Fingerprint Recognition in Biometric Security -A State of the Art

Today, because of the vulnerability of standard authentication system, law-breaking has accumulated within the past few years. Identity authentication that relies on biometric feature like face, iris, voice, hand pure mathematics, handwriting, retina, fingerprints will considerably decrease the fraud. so that they square measure being replaced by identity verification mechanisms. Among bioscience, fingerprint systems are one amongst most generally researched and used. it\'s fashionable due to their easy accessibility. during this paper we tend to discuss the elaborated study of various gift implementation define strategies together with their comparative measures and result analysis thus as realize a brand new constructive technique for fingerprint recognition

A Fingerprint Identification Approach using Neural Networks

Today, because of the vulnerability of standard authentication system, law-breaking has accumulated within the past few years. Identity authentication that relies on biometric feature like face, iris, voice, hand pure mathematics, handwriting, retina, fingerprints will considerably decrease the fraud, so that they square measure being replaced by identity verification mechanisms. Among bioscience, fingerprint systems are one amongst most generally researched and used. it\'s fashionable due to their easy accessibility. Moreover in this work the system modified to an adaptive system i.e intelligent by using neural networks

Fingerprint Recognition in Biometric Security -A State of the Art

Today, because of the vulnerability of standard authentication system, law-breaking has accumulated within the past few years. Identity authentication that relies on biometric feature like face, iris, voice, hand pure mathematics, handwriting, retina, fingerprints will considerably decrease the fraud. so that they square measure being replaced by identity verification mechanisms. Among bioscience, fingerprint systems are one amongst most generally researched and used. it\'s fashionable due to their easy accessibility. during this paper we tend to discuss the elaborated study of various gift implementation define strategies together with their comparative measures and result analysis thus as realize a brand new constructive technique for fingerprint recognition

Artificial neural networks model for predicting the behavior of different injection pressure characteristics powered by blend of biofuel‐nano emulsion

Abstract This investigation deals with the usage of graphene oxide (GO) nanoparticles with orange peel biodiesel in a conventional CI engine. The different fuel blends used for this experiment are biodiesel 10% + diesel 80% + ethanol 5% + surfactant 5% + GO 50 ppm (B10), biodiesel 20% + diesel 70% + ethanol 5% + surfactant 5% + GO 50 ppm (B20), biodiesel 50% + diesel 40% + ethanol 5% + surfactant 5% + GO 50 ppm (B50) and B100. The addition of ethanol has dual benefits for improving the vaporization of fuel blends and reduction of oxides of nitrogen (NOx) emission. Span80 and Tween80 were chosen as surfactants based on hydrophilic‐lipophilic balance numbers. It is useful for improving the homogeneity of immiscible fuel blends. From this study, the injection pressure (IP) was varied from 180, 200 to 220 bar for better atomization characteristics of nano additive biodiesel blend. The experimental results indicated that an increase in the percentages of biodiesel beyond 20% in the blend, NOx increases, and hydrocarbon (HC) and carbon dioxide (CO) emissions were found to be decreased. It is also observed that the highest brake thermal efficiency (BTE) was found for fuel 20 at the IP of 220 bar. The addition of nano additive has a great influence on fuel droplets and reduction of NOx emission levels. B20 blend was found to higher cylinder pressure and heat release rate when compared to other nano additive blends. Finally, indicated that the nano additive B20 blend is a better alternative to conventional fuel in unmodified CI engines. The RMSE value for the BTE, brake‐specific energy consumption (BSEC), CO, HC, NOx, and smoke is obtained to be 0.036, 0.0216, 0.044, 0.041, 0.0446, and 0.0435, respectively. The mean absolute percentage error value for the BTE, BSEC, CO, HC, NOx, and smoke is obtained to be 1.12, 0.84, 3.04, 4.12, 1.71, and 0.97, respectively