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Multiresolution hierarchical support vector machine for classification of large datasets
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Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in comparison with existing SVM algorithms.

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Publication Date
Mon Aug 01 2016
Journal Name
Journal Of Engineering
Preparation and Characterization of Activated Carbon for Adsorption of Fluoroquinolones Antibiotics
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           In this research, the preparation of a chemically activated carbon from date stones by using electric and microwave assisted K2CO3 activation was studied. The effect of radiation power, radiation time, and impregnation ratio on the yield and Iodine number on the activated carbons was investigated. The activated carbon characterizations were examined by its surface area, pore structure analysis, bulk density, moisture content, ash content, iodine number, FTIR, and scanning electron microscopy (SEM). The adsorption capacity was also studied by adsorption of fluoroquinolones antibiotics, CIP, NOR, and LEVO, by the prepared activated carbon.

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Publication Date
Mon Apr 10 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Evaluation of Antibacterial Activity of Ethanolic Extracts for Three Local Plants
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There is an increasing interest in the use of plant extracts as therapeutic agents, particularly their capacity to inhibit the growth of pathogenic microorganisms. In this study antibacterial effect of Malva sylvestris, Anastatica hierochuntica and Vitis vinifera leaves extracts were evaluated against Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis, Staphylococcus aureus and Proteus mirabilis. The in vitro antibacterial activity was performed using agar well diffusion method and the minimum inhibitory concentration (MIC) was determined by microtitration technique. The result indicated that the extract of V. vinifera leaves inhibited with the growth of gram-positive bacteria, as well as gram-negative bacteria while the extract

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Publication Date
Mon May 22 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
The Effect of Number of Training Samples for Artificial Neural Network
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 In this paper we study the effect of the number of training samples for  Artificial neural networks ( ANN ) which is necessary for training process of feed forward neural network  .Also we design 5 Ann's and train 41 Ann's which illustrate how good the training samples that represent the actual function for Ann's.

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Publication Date
Wed Aug 21 2019
Journal Name
International Journal On Advanced Science, Engineering And Information Technology
Evaluation of the Performance of Glasphalt Concrete Mixtures for Binder Course
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Publication Date
Sun Feb 25 2024
Journal Name
Iraqi Journal Of Agricultural Sciences
THE EFFICIENCY OF GUM-SILICA CMPOSITE FOR REMOVING OF WASTEWATER TURBIDITY
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A novel mixed natural coagulant has been developed to remove sewage pollutants and heavy metals from Qanat- al- Jayesh by using low cost adsorbent natural materials. In these materials, significant interaction contains Arabic gum mixed with extracted silica from rice husk ash (natural coagulants) by the Batch device approach, using two variables, pH values ranging from 5-8 and contact times between 0.25-5 hrs. All wastewater samples were collected after treatment by adsorbents and examined for determination of residual heavy metal concentrations: Pb, Ni, Zn and Cu by atomic absorption spectroscopy (AAS), turbidity, pH, total dissolved salts (TDS), electrical conductivity (EC) and total salinity (TS). The results obtained indicate Th

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Publication Date
Thu Oct 15 2015
Journal Name
Al Mustansyriah Journal Of Science
Comparison between (ARIMA) and (ANNs) models for estimating the relative humidity for Baghdad city
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The aim of the research is to study the comparison between (ARIMA) Auto Regressive Integrated Moving Average and(ANNs) Artificial Neural Networks models and to select the best one for prediction the monthly relative humidity values depending upon the standard errors between estimated and observe values . It has been noted that both can be used for estimation and the best on among is (ANNs) as the values (MAE,RMSE, R2) is )0.036816,0.0466,0.91) respectively for the best formula for model (ARIMA) (6,0,2)(6,0,1) whereas the values of estimates relative to model (ANNs) for the best formula (5,5,1) is (0.0109, 0.0139 ,0.991) respectively. so that model (ANNs) is superior than (ARIMA) in a such evaluation.

Publication Date
Wed Jan 24 2018
Journal Name
The Journal Of Physical Chemistry C
Novel Approach for Fabricating Transparent and Conducting SWCNTs/ITO Thin Films for Optoelectronic Applications
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Publication Date
Thu Aug 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
Some Estimation methods for the two models SPSEM and SPSAR for spatially dependent data
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ABSTRUCT

In This Paper, some semi- parametric spatial models were estimated, these models are, the semi – parametric spatial error model (SPSEM), which suffer from the problem of spatial errors dependence, and the semi – parametric spatial auto regressive model (SPSAR). Where the method of maximum likelihood was used in estimating the parameter of spatial error          ( λ ) in the model (SPSEM), estimated  the parameter of spatial dependence ( ρ ) in the model ( SPSAR ), and using the non-parametric method in estimating the smoothing function m(x) for these two models, these non-parametric methods are; the local linear estimator (LLE) which require finding the smoo

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Publication Date
Wed Jan 20 2021
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
The Necessary Condition for Optimal Boundary Control Problems for Triple Elliptic Partial Differential Equations
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       In this work, we prove that the triple linear partial differential equations (PDEs) of elliptic type (TLEPDEs) with a given classical continuous boundary control vector (CCBCVr) has a unique "state" solution vector (SSV)  by utilizing the Galerkin's method (GME). Also, we prove the existence of a classical continuous boundary optimal control vector (CCBOCVr) ruled by the TLEPDEs. We study the existence solution for the triple adjoint equations (TAJEs) related with the triple state equations (TSEs). The Fréchet derivative (FDe) for the objective function is derived. At the end we prove the necessary "conditions" theorem (NCTh) for optimality for the problem.

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Publication Date
Sun Oct 01 2017
Journal Name
International Journal Of Computer Science And Information Security (ijcsis)
Finite State Automata Generator for DNA Motif Template as Preparation Step for Motif Mining
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There are many tools and S/W systems to generate finite state automata, FSA, due to its importance in modeling and simulation and its wide variety of applications. However, no appropriate tool that can generate finite state automata, FSA, for DNA motif template due to the huge size of the motif template. In addition to the optional paths in the motif structure which are represented by the gap. These reasons lead to the unavailability of the specifications of the automata to be generated. This absence of specifications makes the generating process very difficult. This paper presents a novel algorithm to construct FSAs for DNA motif templates. This research is the first research presents the problem of generating FSAs for DNA motif temp

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