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bsj-571
B-splines Algorithms for Solving Fredholm Linear Integro-Differential Equations
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Algorithms using the second order of B -splines [B (x)] and the third order of B -splines [B,3(x)] are derived to solve 1' , 2nd and 3rd linear Fredholm integro-differential equations (F1DEs). These new procedures have all the useful properties of B -spline function and can be used comparatively greater computational ease and efficiency.The results of these algorithms are compared with the cubic spline function.Two numerical examples are given for conciliated the results of this method.

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Publication Date
Tue Sep 01 2009
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of estimation methods for regression model parametersIn the case of the problem of linear multiplicity and abnormal values
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 A simulation study is used to examine the robustness of some estimators on a multiple linear regression model with problems of multicollinearity and non-normal errors, the Ordinary least Squares (LS) ,Ridge Regression, Ridge Least Absolute Value (RLAV), Weighted Ridge (WRID), MM and a robust ridge regression estimator MM estimator, which denoted as RMM this is the modification of the Ridge regression by incorporating robust MM estimator . finialy, we show that RMM is the best among the other estimators

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Publication Date
Sun Sep 02 2012
Journal Name
Baghdad Science Journal
Evaluation of the Compton (Incoherent) and Rayleigh (Coherent) Differential Cross Sections of Scattering for Rhodium 103Rh45 and Tantalum181Ta73 by Employing CSC model
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The differential cross section for the Rhodium and Tantalum has been calculated by using the Cross Section Calculations (CSC) in range of energy(1keV-1MeV) . This calculations based on the programming of the Klein-Nashina and Rayleigh Equations. Atomic form factors as well as the coherent functions in Fortran90 language Machine proved very fast an accurate results and the possibility of application of such model to obtain the total coefficient for any elements or compounds.

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Publication Date
Wed Jan 01 2025
Journal Name
Open Veterinary Journal
<b>Antimicrobial susceptibility patterns of infective <i>Streptococci</i> recovered from local and imported mozzarella cheese from different markets in Baghdad</b>
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Background: This research identified Streptococci spp. depending on culture, biochemistry, the VITEK technique, ability to produce biofilms, and antibiotic resistance. Aim: The goal of this study was to perform microbiological procedures to evaluate the qualitative qualities of mozzarella cheese against infective Streptococci using microbiological care. Methods: Sixty (60) mozzarella cheese samples were brought from diverse markets in Baghdad from October 2023 to December 2023 at the Zoonoses Research Unit and Veterinary Public Health Department, Veterinary Medicine College, University of Baghdad. Culture of samples on agar (MacConkey and blood) and aerobically incubated at 37°C for 48 hours. Gram staining purified colonies to

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Publication Date
Mon May 25 2020
Journal Name
International Journal Of Psychosocial Rehabilitation
Training for Anaerobic Differential Threshold Stand and its Impact on Lactic Acid Concentration and LDH Enzyme and VO2MaX and Cortisol Hormone for Free 400 m men-runners
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The study aimed at designing a training program by using training for the anaerobic differential threshold stand and the effects of those trainings on the variables of (Concentration of Lactic Acid and LDH Enzyme, VO2 MaX and Cortisol Hormone). The Researchers used the experimental program with one-group style. Also, they used a sample with (8) men-players in a (free 400 m men-runners) and they used many instruments and procedures, most notably the training-program prepared for 10 weeks and for 3 training units weekly, (70-90 min) for each unit. They used the training intensity from 85-100% of the player's ability. After finishing the training program and doing some pre-tests and post-tests then statistically checking the results, the resea

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Publication Date
Wed Mar 18 2020
Journal Name
Baghdad Science Journal
A Software Defined Network of Video Surveillance System Based on Enhanced Routing Algorithms
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Software Defined Network (SDN) is a new technology that separate the ‎control plane from the data plane. SDN provides a choice in automation and ‎programmability faster than traditional network. It supports the ‎Quality of Service (QoS) for video surveillance application. One of most ‎significant issues in video surveillance is how to find the best path for routing the packets ‎between the source (IP cameras) and destination (monitoring center). The ‎video surveillance system requires fast transmission and reliable delivery ‎and high QoS. To improve the QoS and to achieve the optimal path, the ‎SDN architecture is used in this paper. In addition, different routing algorithms are ‎used with different steps. First, we eva

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Publication Date
Mon Aug 01 2016
Journal Name
2016 38th Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (embc)
Selecting the optimal movement subset with different pattern recognition based EMG control algorithms
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Publication Date
Mon Jul 01 2024
Journal Name
Journal Of Engineering
Efficient Intrusion Detection Through the Fusion of AI Algorithms and Feature Selection Methods
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With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

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Publication Date
Wed Feb 06 2013
Journal Name
Eng. & Tech. Journal
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 dete

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Publication Date
Wed Dec 01 2021
Journal Name
Civil And Environmental Engineering
Prediction of the Delay in the Portfolio Construction Using Naïve Bayesian Classification Algorithms
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Abstract<p>Projects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postpo</p> ... Show More
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Publication Date
Thu Sep 18 2025
Journal Name
Sustainable Engineering And Innovation
Using fruit fly and dragonfly optimization algorithms to estimate the Fama-MacBeth model
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This research proposes the application of the dragonfly and fruit fly algorithms to enhance estimates generated by the Fama-MacBeth model and compares their performance in this context for the first time. To specifically improve the dragonfly algorithm's effectiveness, three parameter tuning approaches are investigated: manual parameter tuning (MPT), adaptive tuning by methodology (ATY), and a novel technique called adaptive tuning by performance (APT). Additionally, the study evaluates the estimation performance using kernel weighted regression (KWR) and explores how the dragonfly and fruit fly algorithms can be employed to enhance KWR. All methods are tested using data from the Iraq Stock Exchange, based on the Fama-French three-f

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