A new spectrophotometric method has been developed for the assay of olanzapine (OLN.) in pure and dosage forms. The method is based on the diazocoupling of (OLN.) with diazotized p-nitroaniline in alkaline medium to form a stable brown colored water-soluble azo dye with a maximum absorption at 405 nm.  The variables that affect the completion of reaction have been carefully optimized. Beer’s law is obeyed over the concentration range of (0.5-45.0 μg.mL^-1) with a molar absorptivity of 1.5777×10⁴ L.mol^-1.cm^-1.  The limit of detection was  0.3148  Î¼g.mL^-1 and  Sandell’s  sensitivity value was 0.0198 μg.cm^-2. The propose

Nicotine was separated from eggplant and green pepper seeds (Solanaceous) by High Performance Liquid Chromatography (HPLC).The concentration of nicotine in the eggplant extract (0.871-0.877 μg/ml) was determined by injecting standard material with 0.5 and 1.5 μg/ml, while the concentrations of nicotine in green pepper extract (0.613-0.618 μg/ml) was determined when the standard material was injected with 0.5 and 1.5 μg/ml. The qualitative chemical data was calculated from derivations of the standard material. Nicotine concentration was measured qualitatively in both extracts through the calibration curve and method of the standard addition. This technique has high accuracy and compatibility, bringing the proportion of relati

To determine the relationship between celiac disease and reproductive disorder, twenty two women with recurrent spontaneous abortion (18-35) years have been investigated from the period 2017/11/1 – 2018/2/1 and compared wih twenty two parentally healthy women. All studied groups were carried out to measure antitissue transglutaminase IgA and IgG antibodies by Enzyme linked immunosorbent assay (ELISA) technique, There were a highly significant differences (P < 0.01) in the concentration of anti TtG IgA and IgG Ab compared to control group, while there was non-significant differences (P > 0.05) in the concentration of anti TtG IgA according to the age group and there was a significant difference (P < 0.05) in the concentration of anti TtG I

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

Finger vein recognition and user identification is a relatively recent biometric recognition technology with a broad variety of applications, and biometric authentication is extensively employed in the information age. As one of the most essential authentication technologies available today, finger vein recognition captures our attention owing to its high level of security, dependability, and track record of performance. Embedded convolutional neural networks are based on the early or intermediate fusing of input. In early fusion, pictures are categorized according to their location in the input space. In this study, we employ a highly optimized network and late fusion rather than early fusion to create a Fusion convolutional neural network

A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a

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

This study presents a rapid, sensitive, and straightforward approach to measure chlorpheniramine maleate (CPM) by using turbidity CFIA. The method involves CPM reacting with sodium nitroprusside (Nitropress) to produce a pale white precipitate. The NAG-SSP-5S1D analyzer was used to measure turbidity at 0°–180° angle to detect the attenuation of incident light as a result of collision on the surfaces of the precipitate particles. The linear range of CPM measurements was between 0.008 and 11 m.mol/L, with correlation coefficient of 0.9983 and R2% = 99.65. The limit of detection was determined to be 0.0328 µg/sample from the lowest concentration in the calibration curve, and the repeatability of the method (RSD%) was less than 0.4% (n = 6

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet