The title compound was synthesized by 2:1 condensation between adamantan-1-ylamine and benzene-1,4- dicarbaldehyde in n-BuOH and produced a good yield 87% of new bis Schiff base. The compound skeleton was affirmed by FTIR, ¹H NMR, LC-MS, and X-ray powder diffraction. The structure was solved by a parallel tempering process and refined by using Rietveld refinement. Two adamantan-1-ylimino groups are connected in the anti-positions to the planar central 1,4-dimethylbenzene group. All rings of the adamantyl group possess normal chair conformation.

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

Background: Few updated retrospective histopathological-based studies in Iraq evaluate a comprehensive spectrum of oro-maxillofacial lesions. Also, there was a need for a systematic way of categorizing the diseases and reporting results in codes according to the WHO classification that helps occupational health professionals in the clinical-epidemiological approach.

Objectives: to establish an electronic archiving database according to the ICD-10 that encompasses oro-maxillofacial lesions in Sulaimani city for the last 12 years, then to study the prevalence trend and correlation with clinicopathological parameters.

Subjects and Methods:  A descri

     Ovarian cancer is a heterogeneous disease with disparities in clinical performance and consequences. It is a cluster of numerous subtypes with diverse biological topographies that cause alterations in response to treatments, relapse rates, and endurance. This task was designed to investigate the epidemiology of the diagnosed cases of ovarian cancer from 2014 to 2020 in Baghdad. A total of 51 cases of different ovarian cancer samples were collected from Al-Elwea Maternity Hospital and Medical City Teaching Hospital, Baghdad. Clinical information including patients' age, tumor size and location, pathological grade and stage were also collected. Results revealed high incidence of OC in patients at age of ˂55 years for the rate 59%

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

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

The growth of developments in machine learning, the image processing methods along with availability of the medical imaging data are taking a big increase in the utilization of machine learning strategies in the medical area. The utilization of neural networks, mainly, in recent days, the convolutional neural networks (CNN), have powerful descriptors for computer added diagnosis systems. Even so, there are several issues when work with medical images in which many of medical images possess a low-quality noise-to-signal (NSR) ratio compared to scenes obtained with a digital camera, that generally qualified a confusingly low spatial resolution and tends to make the contrast between different tissues of body are very low and it difficult to co

     Breast cancer (BC) is the most common malignancy in women worldwide and a major cause of cancer-related deaths for women in Iraq. This assignment was created to investigate the characteristics of BC diagnosed in Baghdad from 2018 to 2021. A total of eighty-nine of paraffin embedded tissue blocks of different breast tissue tumors (71 females and 18 males) with their data, were collected from archive of Histopathology Department, Teaching Laboratories of Medical City, Al-Yarmouk Teaching Hospital, and a private laboratory in Baghdad-Iraq. The clinical information regarding age, gender, tumor size, tumor stage and grade, lymph nodes metastasis, in addition to the findings of estrogen receptor (ER), progesterone receptor (PR), human