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

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 through our ca

<p class="0abstract">The rapidly growing 3D content exchange over the internet makes securing 3D content became a very important issue. The solution for this issue is to encrypting data of 3D content, which included two main parts texture map and 3D models. The standard encryption methods such as AES and DES are not a suitable solution for 3D applications due to the structure of 3D content, which must maintain dimensionality and spatial stability. So, these problems are overcome by using chaotic maps in cryptography, which provide confusion and diffusion by providing uncorrelated numbers and randomness. Various works have been applied in the field of 3D content-encryption based on the chaotic system. This survey will attempt t

This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper,

The subject of an valuation of quality of construction projects is one of the topics which it becomes necessary of the absence of the quantity standards in measuring the control works and the quality valuation standards in constructional projects. In the time being it depends on the experience of the workers which leads to an apparent differences in the valuation.

The idea of this research came to put the standards to evaluate the quality of the projects in a special system depending on quantity scale nor quality specifying in order to prepare an expert system “ Crystal “ to apply this special system to able the engineers to valuate the quality of their projects easily and in more accurate ways.

Ensuring security, integrity, and reliability of the election process consider as the main challenges in the electronic voting system. This paper describes the e-voting system by integrating the biometric authentication, advanced encryption, and watermarking techniques towards meeting such challenges. The system employs the fingerprint authentication by utilizing the Scale-Invariant Feature Transform (SIFT) for verifying the identity of the voter to ensure genuineness and non-repudiation of the service. The vote will be encrypted with the AES-GCM technique to be employed in securing the voting process, thus ensuring both data privacy and integrity. Hybrid Blind Watermarking employs the technique of Discrete Wavelet Transform (DWT) a

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

Translating culture-specific proverbs (CSPs) is a challenging task since they often occur in a peculiar context. Further, CSPs are intended to imply meanings that extend far beyond the literal meaning of such a kind of proverbs. As far as English and Arabic are concerned, translators often encounter problems in translating CSPs due to cultural differences between the source language(SL) and the target language (TL) as well as what seems to be the lack of equivalence for some CSPs.

In view of this, the present study aims at investigating the translation of CSPs in three English-Arabic dictionaries of proverbs, namely Dictionary of Common English Proverbs Translated and Explained (2004), One thousand and One English Pr