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BotDetectorFW: an optimized botnet detection framework based on five features-distance measures supported by comparisons of four machine learning classifiers using CICIDS2017 dataset
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<p><span>A Botnet is one of many attacks that can execute malicious tasks and develop continuously. Therefore, current research introduces a comparison framework, called BotDetectorFW, with classification and complexity improvements for the detection of Botnet attack using CICIDS2017 dataset. It is a free online dataset consist of several attacks with high-dimensions features. The process of feature selection is a significant step to obtain the least features by eliminating irrelated features and consequently reduces the detection time. This process implemented inside BotDetectorFW using two steps; data clustering and five distance measure formulas (cosine, dice, driver &amp; kroeber, overlap, and pearson correlation) using C#, followed by selecting the best N features used as input into four classifier algorithms evaluated using machine learning (WEKA); multilayerperceptron, JRip, IBK, and random forest. In BotDetectorFW, the thoughtful and diligent cleaning of the dataset within the preprocessing stage beside the normalization, binary clustering of its features, followed by the adapting of feature selection based on suitable feature distance techniques, and finalized by testing of selected classification algorithms. All together contributed in satisfying the high-performance metrics using fewer features number (8 features as a minimum) compared to and outperforms other methods found in the literature that adopted (10 features or higher) using the same dataset. Furthermore, the results and performance evaluation of BotDetectorFM shows a competitive impact in terms of classification accuracy (ACC), precision (Pr), recall (Rc), and f-measure (F1) metrics.</span></p>

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Publication Date
Sat Aug 01 2015
Journal Name
International Journal Of Emerging Trends Technology In Computer Science
A survey of similarity measures in web image search
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Publication Date
Wed Jan 02 2019
Journal Name
Journal Of Educational And Psychological Researches
Training Program based on Some Strategies of Self-Regulated Learning for the Teachers of Mathematics and its Effect on their Relational Mathematics
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This research aims at building a proposed training program according to the self-regulated strategies for the mathematics teachers and to identify the effect of this program on relational Mathematics of teachers. The sample of the research was (60) Math teachers; (30) teachers as experimental group and (30) teachers as control group. The results of the current research reacheded that the proposed training program according to some self-managed learning strategies, meets the needs of trainees with remarkable effectiveness to improve the level of their teaching performance to achieve the desired goals. Training teacher according to self-managed learning strategies is effective in bringing about the transition of training to their students

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Publication Date
Sat Dec 01 2012
Journal Name
Journal Of Engineering
Optimized Zero and First Order Design of Micro Geodetic Networks
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Precision is one of the main elements that control the quality of a geodetic network, which defines as the measure of the network efficiency in propagation of random errors. This research aims to solve ZOD and FOD problems for a geodetic network using Rosenbrock Method to optimize the geodetic networks by using MATLAB programming language, to find the optimal design of geodetic network with high precision. ZOD problem was applied to a case study network consists of 19 points and 58 designed distances with a priori deviation equal to 5mm, to determine the best points in the network to consider as control points. The results showed that P55 and P73 having the minimum ellipse of error and considered as control points. FOD problem was applie

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
An exploratory study of history-based test case prioritization techniques on different datasets
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In regression testing, Test case prioritization (TCP) is a technique to arrange all the available test cases. TCP techniques can improve fault detection performance which is measured by the average percentage of fault detection (APFD). History-based TCP is one of the TCP techniques that consider the history of past data to prioritize test cases. The issue of equal priority allocation to test cases is a common problem for most TCP techniques. However, this problem has not been explored in history-based TCP techniques. To solve this problem in regression testing, most of the researchers resort to random sorting of test cases. This study aims to investigate equal priority in history-based TCP techniques. The first objective is to implement

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Publication Date
Mon Jun 19 2023
Journal Name
Journal Of Engineering
REVIEW THE ASSESSMENT OF EFFECTS OF LOST TIME INJURIES IN AN INDUSTRIAL SYSTEM BY USING AN EXPLANATORY PROGRAM
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Health and safety problem can be described by statistics it can only be understood by knowing and feeling the pain, suffering, and depression. Health and safety has a legal responsibility to protect it for everyone who can affect in the workplace. This includes manufacturers, suppliers, designers and controllers of work places and employees. Work injury is one of the major problems in manufacturing and production systems industries; it is reduced production efficiency and affects the cost. To gain flexibility from a traditional manufacturing system and production efficiency, this paper is about the application of estimating technology to preview and synthesis of Lost Time of Work Injuries in industry systems aims to provide a safe workin

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Publication Date
Fri Jan 01 2021
Journal Name
Analytical And Bioanalytical Chemistry Research
Mutual Derivatization in the Determination of Dapsone and Thymol Using Cloud Point Extraction Followed by Spectrophotometric Detection
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A procedure for the mutual derivatization and determination of thymol and Dapsone was developed and validated in this study. Dapsone was used as the derivatizing agent for the determination of thymol, and thymol was used as the derivatizing agent for the determination of Dapsone. An optimization study was performed for the derivatization reaction; i.e., the diazonium coupling reaction. Linear regression calibration plots for thymol and Dapsone in the direct reaction were constructed at 460 nm, within the concentration range of 0.3-7 μg ml-1 for thymol and 0.3-4 μg ml-1 for Dapsone, with limits of detection 0.086 and 0.053 μg ml-1, respectively. Corresponding plots for the cloud point extraction of thymol and Dapsone were constructed

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Publication Date
Tue Jun 08 2021
Journal Name
مجلة العلوم و التكنولوجية للنشاطات البدنية و الرياضية
The effectiveness of using (7E’s) learning cycle in learning a movement chain on the uneven bars in the artistic gymnastics for women
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Abstract The Object of the study aims to identify the effectiveness of using the 7E’s learning cycle to learn movement chains on uneven bars, for this purpose, we used the method SPSS. On a sample composed (20) students on collage of physical education at the university of Baghdad Chosen as two groups experimental and control group (10) student for each group, and for data collection, we used SPSS After collecting the results and having treated them statistically, we conclude the use 7E’s learning cycle has achieved remarkable positive progress, but it has diverged between to methods, On this basis, the study recommended the necessity of applying 7E’s learning cycle strategy in learning the movement chain on uneven bar

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Publication Date
Mon Apr 29 2024
Journal Name
Euphrates Journal Of Agricultural Science
Effect foliar of some organic fertilizers on growth and yield of four potato cultivars
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Publication Date
Thu Dec 30 2021
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Design a system for an approved video copyright over cloud based on biometric iris and random walk generator using watermark technique
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Publication Date
Mon Jan 01 2024
Journal Name
Studies In Systems, Decision And Control
The Effect of Using an Accounting Information System Based on Artificial Intelligence in Detecting Earnings Management to Enhance the Sustainability of Economic Units
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This research aims to clarify the importance of an accounting information system that uses artificial intelligence to detect earnings manipulation. The research problem stems from the widespread manipulation of earning in economic entities, especially at the local level, exacerbated by the high financial and administrative corruption rates in Iraq due to fraudulent accounting practices. Since earning manipulation involves intentional fraudulent acts, it is necessary to implement preventive measures to detect and deter such practices. The main hypothesis of the research assumes that an accounting information system based on artificial intelligence cannot effectively detect the manipulation of profits in Iraqi economic entities. The researche

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