Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on

Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing

Deepfake is a type of artificial intelligence used to create convincing images, audio, and video hoaxes and it concerns celebrities and everyone because they are easy to manufacture. Deepfake are hard to recognize by people and current approaches, especially high-quality ones. As a defense against Deepfake techniques, various methods to detect Deepfake in images have been suggested. Most of them had limitations, like only working with one face in an image. The face has to be facing forward, with both eyes and the mouth open, depending on what part of the face they worked on. Other than that, a few focus on the impact of pre-processing steps on the detection accuracy of the models. This paper introduces a framework design focused on this asp

Background. Echinococcosis/ hydatitdosis is a zoonotic parasitic disease caused by the infestation of the larval form of the tapeworm of the genus Echinococcus .The Liver, lungs, and kidneys are the common areas of infestation.Objectives: To describe hydatid disease in hospitalized patients from a clinico-epidemiological perspectives.Methods:: A retrospective study was conducted over a period of 6 months extending from 15th of November 2011 to the 15th of May 2012 by reviewing records of 125 patients who were hospitalized at Baghdad Teaching Hospital during 2011and received medical and surgical treatment for hydatid cyst disease. The information covered the socio-demographic and clinical characteristics of the patientsResults:.The presen

Background: Neonatal hyperbilirubinemia is a common disease in neonates especially in early days of birth that requires a good and successful treatment for reducing the severity and its complications that can produce important and irreversible effects.

Objectives: To evaluate the effectiveness of conventional phototherapy, intensive phototherapy and exchange transfusion on outcomes of neonatal jaundice at Fatima Al-Zahra Hospital for maternity and child care in Baghdad.

Patients & Methods: A retrospective study was carried out using medical records of neonates with diagnosis of unconjugated jaundice, admitted in the septic neonatal care unit of Fatima Al-Zahra hospital over 6 months period b

     Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem.  In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a

Anomaly detection is still a difficult task. To address this problem, we propose to strengthen DBSCAN algorithm for the data by converting all data to the graph concept frame (CFG). As is well known that the work DBSCAN method used to compile the data set belong to the same species in a while it will be considered in the external behavior of the cluster as a noise or anomalies. It can detect anomalies by DBSCAN algorithm can detect abnormal points that are far from certain set threshold (extremism). However, the abnormalities are not those cases, abnormal and unusual or far from a specific group, There is a type of data that is do not happen repeatedly, but are considered abnormal for the group of known. The analysis showed DBSCAN using the