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BIOCHEMISTRY OF FASTING – A REVIEW ON METABOLIC SWITCH AND AUTOPHAGY.
Volume - 13 | Issue-1
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ONE-POT ENVIRONMENT FRIENDLY SYNTHESIS OF IMINE DERIVATIVE OF FURFURAL AND ASSESSMENT OF ITS ANTIOXIDANT AND ANTIBACTERIAL POTENTIAL
Volume - 13 | Issue-1
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MODELING AND ANALYSIS OF MEDIA INFLUENCE OF INFORMATION DIFFUSION ON THE SPREAD OF CORONA VIRUS PANDEMIC DISEASE (COVID-19)
Volume - 13 | Issue-1
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INCIDENCE OF HISTOPATHOLOGICAL FINDINGS IN APPENDECTOMY SPECIMENS IN A TERTIARY CARE HOSPITAL IN TWO-YEAR TIME
Volume - 13 | Issue-1
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SEVERITY OF URINARY TRACT INFECTION SYMPTOMS AND THE ANTIBIOTIC RESISTANCE IN A TERTIARY CARE CENTRE IN PAKISTAN
Volume - 13 | Issue-1
EFFICIENT PREDICTION OF VULNERABILITY IN TWITTER USING ALEXNET IN COMPARISON OVER RESNET WITH IMPROVED ACCURACY
Main Article Content
Abstract
Aim: Efficient prediction of vulnerability in twitter using AlexNet in comparison over ResNet for improved accuracy. Materials and Methods: The AlexNet (N=10) and ResNet Algorithm (N=10) are two algorithms used in 2 Groups. 20 samples for both algorithms are considered and accuracy in this work is evaluated. Result and Discussion: Based on the results accuracy obtained is identified to be 98.0710% by AlexNet over the ResNet algorithm as 94.8%. Statistical significance difference between AlexNet algorithm and ResNet Algorithm is found to be p<0.05. Conclusion: The vulnerability prediction in twitter using AlexNet is found to be better when compared with ResNet.
Article Details
