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

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

     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

Background: Saliva is a specific bio-fluid with important biomarkers. Analyzing any alternation in these markers could give valuable information, in relation to oral health status parameters. The aim of this study was to investigate the level of α -amylase in unstimulated whole saliva of healthy, primary school children in relation to some oral health parameters. Materials and Methods: A questionnaires consisted of demography and medical histories of participants were filled by children families. Saliva samples were collected for 5- minutes between 9:00 -11:00 AM from 114 healthy students aged 6-13 years, divided into four age groups. Flow- rate, Plaque and Gingival Index were assessed and dentition status was investigated by DMFT/dmft

The Department of Chemical and Biological Engineering, Al-Khwarizmi College of Engineering at Baghdad University has lately renovated its own research laboratories to comply with international safety measures and conduct undergraduate and postgraduate research. In this regard, the department has harnessed some amenities within the college to establish these laboratories taking into accounts creating a convenient, safe, and developed working environment for both researchers and students. A precise procedure was followed to establish this laboratory which includes providing new bench tops which offer spacious working places for workers. These benches were supplied with power points, gas, water, and compressed air outlets. In addition,