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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
ENHANCING ACCURACY IN OBJECT TRACKING USING NOVEL YOLO COMPARED WITH KNEAREST NEIGHBOUR
Main Article Content
Abstract
Aim: To detect the accuracy in object tracking using the K-Nearest Neighbour algorithm. Materials and Methods: This study contains 2 groups i.e.intervention YOLO algorithm and comparison KNearest Neighbour algorithm.Each group has a sample size of ten people, and the study parameters are alpha = 0.05, beta = 0.2, and power = 0.8.Their accuracies are also compared to one another using different sample sizes. Results: The Novel Yolo is 92.8370 more accurate than the K-Nearest Neighbour of 87.64% in Fake Review Detection. Conclusion: The Yolo model is significantly better than the LR in identifying Fake Review. It can be also considered as a better option for Fake News detection
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