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A New Feature-Based Method for Similarity Measurement under the Linux Operating System
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This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper, the performance of the new feature-based algorithm is compared against the performance of seven ontology-based algorithms adapted to Arabic. The results of the evaluation and comparison experiments show that the new proposed algorithm outperforms the adapted word similarity algorithms on the Arabic word benchmark dataset. The proposed algorithm will be included in the AWN-similarity which is free open-source software for Arabic.

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Publication Date
Sun Dec 21 2025
Journal Name
International Journal On Humanities And Social Sciences
Motivation-based Human Needs and their Disclosure Prompts in Chris Cleave’s Little Bee
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The present work folds two qualitative objectives; the first focuses on investigating the multiplicity of motivation-based human needs in Little Bee. The second objective involves examining the linguistic forms adopted to disclose such needs. Consequently, the researchers are to adapt eclectically Alderfer's Existence, Relatedness, and Growth Theory (1969) and Langacker’s theory of Domains (1987) together with his Active Zone Operation (1991). Such a study helps to embody the connectivity between the social and psychological aspects, and the way these two aspects are disclosed using particular linguistic The study has concluded that Bee needed Alderfer’s basic human needs: existence, relatedness, and growth. Besides, satisfying

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Publication Date
Tue Aug 10 2021
Journal Name
Design Engineering
Lossy Image Compression Using Hybrid Deep Learning Autoencoder Based On kmean Clusteri
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Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

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Publication Date
Sun Dec 01 2002
Journal Name
Iraqi Journal Of Physics
Low-Rate High-Quality Parametric Audio Coder based on Sinusoidal plus Noise Representations
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This paper presents a parametric audio compression scheme intended for scalable audio coding applications, and is particularly well suited for operation at low rates, in the vicinity of 5 to 32 Kbps. The model consists of two complementary components: Sines plus Noise (SN). The principal component of the system is an. overlap-add analysis-by-synthesis sinusoidal model based on conjugate matching pursuits. Perceptual information about human hearing is explicitly included into the model by psychoacoustically weighting the pursuit metric. Once analyzed, SN parameters are efficiently quantized and coded. Our informal listening tests demonstrated that our coder gave competitive performance to the-state-of-the- art HelixTM Producer Plus 9 from

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Publication Date
Sat Apr 01 2023
Journal Name
Baghdad Science Journal
Interior Visual Intruders Detection Module Based on Multi-Connect Architecture MCA Associative Memory
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Most recent studies have focused on using modern intelligent techniques spatially, such as those
developed in the Intruder Detection Module (IDS). Such techniques have been built based on modern
artificial intelligence-based modules. Those modules act like a human brain. Thus, they should have had the
ability to learn and recognize what they had learned. The importance of developing such systems came after
the requests of customers and establishments to preserve their properties and avoid intruders’ damage. This
would be provided by an intelligent module that ensures the correct alarm. Thus, an interior visual intruder
detection module depending on Multi-Connect Architecture Associative Memory (MCA)

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Publication Date
Thu Nov 14 2024
Journal Name
Journal Of Emergency Medicine, Trauma And Acute Care
Novel isolation and optimization of anti-MRSA bacteriophages using plaque-based biokinetic methods
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Background: MRSA (methicillin-resistant Staphylococcus aureus) is a global health problem. Many people are looking for new ways to combat MRSA, for example by using bacteriophages (phages). It has been extremely challenging to isolate a sufficient quantity of lytic anti-MRSA phages. Therefore, new techniques for separating, refining, and reworking anti-MRSA phages were sought in this study.

Methods: Of 437 S. aureus isolates, nine clinical MRSA isolates were obtained from three hospitals in Baghdad, Iraq and two ATCC MRSA strains were used to separate wild anti-MRS

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Publication Date
Mon Aug 20 2018
Journal Name
Physical Review E
Dynamical density-functional-theory-based modeling of tissue dynamics: Application to tumor growth
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Publication Date
Fri Apr 01 2005
Journal Name
Ibn Al-haitham Journal For Pure And Applied Science
Synthesis and Characterization of Schiff Base Folic Acid Based Ligand and Its Complexes
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D-mannose sugar was used to prepare [benzoic acid 6-formyl-2,2-dimethyl-tetrahydrofuro[3,4-d][1,3]dioxol-4-yl ester] (compound A). The condensation reaction of folic acid with (compound A) resulted in the formation of new ligand [L]. These compounds were characterized by elemental analysis CHN, atomic absorption A.A, (FT-I.R.), (U.V.-Vis), TLC, E.S. mass (for electrospray), molar conductance, and melting point. The new tetradentate ligand [L], reacted with two moles of some selected metal ions and two moles of (2-aminophenol), (metal : ligand : 2-aminophenol) at reflux in water medium to give a series of new complexes of the general formula K2[M2(L)(HA)2] where M= Co(II), Ni(II), Cu(II) and Cd(II). These complexes were characterized by elem

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Publication Date
Sat Sep 30 2017
Journal Name
Al-khwarizmi Engineering Journal
Robot Arm Path Planning Using Modified Particle Swarm Optimization based on D* algorithm
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Abstract

Much attention has been paid for the use of robot arm in various applications. Therefore, the optimal path finding has a significant role to upgrade and guide the arm movement. The essential function of path planning is to create a path that satisfies the aims of motion including, averting obstacles collision, reducing time interval, decreasing the path traveling cost and satisfying the kinematics constraints. In this paper, the free Cartesian space map of 2-DOF arm is constructed to attain the joints variable at each point without collision. The D*algorithm and Euclidean distance are applied to obtain the exact and estimated distances to the goal respectively. The modified Particle Swarm Optimization al

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Publication Date
Thu Jun 30 2011
Journal Name
Al-khwarizmi Engineering Journal
Performance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems
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The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a

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Publication Date
Thu Mar 05 2026
Journal Name
Journal Of The College Of Basic Education
Fuzzy Nonparametric Regression Model Estimation Based on some Smoothing Techniques With Practical Application
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In this research, we use fuzzy nonparametric methods based on some smoothing techniques, were applied to real data on the Iraqi stock market especially the data about Baghdad company for soft drinks for the year (2016) for the period (1/1/2016-31/12/2016) .A sample of (148) observations was obtained in order to construct a model of the relationship between the stock prices (Low, high, modal) and the traded value by comparing the results of the criterion (G.O.F.) for three techniques , we note that the lowest value for this criterion was for the K-Nearest Neighbor at Gaussian function .

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