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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
AN EFFICIENT QUALITY ANALYSIS OF RICE GRAINS USING SUPPORT VECTOR MACHINE OVER RANDOM FOREST WITH IMPROVED ACCURACY
Main Article Content
Abstract
Aim: The aim of the study is to use an eevcvgggfficient quality analysis of ricoverg gg d c c ains using a Novel Support Vector Machine over a Random Forest with improved accuracy. Materi als and Methods: Novel Support Vector ,Machine algorithm (N=10) and Randov vf RCC z,v (N=of .41% compared to the Random Forest of 80.37%. The statistical significance ovf the analysis of rice grains difference is p=0.001 (p<0.05) and Independent sample T-test value states that the results in the study are significant. Conclusion: The accuracy performance parameter of the Random Forest appears to be better than the Novel Support Vector Machine algorithm .
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