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

A robust and sensitive analytical method is presented for the extraction and determination of six pharmaceuticals in freshwater sediments.

As long as the place in which a person lives has a meaning and temporal dimensions , memory is the main axis of these dimensions , today , city centers and old historical sectors of cities are abandoned , and began to turn into slums , the contradiction between old and historical sectors led cities to lose their identity while people lost their sense of belongingness to the old sectors where their ancestors used to live . The old city of Hilla used to have social , historical and cultural role on determining the identity . The study problem can be summarized as the ( lack of studies regarding the impact of historical memory related to Hilla old city on social and cultural mobility ) , the study hypothesis claims that the social , histori

98 samples were collected from various clinical sources included (Burns, wounds, urines, sputums, blood) From the city of Baghdad, After performing the biochemical and microscopic examination, 52 isolates were obtained for Pseudomonas aeruginosa, 17 (32.7%) isolates from burn infection, 12 (23%) isolates from Wound infection 11 (21.2%) isolates from urine infection, 7 (13.5%) isolates of sputum and 5 (9.6%) isolates from blood. Bacteria susceptibility to form biofilm has been detectedby microtiter plate method, The results showed that 80% of the bacterial isolates were produced the biofilm with different proportions, alg D gene (alginate production) has been detected by polymerase chain reaction (PCR) Which plays an essential role in the fo

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