With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusion Detection System (IDS). Success is measured by a variety of metrics, including accuracy, precision, recall, F1-Score, and execution time. Applying feature selection approaches such as Analysis of Variance (ANOVA), Mutual Information (MI), and Chi-Square (Ch-2) reduced execution time, increased detection efficiency and accuracy, and boosted overall performance. All classifiers achieve the greatest performance with 99.99% accuracy and the shortest computation time of 0.0089 seconds while using ANOVA with 10% of features.
The purpose of this study was to know the reality of motivational administrative methods for academic decision-makers in the faculties of physical education and sports sciences in Baghdad from the perspective of faculty members. To solve the nature of the current problem, the two researchers used the descriptive approach of the survey method. The two researchers determined their research community by limiting the sample to all faculty members in the faculties of physical education and sports sciences in Baghdad (Al-Mustansiriya University, Al-Jadriya Univeristy & Al-Waziriyah University). Their number reached 314 faculty members and the two researchers determined their research community by 90%, so the research sam
... Show MoreThe analysis of the classic principal components are sensitive to the outliers where they are calculated from the characteristic values and characteristic vectors of correlation matrix or variance Non-Robust, which yields an incorrect results in the case of these data contains the outliers values. In order to treat this problem, we resort to use the robust methods where there are many robust methods Will be touched to some of them.
The robust measurement estimators include the measurement of direct robust estimators for characteristic values by using characteristic vectors without relying on robust estimators for the variance and covariance matrices. Also the analysis of the princ
... Show MoreAs the process of estimate for model and variable selection significant is a crucial process in the semi-parametric modeling At the beginning of the modeling process often At there are many explanatory variables to Avoid the loss of any explanatory elements may be important as a result , the selection of significant variables become necessary , so the process of variable selection is not intended to simplifying model complexity explanation , and also predicting. In this research was to use some of the semi-parametric methods (LASSO-MAVE , MAVE and The proposal method (Adaptive LASSO-MAVE) for variable selection and estimate semi-parametric single index model (SSIM) at the same time .
... Show MoreMethods of estimating statistical distribution have attracted many researchers when it comes to fitting a specific distribution to data. However, when the data belong to more than one component, a popular distribution cannot be fitted to such data. To tackle this issue, mixture models are fitted by choosing the correct number of components that represent the data. This can be obvious in lifetime processes that are involved in a wide range of engineering applications as well as biological systems. In this paper, we introduce an application of estimating a finite mixture of Inverse Rayleigh distribution by the use of the Bayesian framework when considering the model as Markov chain Monte Carlo (MCMC). We employed the Gibbs sampler and
... Show MoreOne of the main causes for concern is the widespread presence of pharmaceuticals in the environment, which may be harmful to living things. They are often referred to as emerging chemical pollutants in water bodies because they are either still unregulated or undergoing regulation. Pharmaceutical pollution of the environment may have detrimental effects on ecosystem viability, human health, and water quality. In this study, the amount of remaining pharmaceutical compounds in environmental waters was determined using a straightforward review. Pharmaceutical production and consumption have increased due to medical advancements, leading to concerns about their environmental impact and potential harm to living things due to their increa
... Show MoreThe antiviral activity of leaf extracts from Datura stramonium and tomato plants inoculated with TMV, combined with 20% skimmed milk, was investigated. A TMV isolate was confirmed using bioassay, serological, and molecular approaches and subsequently used to inoculate plants. Tomato plants, both pre- and post-inoculated with TMV, were sprayed with leaf extracts from either TMV-free or infected plants, alone or mixed with 20% skimmed milk. Enzyme-linked immunosorbent assay (ELISA) using tobamovirus-specific antibodies and local lesion tests were conducted to assess antiviral activity based on virus concentration and infectivity in treated plants. The experiment followed a completely randomized design (CRD), and the Least Significant
... Show Moreobjectives: To investigate the polyomaviruses (BK, JC) in asymptomatic kidney transplant recipients and healthy persons as control. It is one of the first reports on serological detection and molecular characterization that describes the circulation of polyomaviruses (BKV, JCV) have been done in Iraq recently. Methodology: The present study was designed as prospective case control study was done during the period from November 2015 to August 2016. Total of 97 serum and urine samples were collected randomly from 25 healthy control person and 72 renal transplant recipients, attending Iraqi Renal Transplantatio
INTRODUCTION: A range of tools and technologies are at disposal for the purpose of defect detection. These include but are not limited to sensors, Statistical Process Control (SPC) software, Artificial Intelligence (AI) and machine learning (ML) algorithms, X-ray systems, ultrasound systems, and eddy current systems. OBJECTIVES: The determination of the suitable instrument or combination of instruments is contingent upon the precise production procedure and the category of flaw being identified. In certain cases, defects may necessitate real-time monitoring and analysis through the use of sensors and SPC software, whereas more comprehensive analysis may be required for other defects through the utilization of X-ray or ultrasound sy
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