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Hybrid approaches to feature subset selection for data classification in high-dimensional feature space
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This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spent to achieve the best classification accuracy.

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Publication Date
Thu Jun 01 2017
Journal Name
Journal Of Huazhong University Of Science And Technology [medical Sciences]
New mini dental implant attachments versus O-ring attachment after cyclic aging: Analysis of retention strength and gap space
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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Journal Of Science
Numerical and Analytical Solutions of Space-Time Fractional Partial Differential Equations by Using a New Double Integral Transform Method
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  This work discusses the beginning of fractional calculus and how the Sumudu and Elzaki transforms are applied to fractional derivatives. This approach combines a double Sumudu-Elzaki transform strategy to discover analytic solutions to space-time fractional partial differential equations in Mittag-Leffler functions subject to initial and boundary conditions. Where this method gets closer and closer to the correct answer, and the technique's efficacy is demonstrated using numerical examples performed with Matlab R2015a.

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Publication Date
Sat Sep 15 2018
Journal Name
Journal Of Baghdad College Of Dentistry
Serum Tumor Necrosis Factor Alpha and High Sensitive C-Reactive protein as Biomarkers in Periodontitis in Iraqi Patients with Osteoarthritis
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Background: Periodontitis (PD) is well-known chronic disease affecting the periodontal ligament and alveolar bone, Osteoarthritis (OA) is a chronic joint disease with compound reasons characterized by synovial inflammation, subchondral bone remodeling, also the formation of osteophytes, that cause cartilage degradation. Chronic periodontitis and osteoarthritis are considered widely prevalent diseases and related to tissue destruction due to chronic inflammation in general health and oral health. The aim of this study is todetermine the association of chronic periodontitis and osteoarthritits in patients by analysing tumor necrosis factor alpha TNFα and high sensitive c-reactive protein (hsCRP) in the serum. Materials and Method: A tot

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Publication Date
Wed Feb 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
The role of accounting responsibility in the success of the reform of financial and administrative / applied research in high School
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The reform process is a dynamic process going on, especially the administrative and financial reform, which contributes to the work and directing operations towards success and continuous development, which requires determining the validity and the responsibility and the rights and duties of all officials in the school settings (within the formations educational institutions) for the purpose of reducing the administrative and financial corruption, and then ensure management efficient and effective way by taking advantage of the physical, financial and human resources available to achieve the greatest benefit at the lowest cost to the fact that the follow-up performance on an ongoing basis in accordance with the specific of powers

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Publication Date
Mon Mar 25 2019
Journal Name
Al-academy
Educational -Learning Design in the Achievement and Motivation of Students in the High School towards Art Education: كنعان غضبان حبيب
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  The present study aimed at ((building an educational -learning design based on the theory of Merrill in (CDT) and measuring the effectiveness of this design in the motivation and achievement of the high school fifth grade students to art education in the subject of the history of modern art)). The research community is made of fifth grade preparatory students in the secondary school of Umm Ayman in the Directorate of Education of Baghdad / Ar-Rusafa in a simple random way. The study sample (58 students) was chosen from section (e) to study according to Merrill theory (CDT) and section (d) to study according to the traditional way.
The pilot design of the control and experimental equivalent groups that have partial control in t

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Publication Date
Sun Jun 01 2025
Journal Name
Journal Of Physics: Conference Series
Determination of Radioactivity Levels in Some Rice Samples available in the Iraqi Markets Using the system of High-purity Germanium
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Abstract<p>This study aimed to determine the radioactivity and radiation hazard indicators of rice samples potentially for human consumption. Gamma spectroscopy was used to calculate the specific activity of natural and artificial radionuclides (<sup>238</sup>U, <sup>232</sup>Th, <sup>40</sup>K, and <sup>137</sup>Cs) in local and imported rice samples collected from local markets in Baghdad Governorate, Iraq, in addition to various radiological hazard indices. The radionuclide concentrations in the samples varied from 2.123 ± 1.457 Bq/kg to 13.032 ± 3.610 Bq/kg for <sup>238</sup>U, 2.906 ± 1.705 Bq/kg to 17.290 ± 4.158 Bq/kg for <sup>232</sup></p> ... Show More
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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
Fri Nov 21 2025
Journal Name
Journal Of Advances In Information Technology
Towards Accurate SDG Research Categorization: A Hybrid Deep Learning Approach Using Scopus Metadata
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The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

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Publication Date
Fri May 01 2026
Journal Name
Statistics, Optimization &amp; Information Computing
Hybrid Modeling of Currency Circulation Volatility: Evidence from the Central Bank of Iraq
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The currency in circulation is a key element of the monetary supply system of the Iraqi economy because itreflects the level of economic activity and the liquidity level in the market. It can be expressed as an important tool when formulating monetary policy. This research aims to analyze and forecast the behavior of the currency in circulation in Iraq using the ARMA-GARCH model for monthly data from 2004 to 2025 to understand the dynamics of monetary liquidity, The sample was divided into two parts: approximately 80% for the training set (2004-2021), and approximately 20% for the testing set (2022-2025). Data were analyzed in Python using many packages. The results showed that the time series was initially non-stationary but became

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Publication Date
Mon Mar 20 2023
Journal Name
2023 International Conference On Information Technology, Applied Mathematics And Statistics (icitams)
Hybrid Color Image Compression Using Signals Decomposition with Lossy and Lossless Coding Schemes
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