The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Convolutional Neural Network (CNN) has been chosen as a better option for the training process because it produces a high accuracy. The final accuracy has reached 91.18% in five different classes. The results are discussed in terms of the probability of accuracy for each class in the image classification in percentage. Cats class got 99.6 %, while houses class got 100 %.Other types of classes were with an average score of 90 % and above.
This current study was built on creating four electrodes based on molecularly imprinted polymers (MIPs). As the template using Cefalexin (CFX), 1-vinyl imidazole (VIZ) and vinyl acetate (VA) as monomer, and N, N-methylene bis acrylamide (MBAA) as cross-linkers and benzoyl peroxide as the initiator, two MIPs were prepared. The same composition was used in non-impressed polymers (NIPs) preparation, but without the template (Cefalexin). For the membranes preparation, numerous plasticizers, such as tri-oly phosphate (TOP) and di-octyl phthalate (DOP), were used in the PVC matrix, slop, detection limit, lifetime, and linearity range of CFX-MIPs electrodes are characteristics &nb
... Show MoreDeep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod
... Show MoreThis work was conducted to determine the volumetric mass transfer coefficient (Ky.a) infixed bed adsorption using hexane-benzene mixture by adsorption onto a fixed bed of white silica gel. Benzene concentration was measured by gas chromatography. The effect of feed flow rate and initial concentration of benzene in hexane-benzene mixture on the volumetric mass transfer coefficient and on the adsorption capacity of silica gel was investigated.
In general, the volumetric mass transfer coefficient increases with increasing hexane flow rate, and with increasing initial concentration of benzene in the mixture. The ultimate value of (Ky.a) was at 53 ml/min of hexane flow rate with benzene initial concentration of (6.53 wt. %), and it wa
... Show MoreAcute appendicitis is the most common surgical abdominal emergency. Its clinical diagnosis remains a challenge to surgeons, so different imaging options were introduced to improve diagnostic accuracy. Among these imaging modality choices, diagnostic medical sonography (DMS) is a simple, easily available, and cost effective clinical tool. The purpose of this study was to assess the accuracy of DMS, in the diagnosis of acute appendicitis compared to the histopathology report, as a gold standard. Between May 2015 and May 2016, 215 patients with suspected appendicitis were examined with DMS. The DMS findings were recorded as positive and negative for acute appendicitis and compared with the histopathological results, as a gold standard
... Show MoreThe estimation of the parameters of Two Parameters Gamma Distribution in case of missing data has been made by using two important methods: the Maximum Likelihood Method and the Shrinkage Method. The former one consists of three methods to solve the MLE non-linear equation by which the estimators of the maximum likelihood can be obtained: Newton-Raphson, Thom and Sinha methods. Thom and Sinha methods are developed by the researcher to be suitable in case of missing data. Furthermore, the Bowman, Shenton and Lam Method, which depends on the Three Parameters Gamma Distribution to get the maximum likelihood estimators, has been developed. A comparison has been made between the methods in the experimental aspect to find the best meth
... Show MoreA mathematical model was proposed to study the microkinetics of esterification reaction of oleic acid with ethanol over prepared HY zeolite catalyst. The catalyst was prepared from Iraqi kaolin source and its properties were characterized by different techniques. The esterification was done under different temperature (40 to 70˚C) with 6:1 for molar ratio of ethanol to oleic acid and 5 % catalyst loading.
The microkinetics study was done over two period of time each period was examined individually to calculate the reaction rate constant and activation energy. The impact of the mass transfer resistance to the reactant was also investigated; two different studies have been accomplished to do this purpose.
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... Show MoreThe reduction of vibration properties for composite material (woven roving E-glass fiber plies in thermosetting polyester matrix) is investigated at the prediction time under varied combined temperatures (60 to -15) using three types of boundary conditions like (CFCF, CCCF, and CFCC). The vibration properties are the amplitude, natural frequency, dynamic elastic moduli (young modulus in x, y directions and shear modulus in 1, 2 plane) and damping factor. The natural frequency of a system is a function of its elastic properties, dimensions, and mass. The woven roving glass fiber has been especially engineered for polymer reinforcement; but the unsaturated thermosetting polyester is widely used, offering a good balance of vibration p
... Show MoreA numerical investigation has been performed to study the effect of eccentricity on unsteady state, laminar aiding mixed convection in a horizontal concentric and eccentric cylindrical annulus. The outer cylinder was kept at a constant temperature
while the inner cylinder was heated with constant heat flux. The study involved numerical solution of transient momentum (Navier-Stokes) and energy equation using finite difference method (FDM), where the body fitted coordinate system (BFC) was
used to generate the grid mesh for computational plane. The governing equations were transformed to the vorticity-stream function formula as for momentum equations and to the temperature and stream function for energy equation.
A computer progra
ArcHydro is a model developed for building hydrologic information systems to synthesize geospatial and temporal water resources data that support hydrologic modeling and analysis. Raster-based digital elevation models (DEMs) play an important role in distributed hydrologic modeling supported by geographic information systems (GIS). Digital Elevation Model (DEM) data have been used to derive hydrological features, which serve as inputs to various models. Currently, elevation data are available from several major sources and at different spatial resolutions. Detailed delineation of drainage networks is the first step for many natural resource management studies. Compared with interpretation from aerial photographs or topographic maps, auto
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