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

In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

This work studies the role of serum apelin-36 and Glutathione S-transferases (GST) activity in association with the hormonal, metabolic profiles and their link to the risk of cardiovascular disease (CVD) in healthy and patients' ladies with polycystic ovary syndrome (PCOS). A total of fifty-four (PCOS) patients and thirty-one healthy woman as a control have been studied. The PCOS patients were subdivided on the basis of body-mass-index (BMI), into 2-subgroups (the first group was obese-PCOS with BMI ≥ 30 and the second group was non-obese PCOS MBI<30). Fasting-insulin-levels and Lipid-profile, Homeostatic-model assessment-of-insulin-resistance (HOMA-IR), follicle-stimulating-hormone (FSH), luteinizing-hormone (LH), testosterone and

The aim of the present study is to highlight the role of total cholesterol (TC), triacylglycerol (TG), Glycated hemoglobin A1c and iron in Iraqi women with multiple sclerosis and also to examine the biochemical action of copaxone (which is the most widely used in the 21st century to treat multiple sclerosis) on these biochemical parameters. This is the first study in Iraq which deals copaxone action on TC , TG , HbA1c and iron. Ninety women in their fourth decade suffering from multiple sclerosis were enrolled in this study. They were divided into: the first (group B) composed of (30) women without any treatment related to multiple sclerosis or any treatment linked with chronic or inflammatory diseases. The second (group A1) included (30)

This research deals with the relationship between television advertising and buying random cosmetics, where we find that TV ads influence on the purchasing behavior of women, has conducted research in the field on a sample of women in the University of Baghdad, was a random sample taken from 150 different women in the age and social levels educational and cultural students and employees and teachers in order to sample representative be for the research community, and designed a questionnaire for this purpose form as a tool to collect data and information search and analyzed they answered the sample surveyed using a statistical program (spss) to extract percentages And correlation coefficients and testing square Kay , The study found Of w

This research aims to measure the productivity of nursing services and recognize the reality of providing such services in some public hospitals in the province of Diyala, as well as identify the most important factors affecting the productivity of nursing services, and developer measure adopted by the researcher (Kien, 2012), which includes seven factors (workforce , management team, stimulation, pressure of working hours (time), material / equipment, supervision, safety), Used the experimental method in the search, one of the private scientific research curriculum depends primarily on the scientific experiment and study of the subject, or a scientific phenomenon, which provides an opportunity to learn the facts

The aim of this paper is to design artificial neural network as an alternative accurate tool to estimate concentration of Cadmium in contaminated soils for any depth and time. First, fifty soil samples were harvested from a phytoremediated contaminated site located in Qanat Aljaeesh in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. The inputs are the soil depth, the time, and the soil parameters but the output is the concentration of Cu in the soil for depth x and time t. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Cadmium. The performance of the ANN technique was compared with the traditional laboratory inspecting

Objective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes. Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value. Results The evaluation is performed using