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Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers
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Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of selected features have been adopted to train four machine-learning based classifiers.  The two sets of selected features are based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) approach respectively.  These evolutionary-based algorithms are known to be effective in solving optimization problems.  The classifiers used in this study are Naïve Bayes, k-Nearest Neighbor, Decision Tree and Support Vector Machine that have been trained and tested using the NSL-KDD dataset. The performance of the abovementioned classifiers using different features values was evaluated.  The experimental results indicate that the detection accuracy improves by approximately 1.55% when implemented using the PSO-based selected features than that of using GA-based selected features.  The Decision Tree classifier that was trained with PSO-based selected features outperformed other classifiers with accuracy, precision, recall, and f-score result of 99.38%, 99.36%, 99.32%, and 99.34% respectively.  The results show that using optimal features coupling with a good classifier in a detection system able to reduce the classifier model building time, reduce the computational burden to analyze data, and consequently attain high detection rate.

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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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Publication Date
Mon Dec 01 2014
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Towards Perceptions building a descriptive and procedural for the knowledge assets at the center of learning and growth for the techniques of Balanced Score card in the light of the strategic map concepts to achieve the strategic and operational: requirements of the contemporary business
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Abstract:

   Witness the current business environment changes rapidly reflected on the performance of the facility wishing to stay , which is no longer style reaction enough to handle installations with their environment , and quickly began to lose its luster with the emergence of a message and the vision of contemporary business environment from a set of parts interacting with each other and the concept of behavioral includes all dimensions of performance, it is imperative to adopt a system installations influence variables and positive interaction through the development of strategic plans and the use of implementation and follow-up strategies to ensure the effectiveness of the method for meas

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Publication Date
Wed Jan 28 2026
Journal Name
Journal Of Physical Education
The Effect of Constructive Learning Model on Cognitive Achievement and Learning dribbling Skill in Soccer for Secondary School Students
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The research aimed at identifying the effect of using constructive learning model on academic achievement and learning soccer dribbling Skill in 2nd grade secondary school students. The researcher used the experimental method on (30) secondary school students; 10 selected for pilot study, 20 were divided into two groups. The experimental group followed constructive learning model while the controlling group followed the traditional method. The experimental program lasted for eight weeks with two teaching sessions per week for each group. The data was collected and treated using SPSS to conclude the positive effect of using constructive learning model on developing academic achievement and learning soccer dribbling Skill in 2nd grade seconda

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Publication Date
Wed Feb 01 2023
Journal Name
Baghdad Science Journal
Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption
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A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a

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Publication Date
Wed Dec 01 2021
Journal Name
Baghdad Science Journal
Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks
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Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file.  In this work, a video steganography model has been proposed through training a model to hiding video (or images) within another video using convolutional neural networks (CNN). By using a CNN in this approach, two main goals can be achieved for any steganographic methods which are, increasing security (hardness to observed and broken by used steganalysis program), this was achieved in this work as the weights and architecture are randomized. Thus,

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Publication Date
Sat Jul 01 2023
Journal Name
International Journal Of Computing And Digital Systems
Human Identification Based on SIFT Features of Hand Image
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Publication Date
Wed Sep 01 2021
Journal Name
Baghdad Science Journal
Determination of Sulfacetamide Sodium in Pure and Their Pharmaceutical Formulations by Using Cloud Point Extraction Method
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      In this study, simple, low cost, precise and speed spectrophotometric methods development for evaluation of sulfacetamide sodium are described. The primary approach contains conversion of sulfacetamide sodium to diazonium salt followed by a reaction with p-cresol as a reagent in the alkaline media.  The colored product has an orange colour with absorbance at λmax 450 nm. At the concentration range of (5.0-100 µg.mL-1), the Beer̆ s Low is obeyed with correlation coefficient (R2= 0.9996), limit of detection as 0.2142 µg.mL-1, limit of quantification as 0.707 µg.mL-1 and molar absorptivity as 1488.249 L.mol-1.cm-1. The other approach, cloud point extraction w

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Publication Date
Tue Dec 01 2020
Journal Name
Journal Of Economics And Administrative Sciences
Fuzzy Analytic Hierarchy Process FEAHP to Prioritize The Evaluation of The Main and Subsidiary Criteria in B2B Industrial Market Sectors – Applied Research
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The research aims to define the main and subsidiary criteria for evaluating the industrial market sectors and proposing a model for arranging these criteria according to priority and knowing the highest criteria in terms of relative importance in the General Company for Automobile Trade and Machinery, and for the purpose of establishing this model, experiences in the concerned company were approved, and this study proposes a multi-criteria decision model According to the FEAHP, the expanded fuzzy hierarchical analysis method enables the commercial company to develop clear strategic policies on which the company’s management system depends on determining criteria for evaluating and selecting market sectors and making appropriate

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Publication Date
Sun Feb 03 2019
Journal Name
Journal Of The College Of Education For Women
Solution of the electromechanical machining problem using the collocation method based on Bernstein polynomials
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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Academic research the impact of organizational culture on the performance of the internal – study in government institutions- analysis study on some government institutions in state of Qatar
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This study was aimed to explore the impact of social concepts about tribe, clan and women, on internal audit performance. These concepts are considered to be components of the organizational culture and performance of internal audit practice, with respect to the individual and collective performance within the institution. The study, furthermore, was intended to investigate and understand the role of the organizational culture of the tribal, clan and women components with regard to their role in society, in Qatar.

To achieve these objectives, the researcher followed the descriptive analytical approach, using a questionnaire directed to experts and staff working in the banking sector, with the view to test

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