Blockchain has garnered the most attention as the most important new technology that supports recent digital transactions via e-government. The most critical challenge for public e-government systems is reducing bureaucracy and increasing the efficiency and performance of administrative processes in these systems since blockchain technology can play a role in a decentralized environment and execute a high level of security transactions and transparency. So, the main objectives of this work are to survey different proposed models for e-government system architecture based on blockchain technology implementation and how these models are validated.  This work studies and analyzes some research trends focused on blockchain

Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algorithms speak of the problem of imbalanced data, where there are situations that belong to more classes than others. Imbalanced data result in poor performance and bias to a class without other classes. In this paper, we proposed three techniques based on the Over-Sampling (O.S.) technique for processing imbalanced dataset and redistributing it and converting it into balanced dataset. These techniques are (Improved Synthetic Minority Over-Sampling Technique (Improved SMOTE),  Border

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

the effecth of some chemicals on growth of two azotobacter chroococcum and aniline caused significant increase of growth

The experiment was conducted to investigate the effect of prey type (Artemia nauplii, mosquito larvae and paramecium) on some reproductive aspects in crustacean zooplankton M. albidus which included reproductive period, post reproductive period, period spend to egg appearance and the period from appearance of egg to nauplii releasing. Results revealed that females fed on mosquito larvae had the highest mean of postreproductive period and lowest mean of the period spend to egg appearance, which differed significantly (P < 0.05) compared with the means of females who fed on Artemia nauplii and paramecium on the other hand the differences were not significant in reproductive period and the period from appearance of egg to nauplii releasing.

In this study, composite materials were prepared using unsaturated polyester resin as binder with two types of fillers (sawdust and chopped reeds). The molding method is used to prepare sheets of UPE / sawdust composite and UPE / chopped reeds composite. The mechanical properties were studied including flexural strength and Young's modulus for the samples at normal conditions (N.C). The Commercial wood, UPE and its composite samples were immersed in water for about 30 days to find the weight gain (Mt%) of water for the samples, also to find the effect of water on their flexural strength and Young's modulus. The results showed that the samples of UPE / chopped reeds composite gained highest values of flexural strength (24.