Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOM

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

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

This study aims at detecting the differences in genotyping of coding region fusA gene in clinical isolates of Acinetobacter baumannii from Baghdad, Iraq. Collected two hundred clinical samples (50 samples from urine, 50 samples from wound, 50 samples from sputum and 50 samples from otitis infections). Laboratory diagnosis for bacterial isolates carried out by some biochemical tests and confirmed by using VITEK- 2 compact system. The results appeared that twenty isolates of Acinetobacter baumannii in all these samples. Genotyping study was performed of coding region fusA gene of the extracted genome of all bacterial isolates and used specific primers in achieved amplification process of this target gene. DNA sequencing of this gene and alig

Probiotics are live microbes that give many health benefits to human beings and animals, the most studied and commonly used probiotics are Gram-positive bacteria; lactobacilli and bifidobacteria. At nowadays, Lactobacillus spp. constitute more than two-thirds of the total numbers of probiotic species. The present study aimed to characterize Lactobacillus that locally isolated from human mouth and feces as probiotics. A total of three Lactobacillus isolates; Lactobacillus fermentum Lb2, Lactobacillus rhamnosus Lb9, and Lactobacillus paracasei Lb10 were investigated in respect to acid and bile salts tolerance, antibiotics susceptibility, and cell surface hydrophobicity in vitro using bacterial adhesion to hydrocarbons method. In compa

The reason for applying the project is for the development of some physiological variables and skill performance using a device for the first time applied in Iraq to a sample of badminton players, and this device has a patent on 7/2/2019. An experimental method is used for a sample of (12) players from (Al-Orthodoxy club). The experimental group is applied the proposal technique, while, the control group is instructed by the coach.The results of the research attribute to the role of the proposal exercises of fit light technology, thus, this technology has increased the attention and focusing of the sample and some physiological variables and smash shot skill. It has been concluded that the exercises using fit light technology is helped to i

This study aims to evaluate the influence of the air abrasion of dentin on the shear bond strength of lithium disilicate using three different types of luting cements. Sixty cylindrical specimens were milled from lithium disilicate CAD/CAM blocks (IPSe.max CAD). Sixty sound human maxillary premolar teeth were decoronated to the level of peripheral dentin, then randomly divided into three groups according to the type of luting cement used for the cementation of the lithium disilicate specimens (n = 20); Group A: Glass ionomer cement (Riva Self- Cure); Group B: Adhesive resin cement (Rely X Ultimate); Group C: Self-adhesive resin cement (Rely X U200). Each group was then further subdivided into two subgroups (n=10); Subgroups AI, BI, and CI,