Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the eff

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

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

In the present work, radon gas concentrations in different surface soil samples in Baghdad governorate were measured using RAD-7 detector. The results have been shown that, the Radon gas concentrations ranged between (41.67±1.78Bq/m3), to (185.67±4.22Bq/m3), a map showing the distribution of the concentration of radon in selected areas was defined to identify areas with high pollution level. The reason for the high concentration of radon is that these surface soil samples are taken from agricultural areas. It is also known that fertilizers contain uranium levels as well as areas bombed in wars in the country. It is worth noting that all radon concentrations in Baghdad governorate are below the recommended minimum of 200-300Bq/m3) (Inte

Back ground: Visceral leishmaniasis is endemic in the
middle and south of Iraq, it involves mostly infants. The
disease is observed mainly among rural areas that are far
from equipped medical centers. Therefore, there is a need
for anon- invasive, cost- effective, reliable, easily
available and fast method of diagnosis of this dngerous
disease.
Objective: The aim was to compare the validity and
predictive values of the recombinant K39 antigen (rK39)
test with that of the indirect fluorescent antibody test
(IFAT) test (the usual laboratory method) in the detection
of visceral leishmaniasis.
Methods: A Cross-sectional study was done in ALMansour Pediatric Hospital (in AL-Rusafa), and Central
Pediatric

The ground level ozone concentration at different locations in Baghdad city was identified. Five
different sites have been chosen to identify the ground level ozone concentration. Al- Dora and Al-
Za'afarania were chosen as areas contained point source ( power plant station ) in addition to high traffic
load , while Al –Uma park, Aden square and Al-Mawal square were chosen as area contained heavy
traffic only (line source). The measurement focuses on spring and fall because these periods display
favorable meteorology to ozone formation. During the research period the maximum values (peaks) for
ground level ozone concentration were observed at fall: at Al-Za'afarania area 101ppb as an average, at
Al-Dora 87 ppb as a