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A proposal to detect computer worms (malicious codes) using data mining classification algorithms
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Malicious software (malware) performs a malicious function that compromising a computer system’s security. Many methods have been developed to improve the security of the computer system resources, among them the use of firewall, encryption, and Intrusion Detection System (IDS). IDS can detect newly unrecognized attack attempt and raising an early alarm to inform the system about this suspicious intrusion attempt. This paper proposed a hybrid IDS for detection intrusion, especially malware, with considering network packet and host features. The hybrid IDS designed using Data Mining (DM) classification methods that for its ability to detect new, previously unseen intrusions accurately and automatically. It uses both anomaly and misuse detection techniques using two DM classifiers (Interactive Dichotomizer 3 (ID3) classifier and Naïve Bayesian (NB) Classifier) to verify the validity of the proposed system in term of accuracy rate. A proposed HybD dataset used in training and testing the hybrid IDS. Feature selection is used to consider the intrinsic features in classification decision, this accomplished by using three different measures: Association rules (AR) method, ReliefF measure, and Gain Ratio (GR) measure. NB classifier with AR method given the most accurate classification results (99%) with false positive (FP) rate (0%) and false negative (FN) rate (1%).

Publication Date
Tue Jan 01 2019
Journal Name
Ieee Access
Implementation of Univariate Paradigm for Streamflow Simulation Using Hybrid Data-Driven Model: Case Study in Tropical Region
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Publication Date
Thu Dec 31 2020
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Application of data content analysis (DEA) technology to evaluate performance efficiency: applied research in the General Tax Authority
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The aim of the research is to use the data content analysis technique (DEA) in evaluating the efficiency of the performance of the eight branches of the General Tax Authority, located in Baghdad, represented by Karrada, Karkh parties, Karkh Center, Dora, Bayaa, Kadhimiya, New Baghdad, Rusafa according to the determination of the inputs represented by the number of non-accountable taxpayers and according to the categories professions and commercial business, deduction, transfer of property ownership, real estate and tenders, In addition to determining the outputs according to the checklist that contains nine dimensions to assess the efficiency of the performance of the investigated branches by investing their available resources T

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Publication Date
Fri Mar 31 2017
Journal Name
Iraqi Journal Of Biotechnology
Reliable Reference Gene for Normalization of RT- qPCR Data in Human Cancer Cell Lines
Subjected to Gene Knockdown
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Quantitative real-time Polymerase Chain Reaction (RT-qPCR) has become a valuable molecular technique in biomedical research. The selection of suitable endogenous reference genes is necessary for normalization of target gene expression in RT-qPCR experiments. The aim of this study was to determine the suitability of each 18S rRNA and ACTB as internal control genes for normalization of RT-qPCR data in some human cell lines transfected with small interfering RNA (siRNA). Four cancer cell lines including MCF-7, T47D, MDA-MB-231 and Hela cells along with HEK293 representing an embryonic cell line were depleted of E2F6 using siRNA specific for E2F6 compared to negative control cells, which were transfected with siRNA not specific for any gene. Us

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Publication Date
Fri Jan 01 2010
Journal Name
Conference Proceedings
Assessing the accuracy of 'crowdsourced' data and its integration with official spatial data sets
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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Computing
Twitter Location-Based Data: Evaluating the Methods of Data Collection Provided by Twitter Api
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Twitter data analysis is an emerging field of research that utilizes data collected from Twitter to address many issues such as disaster response, sentiment analysis, and demographic studies. The success of data analysis relies on collecting accurate and representative data of the studied group or phenomena to get the best results. Various twitter analysis applications rely on collecting the locations of the users sending the tweets, but this information is not always available. There are several attempts at estimating location based aspects of a tweet. However, there is a lack of attempts on investigating the data collection methods that are focused on location. In this paper, we investigate the two methods for obtaining location-based dat

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Publication Date
Fri Oct 31 2025
Journal Name
Mathematical Modelling Of Engineering Problems
Modified Optimization Algorithms for Infinite Integro-Differential Equations via Generalized Laguerre Polynomials
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This study emphasizes the infinite-boundary integro-differential equation. To examine the approximate solution of the problem, two modified optimization algorithms are proposed based on generalized Laguerre functions. In the first technique, the proposed method is applied to the original problem by approximating the solution using the truncated generalized Laguerre polynomial of the unknown function, optimizing coefficients through error minimization, and transforming the integro-differential equation into an algebraic equation. In contrast, the second approach incorporates a penalty term into the objective function to effectively enforce boundary and integral constraints. This technique reduces the original problem to a mathematical optimi

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Publication Date
Fri Mar 31 2023
Journal Name
Wasit Journal Of Computer And Mathematics Science
Security In Wireless Sensor Networks Based On Lightweight Algorithms : An Effective Survey
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At the level of both individuals and companies, Wireless Sensor Networks (WSNs) get a wide range of applications and uses. Sensors are used in a wide range of industries, including agriculture, transportation, health, and many more. Many technologies, such as wireless communication protocols, the Internet of Things, cloud computing, mobile computing, and other emerging technologies, are connected to the usage of sensors. In many circumstances, this contact necessitates the transmission of crucial data, necessitating the need to protect that data from potential threats. However, as the WSN components often have constrained computation and power capabilities, protecting the communication in WSNs comes at a significant performance pena

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Publication Date
Mon Mar 01 2010
Journal Name
Journal Of Computer Science
Dropping down the Maximum Item Set: Improving the Stylometric Authorship Attribution Algorithm in the Text Mining for Authorship Investigation
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Publication Date
Wed May 01 2024
Journal Name
Journal Of Engineering
Orbital Prosthesis Rehabilitation in Biomedical Engineering by Means of Computer Vision-Photogrammetry and 3D Prototyping
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Eye loss may be caused as a result of eye trauma, accidents, or malignant tumors, which leads the patient to undergo surgery to remove the damaged parts. This research examines the potential of computer vision represented by Structure from Motion (SfM) photogrammetry in fabricating the orbital prosthesis as a noninvasive and low-cost technique. A low-cost camera was used to collect the data towards extracting the dense 3D data of the patient facial features following Structure from Motion-Multi View Stereo (SfM-MVS) algorithms. To restore the defective orbital, a Reverse Engineering (RE) based approach has been applied using the similarity RE algorithms based on the opposite healthy eye to rehabilitate the defected orbital precisely

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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Agricultural And Statistical Sciences
A noval SVR estimation of figarch modal and forecasting for white oil data in Iraq
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The purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals

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