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

    The emergence of COVID-19 has resulted in an unprecedented escalation in different aspects of human activities, including medical education. Students and educators across academic institutions have confronted various challenges in following the guidelines of protection against the disease on one hand and accomplishing learning curricula on the other hand. In this short view, we presented our experience in implementing e-learning to the undergraduate nursing students during the present COVID-19 pandemic emphasizing the learning content, barriers, and feedback of students and educators. We hope that this view will trigger the preparedness of nursing faculties in Iraq to deal with this new modality of learning and improve it should t

fields of the College of Agricultural Engineering Sciences, University of Baghdad/Al-Jadriya, in the fall season 2021-2022. With three sowing dates (26 July, 4 August, 12 August), The experiment was implemented according to a randomized complete block design (RCBD) with three replications. The aim is to detect the expression of the INCW1 gene associated with sucrose-to-glucose analysis, which is critical for many metabolic functions in different sink tissues, and determine the amount of fold gene expression to find out which of the genotypes contain the invertase gene, and which ones have a high level of gene expression based on the fold of gene expression. The results showed significant differences in the invertase enzyme activity for the

The aim of this study was to identify the rate of return of the stock through the financial information disclosed by the financial statements of companies both services and insurance included in Iraqi market for securities . The study used a descriptive statistical methods and the correlation matrix for the independent factors , in addition to a regression model for data  analysis and hypothesis . Model included a number of independent variables , which was measured in the size of company (sales or revenue) , and the leverage , in addition to the structure of assets and the book value of owners'  equity in the company , as well as the general price index .Based on the data of (11)companies and for three years, showed the result

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

Dynamic machine foundations can be considered as a necessary component of the industrial infrastructure. Design of the dynamic equipment foundations has, however, traditionally been grounded on a rule of thumb that is inaccurate and rigid to use at the discretion of the engineers. The conventional rule of thumb, which includes minimum weight ratios and resonance avoidance criteria, has been used singularly with two poles, which can be either conservatively designed systems that are too heavy, or systems that are going to experience too much vibration and fatigue. This paper presents a novel, analytical framework for the reinterpretation of traditional design practices, using a physics-based approach, and results in a single, unified overall

This research suggests a robust and systematic way for Arabic Sentiment Analysis using a vast dataset of 66,666 text reviews. One of the main advantages of this study is that the dataset was perfectly balanced (33,333 positive samples and 33,333 negative samples). In machine learning, this 50/50 split is important because it eliminates class bias and enables the predictive model to treat both sentiment classes equally. As shown in the values of the metrics — overall accuracy, weighted precision, weighted recall, and F1 score — there is great similarity among them, indicating a stable and reliable assessment of the model's real potential throughout the Arabic dataset. Based on data profile, the average word count per review is 42.3

Background: The disc prolapse is a common condition especially in young adults. Different levels are affected in the lumber region; the L4/L5 disc is more susceptible to longitudinal load and is the most common site of lumbar disc prolapse. The L5/S1 disc is protected from torsion load by strong ilio-lumbar ligaments but it is more susceptible to axial compressive forces. Many factors affect the result and outcome of surgery in these levels.Objective: The aim of this study is to correlate operative data, short-term results, complications, and prognostic factors (age, gender, mobility, hospital stay, and level of pain) for one-level lumber discectomybetween different levels (L4–L5 vs. L5–S1).Methods In this prospective study, 32 patie

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

The objective of this work is to investigate the performance of a conventional three phase induction motor supplied by unbalanced voltages. An effort to study the motor steady state performance under this disturbance is introduced. Using per phase equivalent circuit analysis with the concept of symmetrical components approach, the steady state performance is theoretically calculated. Also, a model for the induction motor with the MATLAB/Simulink SPS tools has been implemented and steady state results were obtained. Both results are compared and show good correlation as well. The simulation model is introduced to support and enhance electrical engineers with a complete understanding for the steady state performance of a fully loaded induc