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.
Experiments were carried out to investigate natural convection heat transfer in an inclined uniformly heated circular cylinder . The effects of surface heat flux and angle of inclination on the temperature and local Nusselt number variations along the cylinder surface are discussed . The investigation covers heat flux range from 92 W/m² to 487 W/m², and angles of inclination 0° ( horizontal) , 30° , 60° and 90° (vertical) . Results show an increase in the natural convection as heat flux increases and as angle of inclination moves from vertical to horizontal position. An empirical equation of average Nusselt number as a function of Rayliegh number was deduced for each angle of inclination .
Deep 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 MoreThe current research discusses “The impact of the critical factors for the transfer of knowledge on opportunities and strategic success ", the attention have been increased on knowledge transfer and strategic success subjects because on being one of the important and contemporary issues, which have a significant impact on the existence of organizations and its future. The research aims to identify the critical factors for knowledge transfer in private high education environment which enables (the college community surveyed) to achieve strategic success, also the research sought to answer questions related to research problem by testing a number of major and minor hypothes in impact, in order to test the hypotheses I used a fiel
... Show Moreالاهمية الاقتصادية للموانىء الحرة مع الاشارة الى المنطقة الاقتصادية الحرة المقترحة في ميناء الفاو
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
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The 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
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