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Performance Evaluation of SDN DDoS Attack Detection and Mitigation Based Random Forest and K-Nearest Neighbors Machine Learning Algorithms
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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 network topology have been generated to observe the effectiveness of proposed algorithms on different network architectures. The results reveal that RF performs better than KNN in a single topology, and both have close performance in other topologies.

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Publication Date
Sun Apr 06 2014
Journal Name
Journal Of Educational And Psychological Researches
Effectiveness of cooperative learning in at the implementation of floral material for celli graph and decoration
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The purpose of this resesrh know (the effectiveness of cooperative lerarning implementation of floral material for calligraphy and ornamentation) To achieve the aim of the research scholar put the two zeros hypotheses: in light of the findings of the present research the researcher concluded a number of conclusions, including: -
1 - Sum strategy helps the learner to be positive in all the information and regulations, monitoring and evaluation during the learning process.
2 - This strategy helps the learner to use information and knowledge and their use in various educational positions, and to achieve better education to increase its ability to develop thinking skills and positive trends towards the article.
In light of this, the

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Publication Date
Sat Jun 21 2025
Journal Name
Journal Of Baghdad College Of Dentistry
Evaluation of nano surface modification on CPTi dental implant using chemical method: mechanical and histological evaluation
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Background The application of nanotechnology to biomedical surfaces is explained by the ability of cells to interact with nanometric features. The aim of this study was to consider the role of nanoscale topographic modification of CPTi dental implant using chemical etching method for the purpose of improving osseointegration. Materials and methods: Commercial pure titanium rod was machined into 20 dental implants. Each implant was machined in diameter about 3mm, length of 8mm (5mm was threaded part and 3mm was flat part). Implants were prepared and divided into 2 groups according to the types of surface modification method used: 1st group (10 implant) remained without nano surface modification (control), 2nd group include (10 implant) etche

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Publication Date
Sat Jun 21 2025
Journal Name
Journal Of Baghdad College Of Dentistry
Assessment of some mechanical properties of Imprelon® and Duran® thermoplastic Biostar machine sheets in comparison with some types of acrylic resins
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Background: Imprelon® Biostar foils are new alternative tray material that has become increasingly popular because oftheir several advantages. Also, (Duran®) is another type of Biostar foils which is used in splint therapy. This study assessed some mechanical properties of these two types Biostar sheets in comparison with some types of acrylic resins used for construction of trays and splints. Materials and Methods: A total of 150 specimens were prepared, 30 specimens for each test, 10 for each group material in order to assess some mechanical properties of the Imprelon® Biostar foil (dimension stability, surface roughness and shear bond strength of Imprelon® materialto zinc oxide impression material) and compare them to that of the oth

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Preparation and Evaluation of PolyVinyl Alcohol Derivative as Antimicrobial and Antifungal Agents
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Polyvinal alcohol was Cynoethylated , complex compound with Iodin in presence of Cu++ ions were preparated and their ultra violet (U.V) and infra red( IR) spectra were investigated. The prepared derivative and complexes were evaluated as antibacterial and antifungal agents following the standard dilution method. MIC(minimum inhibitory concentration) for each polymer using ten types of gram + ve and gram _ ve bacteria were determinated in addition to three types of fungi. The results obtainded showed that MIC, s were around 0.0011 × 103 molar for different polymetric derivatives tried.

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Publication Date
Mon May 08 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Evaluation Of liver And Kidney Functions In Lung And Bladder Cancer Patients
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This study was performed at Nuclear Radiation Hospital in Baghdad for the period from
January 2011 to May 2011. 44 Blood samples were collected from patients suffered lung and
bladder cancer and 24 samples as healthy control individuals.
Routine liver functions tests were studied by measuring S.GPT, S.GOT and Kidney
function was evaluated by estimation of blood urea and creatinine in serum samples of
individuals studied.
It was observed that the incidence of lung and bladder cancer was higher in males than
females patients ( male 81.82 %, 72.73%, female18 .18%, 27.27% respectively).
Insignificant difference was noted among age of lung and bladder cancer patients
compared with control group. The results

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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Artificial Neural Network and Latent Semantic Analysis for Adverse Drug Reaction Detection
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Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

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Publication Date
Thu May 30 2024
Journal Name
Iraqi Journal Of Science
A Review Study on Forgery and Tamper Detection Techniques in Digital Images
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Digital tampering identification, which detects picture modification, is a significant area of image analysis studies. This area has grown with time with exceptional precision employing machine learning and deep learning-based strategies during the last five years. Synthesis and reinforcement-based learning techniques must now evolve to keep with the research. However, before doing any experimentation, a scientist must first comprehend the current state of the art in that domain. Diverse paths, associated outcomes, and analysis lay the groundwork for successful experimentation and superior results. Before starting with experiments, universal image forensics approaches must be thoroughly researched. As a result, this review of variou

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Publication Date
Wed Jan 15 2020
Journal Name
Arab World English Journal
Posters in Vocabulary Learning
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An essential element in English as a foreign language (EFL) learning is vocabulary. There is a big emphasis on learning the new words' meaning from the books or inside classrooms. Also, it is a major part of language teaching as well as being fundamental to the learner but there is a big challenge in vocabulary instruction due to the weak confidence by teachers in selecting the suitable practice in teaching vocabulary or they sometimes unable to specify a suitable time for it during the teaching process. The major aim of this study is to investigate the value of posters in vocabulary learning on the 2nd grade students at Halemat Alsaadia High School in Baghdad – Iraq. It hypothesized that there are no statistically significant differences

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Publication Date
Fri Mar 29 2024
Journal Name
Iraqi Journal Of Science
Evaluating the Performance and Behavior of CNN, LSTM, and GRU for Classification and Prediction Tasks
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     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

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Publication Date
Wed Jan 01 2020
Journal Name
Annals Of Tropical Medicine And Public Health
Isolation and identification of fungi from fish feedstuff of cyprinus carpio and detection of aflatoxin b1 and ochratoxin a using ELISA technique
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This study was conducted at the College of Education for Pure Sciences (Ibn Al-Haitham), University of Baghdad. The aim of this study was to isolate and diagnose fungi from fish feedstuff samples, and also detection of aflatoxin B1 and ochratoxin A in fish muscles and feedstuffs. Randomly, the samples were collected from some fish farms from Baghdad, Babil, Wasit, Anbar, and Salah al-Din provinces. This study included the collection of 35 feedstuff samples and 70 fish muscle samples, and each of the two fish samples fed on one sample of the feedstuff. The results showed the presence of several genera of different fungi including Aspergillus spp, Mucor spp., Penicillium spp., Yeast spp., Fusarium spp., Rhizopus spp., Scopiolariopsis spp., Ep

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