Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different

For the period from February 2014 till May 2014, one hundred and nine lactose fermenter clinical isolates from different samples (urine, stool, wound swab, blood, and sputum) were collected from Alyarmok, Alkadimiya, and Baghdad teaching hospitals at Baghdad governorate. Identification of all Klebsiella pneumoniae isolates were carried out depending on macroscopic, microscopic characterizations, conventional biochemical tests, and Api 20E system. Fifty-three (48.62%) isolates represented K. pneumoniae; however, 51.73% represented other bacteria. Susceptibility test was achieved to all fifty-three K. pneumoniae isolates using five antibiotic disks (Ceftazidime, Ceftriaxone, Cefotaxime, Imipenem, and Meropenem). Most of tested isolates (90

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

Persistence of antibiotics in the aquatic environment has raised concerns regarding their potential influence on potable water quality and human health. This study analyzes the presence of antibiotics in potable water from two treatment plants in Baghdad City. The collected samples were separated using a solid-phase extraction method with hydrophilic-lipophilic balance (HLB) cartridge before being analyzed. The detected antibiotics in the raw and finished drinking water were analyzed and assessed using high-performance liquid chromatography (HPLC), with fluorometric detector and UV detector. The results confirmed that different antibiotics including fluoroquinolones andB-lactams were detected in the raw an

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

The paper include study the effect thickness of the polymeric sample which is manufactured by thermo press way. The sample was used as an active tunable R6G laser media. The remarks show that, when the thickness of the samples is increased, with the same concentration, the spectrum will shift towards the short wavelength, & the quantum fluorescence yield will increased. The best result we obtained for the quantum fluorescence yield is (0.68) at the sample, with thickness (0.304mm) in Ethanol solvent, while when we used the Pure Water as a solvent, we found that the best quantum fluorescence yield is (0.63) at (0.18mm) thickness of the sample.

The paper include studies the effect of solvent of dye doped in polymeric laser sample which manufactured in primo press way, which is used as an active (R6G) tunable dye lasers. The remarks show that, when the viscosity of the solvent (from Pure Water to Ethanol), for the same concentration and thickness of the performance polymeric sample is increased, the absorption spectrum is shifts towards the long wave length (red shift), & towards short wave length (blue shift) for fluorescence spectrum, also increased the quantum fluorescence yield. The best result we obtained for the quantum fluorescence yield is (0.882) with thickness (0.25mm) in Ethanol solvent in concentration (2*10-3mole/liter), while when we used the Pure Water as a solvent,

Expansive soil is one of the most serious problems that face engineers during the execution of any infrastructure projects. Soil stabilization using chemical admixture is one of the most traditional and widespread methods of soil improvement. Nevertheless, soil improvement on site is one of the most economical solutions for many engineering applications. Using construction and demolishing waste in soil stabilization is still under research., The aim of this study is to identify the effect of using concrete demolishing waste (CDW) in soil stabilization. Serious tests were conducted to investigate the changes in the geotechnical properties of the natural soil stabilized with CDW. From the results, it is concluded that the