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Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN
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Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an SVM-based DDoS detection model shows superior performance. This comparative analysis offers a valuable insight into the development of efficient and accurate techniques for detecting DDoS attacks in SDN environments with less complexity and time.

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Publication Date
Tue Jan 14 2020
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The role of the Internal Audit Department in detecting corruption through accountability
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Internal Audit  is one of the most important backers of corporate governance, the researcher expanded his interest in this subject to examine the efficiency of Internal Auditors at the Arab Bank and its branches in Jordan to achieve Accountability which enhances the Corporate Governance and to identify the effect of the International Internal Audit  Standards in strengthening the role of Internal Auditors in Accountability, and the effect of Attribute and Performance Standards in Accountability. The researcher applied descriptive analysis method to define the role of Internal Audit in the Arab Bank in achieving one of the basic principles of Corporate Governance assimilated in Accountability. The researcher’s sources include

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Publication Date
Tue Sep 01 2009
Journal Name
2009 5th International Conference On Wireless Communications, Networking And Mobile Computing
Cooperation Spectrum Sensing Using Optimal Joint Detecting
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In cognitive radio networks, there are two important probabilities; the first probability is important to primary users called probability of detection as it indicates their protection level from secondary users, and the second probability is important to the secondary users called probability of false alarm which is used for determining their using of unoccupied channel. Cooperation sensing can improve the probabilities of detection and false alarm. A new approach of determine optimal value for these probabilities, is supposed and considered to face multi secondary users through discovering an optimal threshold value for each unique detection curve then jointly find the optimal thresholds. To get the aggregated throughput over transmission

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Publication Date
Tue Jul 11 2023
Journal Name
Journal Of Educational And Psychological Researches
Detecting Suicidal Tendencies among Secondary School Students
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The current research aims to detect the level of suicidal tendencies among secondary school students in terms of gender and educational stage (intermediate school students and high school students). The researcher adopted Al Hafeez's (2017) scale for suicidal tendencies, it consists of (57) items including six domains, namely: suicidal ideation, social motives for suicide, tendency to self-harm, desire for death, indifference and pessimism about life, willingness to commit suicide. The scale was modified to be (42) items after it was exposed to a group of experts. The scale was applied to a sample of (200) male and female students from secondary schools in Baghdad Governorate (Karkh - Rusafa) for the academic year 2021-2022. The results

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Detecting Textual Propaganda Using Machine Learning Techniques
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Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation.  Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annota

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Publication Date
Tue Aug 01 2023
Journal Name
Baghdad Science Journal
A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches
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       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

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Publication Date
Tue Oct 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
Using Benford’s law to detecting earnings management Application on a sample of listed companies in the Iraqi market for securities
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Abstract

The net profit reported in the annual financial statements of the companies listed in the financial markets, is considered one of the Sources of information relied upon by users of accounting information in making their investment decisions. At the same time be relied upon in calculating the bonus (Incentives) granted to management, therefore the management of companies to manipulate those numbers in order to increase those bonuses associated to earnings, This practices are called earnings management practices. the manipulation in the figures of earnings by management will mislead the users  of financial statements who depend on reported earnings in their deci

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Publication Date
Wed Mar 08 2023
Journal Name
Sensors
A Critical Review of Remote Sensing Approaches and Deep Learning Techniques in Archaeology
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To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip

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Publication Date
Mon Sep 16 2019
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Surveying the Organization of Digital Contents in the Internet Environments Using Ontological Approaches
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     Ontology is a system for classifying human knowledge according to its objective characteristics and hierarchical relations through building clusters or that bear common characteristics. In digital environments, it is a mechanism that helps regulate a vast amount of information by achieving a complete link between sub-thematic concepts and their main assets. The purpose of this study is to survey the previously conducted studies that use ontology in organizing digital data on social networking sites, such as the search engines Yahoo, Google, and social networks as Facebook and their findings. Results have shown that all these studies invest ontology for the purpose of organizing digital content data, especially on

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Publication Date
Tue Aug 01 2023
Journal Name
Iop Conference Series: Earth And Environmental Science
Modis Satellite Data Evaluation for Detecting the Dust Storm Using Remote Sensing Techniques Over Iraq
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Abstract<p>The phenomena of Dust storm take place in barren and dry regions all over the world. It may cause by intense ground winds which excite the dust and sand from soft, arid land surfaces resulting it to rise up in the air. These phenomena may cause harmful influences upon health, climate, infrastructure, and transportation. GIS and remote sensing have played a key role in studying dust detection. This study was conducted in Iraq with the objective of validating dust detection. These techniques have been used to derive dust indices using Normalized Difference Dust Index (NDDI) and Middle East Dust Index (MEDI), which are based on images from MODIS and in-situ observation based on hourly wi</p> ... Show More
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Publication Date
Mon Jun 01 2009
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Used Approaches In Formulating Accounting Standards in Iraq & Their Develop Importance: filed Study
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This research aims to show the most important approaches applicable in forming the accounting standards , the  importance approaches used to formulate local accounting standards & the  need to cancel or develop the unified accounting system in Iraq besides activate the local auditing & accounting standards council to issue accounting standards able to save the relevance & Reliable accounting information that serve the decisions of stockholders and others of stakeholders in the incorporate companies , especially the companies listed in the Iraq securities exchange , So this study based partially on analyzing the questionnaire form which has been designed to inquiry the specialists and experts opinion about the accoun

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