Background: Strangles is a highly contagious equine respiratory disease caused by Streptococcus equi subsp. equi. It is a globally significant pathogen and one of the most common infectious agents in horses. In Iraq, no sequencing data on this pathogen are available, and only two molecular studies have been published to date. This study provides preliminary insights into strain diversity and provides a foundation for future large-scale investigations. Aim: This study aimed to investigate the molecular characteristics, identify SeM gene alleles, and perform a phylogenetic analysis of S. equi isolates from horses in Baghdad, Iraq. Methods: We analyzed 59 Streptococcus spp. isolates previously obtained from equine clinical sample

Antimicrobial resistance (AMR) is a serious challenge for infectious disease prevention and treatment, according to the World Health Organization. It is a worldwide problem caused primarily by inappropriate and insufficient therapy, misuse of antimicrobials without physician supervision, unnecessary hospital readmissions, and other factors. AMR has several consequences, including increased medical costs and mortality. The present study aimed to evaluate imipenem resistance in gram-negative bacteria in Central Pediatric Teaching Hospital in Baghdad, Iraq, and determine this bacteria resistance in different samples. Initially, a total of 100 different samples were collected from child patients from October 1, 2020, to August 31, 2021. Each is

Investigating the strength and the relationship between the Self-organized learning strategies and self-competence among talented students was the aim of this study. To do this, the researcher employed the correlation descriptive approach, whereby a sample of (120) male and female student were selected from various Iraqi cities for the academic year 2015-2016.  the researcher setup two scales based on the previous studies: one to measure  the Self-organized learning strategies which consist of (47) item and the other to measure the self-competence that composed of (50) item. Both of these scales were applied on the targeted sample to collect the required data

Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatio-temporal inputs. This article presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutil

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

Deep Learning Techniques For Skull Stripping of Brain MR Images

ABSTRACT

              Agricultural production, food security and safety, public health animal welfare, access to markets and alleviation of rural poverty have been achieved by controlling on veterinary services to prevent animal disease. World organization for animal health guidelines focus on controlling of animal disease which depends on good governance and veterinary services quality. The aim of veterinary services is controlling and preventing animal disease some of other aspects; it's responsibility of early detection, rapid response to outbreaks of emerging or re-emerging animal disease, optimizing quality and effectiveness of disease