This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as compared with matching by minimum distance, gave (94%) and (83%) score by using group (1), (gp) and features respectively, which is much better than the minimum distance. Recognition using (gp) neural network (NN) gave a (94%) and (72%) score by using group (2), (gp) and features respectively, while the minimum distance gave (11%) and (33%) scores. Time consumption
through the recognition process using (NN) with (gp) is less than that minimum distance.
A series of new copolyimides containing pendant 1,3,4-oxadiazole moiety were synthesized via multisteps. In the first step five N-(5-substituted-1,3,4-oxadiazole-2-yl)maleamic acids were prepared via reaction of maleic anhydride with 2-amino-5-substituted-1,3,4-oxadiazoles. The obtained amic acids were dehydrated in the second step affording the corresponding N-(5-substituted-1,3,4-oxadiazole-2-yl) maleimides. In the third step the newly synthesized maleimides were introduced successfully in free radical copolymerization reaction with four vinylic monomers including acrylo nitrile, methacrylonitrile, methyl acrylate and methyl meth acrylate respectively producing twelve new copolymers having different physical properties which may serve
... Show MoreWhen optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat
... Show MoreBackground/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the
... Show MoreThe drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the
The purpose of this paper is to find an arc of degree five in 31 ,29),(2, =qqPG , with stabilizer group of type dihedral group of degree five 5 D and arcs of degree six and ten with stabilizer groups of type alternating group of degree five 5 A , then study the effect of 5 D and 5A on the points of projective plane. Also, find a pentastigm which has collinear diagonal points.
Objective: Detection the level of YKL-40 biochemical marker and vitamin D level in sera of Iraqi uterine cancer
females' patients.
Methodology: This study included 90 female volunteers, 30 of them were healthy volunteers who were
considered as a control group, while sixty serum samples were collected from women patients suffering from
uterine tumors (30 malignant and 30 fibroid benign tumors), benign cases were considered as a disease
control group for malignant tumors. The average age of those females was 30-75 years, which matched the
control group. All the samples were collected from Azady hospital in Kirkuk and the gynecologic department at
Medical City in Baghdad during October /2012 to May /2013. All the serum
Two new ligands Na2[ H3B (BDIA)].0.05H2O (L1)(BDIA = 1-Boranyl-2,3-
Dihydro-1H-Indol-3-yl)]Acetic Acid and Na3[H2B(BDIA)2].0.3H2O.0.3CH3Ph (L2)
were synthesized by reaction of NaBH4 with indole -3- acetic acid (IAA) . The
coordination properties of ligands were studied with Co(II) , Ni(II) , Cu(II) and
Pt(IV) ions. Characterization and structural aspects of the prepared compounds were
elucidated by 1HNMR, FTIR electronic spectra, magnetic susceptibility, elemental
and metal analysis, thermal analysis (TG & DTG) and conductivity measurements.
The obtained data for metal complexes suggested square planar geometry for
copper complexes, octahedral geometry for nickel and platinium complexes and
tetrahedral geom
Multiphase flow is a very common phenomenon in oil wells. Several correlation models, either analytical or experimental, have been investigated by various studies to investigate this phenomenon. However, no single correlation model was found to produce good results in all flow conditions. 14 models available on the Prosper software were selected for the purpose of calculating the pressure gradient inside wells within a range of different flow conditions. The pressure gradient was calculated using Prosper software, then compared with the measured gradient based on the production log test (PLT) data. This study was conducted on 31 wells from five different oil fields (Kirkuk, Jambur, Bai-Hassan, Al-Ahdab, and Rumaila). It is worth noting t
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