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

The first aim of the present study was performed to assay the activity of arginase in sera of women with uterine fibroid.. This study consisted of(50) women with uterine fibroid as patient's group and (30) healthy women as control group. The age ranged between (30-55) years for the two groups. The results showed that highly significant increas (P< 0.0001) in the arginase activity in sera of women with uterine fibroid (7.99± 0.23) I.U/L is found when compared with healthy group (0.52±0.02) I.U/L. The second aim was performed to isolate arginase from sera of women with uterine fibroids. The purification is done by addition of ammonium sulfate, dialysis, gel filtration chromatography by using sephadex G-50 and ion exchange chromatography by

Background: Knowledge is considered to be essential for developing healthy practices and preventing the main oral diseases. In some developing countries, women were at higher risk to develop these diseases. This study was conducted to evaluate women’s dental knowledge and practices through a specific questionnaire and the relationship with patient’s educational level and the number of their children. Subjects and method: Women, aged from 25-35 years old, were selected to participate in the current study. They were attending dental clinics in the teaching hospital of Baghdad University. Each participant was instructed to answer questionnaire sheet which is previously prepared in Arabic language by the authors. The total numb

The first aim of the present study was performed to assay the activity of arginase in sera of women with uterine fibroid.. This study consisted of(50) women with uterine fibroid as patient's group and (30) healthy women as control group. The age ranged between (30-55) years for the two groups. The results showed that highly significant increase (P< 0.0001) in the arginase activity in sera of women with uterine fibroid (7.99± 0.23) I.U/L is found when compared with healthy group (0.52±0.02) I.U/L. The second aim was performed to isolate arginase from sera of women with uterine fibroids. The purification is done by addition of ammonium sulfate, dialysis, gel filtration chromatography by using sephadex G-50 and ion exchange chromatography

Background: Menopause can bring oral health problems and also associated with significant adverse changes in the orofacial complex. After menopause, women become more susceptible to periodontal disease due to deficiency of estrogen hormone. Current study aimed to evaluate the periodontal health status in relation to salivary constituent including pH, flow rate and some elements (Magnesium, Calcium and inorganic phosphorus) of pre and post-menopause women. Materials and Methods: Periodontal health status of 52 women aged 48-50 years old (26 pre-menopause and 26 post-menopause) were examined including (gingival index, plaque index, calculus index, probing pocket depth and clinical attachment level). Salivary sample was collected for two women

The aims of study is to detect the inhibitory effect of Saccharomyces boulardii and Lactobacillus acidophilus on Escherichia coli that has been isolated from recurrent urinary tract infection in women. The sensitivity of E.coli isolates to antibiotics had been studied and the most resistant E.coli isolate to antibiotics had been studied .The cup assay was used on nutrient agar and Muller-Hinton agar to detect the inhibitory activity for each S.boulardii yeast grown on YEGP media and L.acidophilus grown on MRS media in which the result showed a high inhibition activity for each of them .Also in this study the adhesion property of E.coli had been evaluated in the presence of S.boulardii at concentration of 1×109 and L.acidophilus at conc

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

This work is divided into two parts first part study electronic structure and vibration properties of the Iobenguane material that is used in CT scan imaging. Iobenguane, or MIBG, is an aralkylguanidine analog of the adrenergic neurotransmitter norepinephrine and a radiopharmaceutical. It acts as a blocking agent for adrenergic neurons. When radiolabeled, it can be used in nuclear medicinal diagnostic techniques as well as in neuroendocrine antineoplastic treatments. The aim of this work is to provide general information about Iobenguane that can be used to obtain results to diagnose the diseases. The second part study image processing techniques, the CT scan image is transformed to frequency domain using the LWT. Two methods of contrast

Abstract

 Metal cutting processes still represent the largest class of manufacturing operations. Turning is the most commonly employed material removal process. This research focuses on analysis of the thermal field of the oblique machining process. Finite element method (FEM) software DEFORM 3D V10.2 was used together with experimental work carried out using infrared image equipment, which include both hardware and software simulations. The thermal experiments are conducted with AA6063-T6, using different tool obliquity, cutting speeds and feed rates. The results show that the temperature relatively decreased when tool obliquity increases at different cutting speeds and feed rates, also it

This study explores the challenges in Artificial Intelligence (AI) systems in generating image captions, a task that requires effective integration of computer vision and natural language processing techniques. A comparative analysis between traditional approaches such as retrieval- based methods and linguistic templates) and modern approaches based on deep learning such as encoder-decoder models, attention mechanisms, and transformers). Theoretical results show that modern models perform better for the accuracy and the ability to generate more complex descriptions, while traditional methods outperform speed and simplicity. The paper proposes a hybrid framework that combines the advantages of both approaches, where conventional methods prod