In this paper, silicon carbonitried thin films were prepared by the method of photolysis of the silane (SiH4) and ethylene (C2H4) gases, with and without ammonia gas (NH3), which is represented by the ratio between the (PNH3) and (PSiH4 + PC2H4 + PNH3), (which assign by the letter X), X has the values (0, 0.13, 0.33). This method carried out by using TEA-CO2 laser, on glass substrate at (375 oC), deposition rate (0.416-0.833) nm/pulse thin film thickness of (500-1000) nm. The optical properties of the films were studied by using Absorbance and Transmittance spectrums in wavelength range of (400-1100) nm, the results showed that the electronic transitions is indirect and the energy gap for the SiCN films increase with increasing of nitrog

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

The extracting of personal sprite from the whole image faced many problems in separating the sprite edge from the unneeded parts, some image software try to automate this process, but usually they couldn't find the edge or have false result. In this paper, the authors have made an enhancement on the use of Canny edge detection to locate the sprite from the whole image by adding some enhancement steps by using MATLAB. Moreover, remove all the non-relevant information from the image by selecting only the sprite and place it in a transparent background. The results of comparing the Canny edge detection with the proposed method shows improvement in the edge detection.

In this paper ,six new mixed metal ligand complexes are reported with Cephalexin (Ceph.H)as a primary ligand and Dimethylglyoxime (DMG) as secondary ligand with metal Chloride [MCl2 .nH2O. M=Mn(II),Co(II),Cu(II),Ni(II) and Zn(II),n=0-6] ,CrCl3.6H2O.The complexes are of (1:1:1)(Metal:Ligand: Ligand) Stoichiometry.The structures of these complexes are confirmed by using FT-IR and UV- electronic spectroscopies, magnetic moments, melting points, molar conductivity measurements and the metal % analysis revealed that the complexes analyze indicates a four coordinated as (A)=[M(HDMG) (Ceph)] .M=[Ni(II)and Zn(II).Six coordinated as (B) = K2[M(DMG)(CePh)(H2O)]. M= Mn (II),Co(II) and Cu(II) and (C)=[Cr(DMG)(Ceph)]Cl2. Interestingly, the in-vitro anti

KE Sharquie, AA Noaimi, HA Al-Mudaris, Journal of Cosmetics, Dermatological Sciences and Applications, 2013 - Cited by 4

This study aims to assess the water quality index (WQI) according to the Canadian Council of Ministers of the Environment's Water Quality Index method (CCME WQI). Four locations (measurement stations) are selected along the Tigris River, in Iraq.  Two of them are located in the north near Mosul City, (Mosul Dam and Mosul city), and the other two are located in the south near Al-Amarah city, (Ali Garbi and Al-Amarah). The water data collected is for the period 2011 to 2013, including eleven water quality parameters. These are magnesium (Mg⁺²), calcium (Ca²⁺), potassium (K⁺), sodium (Na⁺), sulfate (SO₄^2-), chloride (Cl^-), nitrate (NO₃<

Objectives This work presents laser coating of grade 1 pure titanium (Ti) dental implant surface with sintered biological apatite beta-tricalcium phosphate (β-TCP), which has a chemical composition close to bone. Materials and methods Pulsed Nd:YAG laser of single pulse capability up to 70 J/10 ms and pulse peak power of 8 kW was used to implement the task. Laser pulse peak power, pulse duration, repetition rate and scanning speed were modulated to achieve the most homogenous, cohesive and highly adherent coat layer. Scanning electron microscopy (SEM), energy dispersive X-ray microscopy (EDX), optical microscopy and nanoindentation analyses were conducted to characterise and evaluate the microstructure, phases, modulus of elasticity