In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

Online learning is not a new concept in education, but it has been used extensively since the Covid-19 pandemic and is still in use now. Every student in the world has gone through this learning process from the primary to the college levels, with both teachers and students conducting instruction online (at home). The goal of the current study is to investigate college students’ attitudes towards online learning. To accomplish the goal of the current study, a questionnaire is developed and adjusted before being administered to a sample of 155 students. Additionally, validity and reliability are attained. Some conclusions, recommendations, and suggestions are offered in the end.

In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

The purpose of this research is to suggest a model which may be used by law enforcement to broaden individual and organizational creativity while providing a more inclusive, holistic view of management within our internal and external organizational structures. This project introduces alternatives to traditional management, leadership styles and problem solving strategies. Law enforcement organizations in the 21st century will face many challenges. Much like community policing impacted the nation in the mid 1980's through the 1990's, the systems thinking approach offers a philosophy for the new millennium.

Most organizations will face many challenges in the next decade of the twentieth first century. Just

Background:-Osteoarthritis (OA) is the most common form of arthritis and the leading source of physical disability in elderly people. The Prevalence of OA is increasing and will continue to do so as the population gets older. The OA is predominantly managed in primary care centers by primary health care physicians and much can be done to alleviate symptoms from osteoarthritis by combinations of therapeutic options including pharmacological and non-pharmacological treatments.

Objectives of study :- To assess the knowledge, attitude and practice of Iraqi PHCC physicians in Baghdad, AL-Rusafa, regarding the management of osteoarthritis patient, and it's association with sociodemogra

Abstract Background: The prevalence of heart failure (HF) continues to increase with an increase in the aging population. Palliative care should be integrated into routine disease management for all patients with serious illness, regardless of settings or prognosis. Objectives: The purposes of this study were to determine the level of knowledge of nurses concerning palliative care for patients with heart failure after implementation of instructional program. Design: The study was a quasi-experimental study and consists of 60 nurses. Setting: The study was conducted between17th November 2021, to 10th February 2022, at three teaching hospitals in Baghdad city, Iraq. Method: A non-probability (purposive) sample was utilized, nurses who agreed

Find aimed to reveal the teaching practices of the teaching faculties of education departments of the University of Baghdad in the light of the motives of learning from the perspective of students . Researcher selected the research community in a deliberate humanitarian sections of students and faculties of education for girls and IbnRushd . Ed reached basic research sample ( 400 ) students were numbers search tool questionnaire , and included (30) paragraph . In processors statistical data , use the weighted average and the weight percentile , results have shown that it is more paragraphs which received wide acceptance from the viewpoint of students ( do not make fun of the students' answers wrong but corrects and discussed with them )

Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducte

The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne