Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu

The implementation of nanotechnology in all industries is one of most significant research fields. Nanoparticles have shown a promising application in subsurface fields. On the other hand, various surfactants have been used in the oil industry to reduce oil/water interfacial tension and also widely used to stabilize the nano-suspensions. The primary objective of this study was to investigate the improvements of surfactants ability in term of interfacial tension (γ) reduction utilizing addition of silicon dioxide nanoparticles at different temperatures and salinity. The pendant drop technique has been used to measure γ and electrical conductivity has been used to measure the critical micelle concentration (CMC). The synergistic effects of

Dora petroleum refinery waste water is the one of the important source of pollution by priority pollutant aromatic compound discharged to Tigris river in Iraq. the station has waste water treatment unit contains many treatment subunits The most important sub units is :skimmer units ,physiochemical unit ,daf unit, biological unit. The aim of research project is to study the ability of unit to remove the priority pollutant aromatic compound and follow up these compounds in river to study ability of river to self removal. A solid phase extraction (SPE) followed by high performance liquid chromatography-ultra violet (HPLC-UV) technique is depicted for the quantitative estimation of benzidines and phenols. Experimental studies were performed to