The increasing complexity of assaults necessitates the use of innovative intrusion detection systems (IDS) to safeguard critical assets and data. There is a higher risk of cyberattacks like data breaches and unauthorised access since cloud services have been used more frequently. The project's goal is to find out how Artificial Intelligence (AI) could enhance the IDS's ability to identify and classify network traffic and identify anomalous activities. Online dangers could be identified with IDS. An intrusion detection system, or IDS, is required to keep networks secure. We must create efficient IDS for the cloud platform as well, since it is constantly growing and permeating more aspects of our daily life. However, using standard intrusion

This paper offers a systemic review of the deep learning methods to detect violence on campus, which is a critical issue in intelligent surveillance to improve the student safety and prompt cut off of violent accidents. The review reviews studies published 2018-2025, concentrating on model structure to detect fights, bullying, vandalism, and aggressive behavior on problematic campuses due to occlusion and light variations and complicated human interactions. The research design includes a comparative study of different deep learning networks, such as CNNs, RNNs, 3D CNNs, attention-based networks, transformers, graph neural networks, neuro-fuzzy, and multimodal systems and federated learning methods. The paper also assesses benchmark

     In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets  namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant features from

Twenty five samples out of sixty wound swabs taken from burn patients were identified as P. aeruginosabacteria by conventional methods. Antibiotics susceptibility tests were performed against thirteen antibiotics. P. aeruginosa samples were treated with 0.5 mg/ml of Safranin O solution then irradiated with 532nm Q-switched Nd:YAG laser at four energy densities (0.324, 0.704, 1.380, and 1.831 J/cm2) for different times of 5, 8 and 11 minutes with 5Hz repetition rate. The viability, susceptibility to antibiotic and production of pyocyanin were determined before and after irradiation. The results showed that the number of CFU/ml of P. aeruginosa decreased with increasing the dose of irradiation. Complete killing of cells was observed at 1.8

In Present study, 25 clinical isolates of Proteus spp. of clinical samples, urine, wounds and burns collected from different hospitals in Baghdad city, all isolates were identified as Proteus mirabilis using different bacteriological media, biochemical assays and Vitek-2 system. It was found that 15 (60%) isolates were identifying as P. mirabilis. The susceptibility of P. mirabilis isolates to cefotaxime was 66.6 %, while to ceftazidime was 20%. Extended spectrum β-lactamses producing Proteus was 30.7 %. DNA of 5 isolates of P. mirabilis was extracted and detection for blaVEB-1 gene by using multiplex polymerase chain reaction (PCR). Results showed that the presence of this gene in all tested isolates, as an important indicator for increas

The purpose of the study is the city of Baghdad, the capital of Iraq, was chosen to study the spectral reflection of the land cover and to determine the changes taking place in the areas of the main features of the city using the temporal resolution of multispectral bands of the satellite Landsat 5 and 8 for MSS and OLI sensors respectively belonging to NASA and for the period 1999-2021, and calculating the increase and decrease in the basic features of Baghdad. The main conclusions of the study were, This study from 1999 to 2021 and in two different seasons: the Spring of the growing season and Summer the dry season. When using the supervised classification method to determine the differences, the results showed remarkable changes. Where h

Objective. Infection with Coxsackie virus. This virus that damages pancreatic cells, has long been linked to the onset of insulin-dependent diabetic mellitus (IDDM). Pro-inflammatory cytokines can be produced as a result of this illness. Tumor necrosis factor-a is one of these pro-inflammatory cytokines. Materials and Methods. Blood sample were collected from 180 Iraqi participants. Ninety of them is type 1 diabetic patients and other 90 is healthy control .both groups were tested for the incidence of Coxsackie virus B IgG. So the patients groups is divided to two groups according to sero positivity of CVB-IgG .all 180 patients tested to measure of level of TNF-α. Results. The Results showed increasing in levels of TNF-α in CBV po

Iraq suffers from serious pollution with harmful particles that have important direct and indirect effects on human activities and human health. In this research, a system for detecting pollutants in the air was designed and manufactured using infrared laser technology. This system was used to detect the presence of pollutants in the dust storms that swept the city of Baghdad which could have a negative impact on human health and living organisms.

The designed detection system based on the use of infrared laser (IR) with a wavelength of 1064 nm was used for the purposes of detecting pollutants based on the scattering of the laser beam from these pollutants. The system was aligned to obtain the best signal for the scattered rays, w