.
-
BIOCHEMISTRY OF FASTING – A REVIEW ON METABOLIC SWITCH AND AUTOPHAGY.
Volume - 13 | Issue-1
-
ONE-POT ENVIRONMENT FRIENDLY SYNTHESIS OF IMINE DERIVATIVE OF FURFURAL AND ASSESSMENT OF ITS ANTIOXIDANT AND ANTIBACTERIAL POTENTIAL
Volume - 13 | Issue-1
-
MODELING AND ANALYSIS OF MEDIA INFLUENCE OF INFORMATION DIFFUSION ON THE SPREAD OF CORONA VIRUS PANDEMIC DISEASE (COVID-19)
Volume - 13 | Issue-1
-
INCIDENCE OF HISTOPATHOLOGICAL FINDINGS IN APPENDECTOMY SPECIMENS IN A TERTIARY CARE HOSPITAL IN TWO-YEAR TIME
Volume - 13 | Issue-1
-
SEVERITY OF URINARY TRACT INFECTION SYMPTOMS AND THE ANTIBIOTIC RESISTANCE IN A TERTIARY CARE CENTRE IN PAKISTAN
Volume - 13 | Issue-1
ACCURACY ANALYSIS IN THE PREDICTION OF EBUG TRACKING SYSTEM USING MULTINOMIAL NAIVE BAYES ALGORITHM COMPARE WITH DECISION TREE ALGORITHM
Main Article Content
Abstract
report it. Materials and methods: The performance analysis for maximum accuracy in Novel eNovel e-bugs prediction using Multinomial Naive Bayes Algorithm (n=10) over Decision Tree algorithm which identifies and measures the Novel e-bugs. Identification can be done using an image set to distinguish objects. The Gpower test used is 85% (g power setting parameters: α=0.05 and power = 0.85). Result: multinomial naive Bayes (98.16%) identifies the Novel e-bugs over the Decision Tree (97.97%) with a significance value of 0.429 (two-tailed, P>0.05). Conclusion: The accuracy of multinomial Naive Bayes is better when compared to the accuracy of the Decision Tree.
Article Details
