Simon Rasteau, Didier Ernenwein, Charles Savoldelli, Pierre Bouletreau
Artificial intelligence for oral and maxillo-facial surgery: A narrative review
Journal of stomatology, oral and maxillofacial surgery
challenge developing android malware detection framework identify malware realworld apps difficult academicians researchers vulnerability lies permission model android therefore attracted attention various researchers develop android malware detection model using permission set permissions academicians researchers used extracted features previous studies resulting overburdening creating malware detection models effectiveness machine learning model depends relevant features help reducing value misclassification errors excellent discriminative power feature selection framework proposed research paper helps selecting relevant features first stage proposed framework ttest univariate logistic regression implemented collected feature data set classify capacity detecting malware multivariate linear regression stepwise forward selection correlation analysis implemented second stage evaluate correctness features selected first stage furthermore resulting features used input development malware detection models using three ensemble methods neural network six different machinelearning algorithms developed models performance compared using two performance parameters fmeasure accuracy experiment performed using half million different android apps empirical findings reveal malware detection model developed using features selected implementing proposed feature selection framework achieved higher detection rate compared model developed using extracted features data set compared previously developed frameworks methodologies experimental results indicates model developed study achieved accuracy 988