of this research is to increase the intensity percentage of personage traits detection to reveal the impact of coronavirus on Twitter users by utilizing machine learning classifier algorithms by comparing Novel Naive Bayes Classifier algorithm and Logistic Regression algorithm. Material(s) and Method(s): Naive Bayes Classifier algorithm with test size=10 and Logistic Regression algorithm with test size=10 was estimated several times to envision the efficiency percentage with confidence interval of 95% and G-power (value=0.8). Naive Bayes classifier compares whether a specific feature in a class is unrelated to another feature. A logistic regression model predicts the probability of an item belonging to one group or another. Results and Discussion: Naive Bayes algorithm has greater efficiency (86%) when compared to Logistic Regression efficiency (60%). The results achieved with significance value p=0.169 (p>0.05) shows that two groups are statistically insignificant. Conclusion(s): Naive Bayes Algorithm executes remarkably greater than the Logistic Regression algorithm. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

An Improved Efficiency in Envisioning the Personage Traits over Online Social Media based on Indian Metrics during Pandemic using Novel Naive Bayes Classifier Algorithm Comparing with Logistic Regression Algorithm

Aim: The primary aim of this research is to increase the intensity percentage of personage traits detection to reveal the impact of coronavirus on Twitter users by utilizing machine learning classifier algorithms by comparing Novel Naive Bayes Classifier algorithm and Logistic Regression algorithm. Materials and Methods: Naive Bayes Classifier algorithm with test size=10 and Logistic Regression algorithm with test size=10 was estimated several times to envision the efficiency percentage with confidence interval of 95% and G-power (value=0.8). Naive Bayes classifier compares whether a specific feature in a class is unrelated to another feature. A logistic regression model predicts the probability of an item belonging to one group or another. Results and Discussion: Naive Bayes algorithm has greater efficiency (86%) when compared to Logistic Regression efficiency (60%). The results achieved with significance value p=0.169 (p>0.05) shows that two groups are statistically insignificant. Conclusion: Naive Bayes Algorithm executes remarkably greater than the Logistic Regression algorithm.