Development of an Improved Convolutional Neural Network for an Automated Face Based University Attendance System

Because of the flaws of the present university attendance system, which has always been time intensive, not accurate, and a hard process to follow. It, therefore, becomes imperative to eradicate or minimize the deficiencies identified in the archaic method. The identification of human face systems has evolved into a significant element in autonomous attendance-taking systems due to their ease of adoption and dependable and polite engagement. Face recognition technology has drastically altered the field of Convolution Neural Networks (CNN) however it has challenges of high computing costs for analyzing information and determining the best specifications (design) for each problem. Thus, this study aims to enhance CNN’s performance using Genetic Algorithm (GA) for an automated face-based University attendance system. The improved face recognition accuracy with CNN-GA got 96.49% while the face recognition accuracy with CNN got 92.54%

Childhood body mass index and perioperative complications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74152/1/j.1460-9592.2006.02140.x.pd

Viscosity Measurements of Two Potential Deepwater Viscosity Standard Reference Fluids at High Temperature and High Pressure

This paper reports high-pressure viscosity measurements for Krytox GPL 102 lot K2391 and tris­(2-ethylhexyl) trimellitate (TOTM). These two viscous liquids have recently been suggested as potential deepwater viscosity standard (DVS) reference fluids for high temperature, high pressure viscosity studies associated with oil production from ultradeep formations beneath the deepwaters of the Gulf of Mexico. The measurements are performed using a windowed, variable-volume, rolling-ball viscometer at pressures between 7 and 242 MPa and temperatures between 314 and 527 K with an expanded uncertainty of 3% at a 95% confidence level. The viscosity results are correlated using an empirical temperature/pressure-dependent function and a modified Vogel–Fulcher–Tammann (VFT) Equation. The present viscosity data for TOTM and Krytox GPL 102 lot K2391 are in good agreement with the available reported data in the literature at lower temperatures and pressures. The viscosity values of TOTM and Krytox GPL 102 lot K2391 are 9.5 mPa·s and 25 mPa·s, respectively, at 473 K and 200 MPa, whereas the desired DVS viscosity value at this condition is 20 mPa·s. Although the viscosity of Krytox GPL 102 lot K2391 is closer to the targeted value, a comparison of the present viscosity results with data obtained for lot K1537 indicates a very large lot-to-lot variation of the viscosity for this polydisperse perfluoropolyether oil, which represents a significant deficiency for a DVS

Effect of Isomeric Structures of Branched Cyclic Hydrocarbons on Densities and Equation of State Predictions at Elevated Temperatures and Pressures

The <i>cis</i> and <i>trans</i> conformation of a branched cyclic hydrocarbon affects the packing and, hence, the density, exhibited by that compound. Reported here are density data for branched cyclohexane (C6) compounds including methylcyclohexane, ethylcyclohexane (ethylcC6), <i>cis</i>-1,2-dimethylcyclohexane (<i>cis</i>-1,2), <i>cis</i>-1,4-dimethylcyclohexane (<i>cis</i>-1,4), and <i>trans</i>-1,4-dimethylcyclohexane (<i>trans</i>-1,4) determined at temperatures up to 525 K and pressures up to 275 MPa. Of the four branched C6 isomers, <i>cis</i>-1,2 exhibits the largest densities and the smallest densities are exhibited by <i>trans</i>-1,4. The densities are modeled with the Peng–Robinson (PR) equation of state (EoS), the high-temperature, high-pressure, volume-translated (HTHP VT) PREoS, and the perturbed chain, statistical associating fluid theory (PC-SAFT) EoS. Model calculations highlight the capability of these equations to account for the different densities observed for the four isomers investigated in this study. The HTHP VT-PREoS provides modest improvements over the PREoS, but neither cubic EoS is capable of accounting for the effect of isomer structural differences on the observed densities. The PC-SAFT EoS, with pure component parameters from the literature or from a group contribution method, provides improved density predictions relative to those obtained with the PREoS or HTHP VT-PREoS. However, the PC-SAFT EoS, with either set of parameters, also cannot fully account for the effect of the C6 isomer structure on the resultant density