This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

Cipher security is becoming an important step when transmitting important information through networks. The algorithms of cryptography play major roles in providing security and avoiding hacker attacks. In this work two hybrid cryptosystems have been proposed, that combine a modification of the symmetric cryptosystem Playfair cipher called the modified Playfair cipher and two modifications of the asymmetric cryptosystem RSA called the square of RSA technique and the square RSA with Chinese remainder theorem technique. The proposed hybrid cryptosystems have two layers of encryption and decryption. In the first layer the plaintext is encrypted using modified Playfair to get the cipher text, this cipher text will be encrypted using squared

          Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discrimina

Background: Tooth wear is one of the most common problems in the older dentate population which results from the interaction of three processes (attrition, abrasion and erosion) and it affects all societies, different age groups, and all cultures. This study was achieved to evaluate the prevalence and distribution of tooth wear among institutionalized residents in Baghdad city\ Iraq. Subjects and Methods: This survey was accomplished on four private and one governmental institution in Baghdad city. One-hundred twenty three (61 males, 62 females) aged 50-89 years were participated in this study. The diagnosis and recording of tooth wear were according to criteria of Smith and Knight. Results: The prevalence of tooth wear was 100% with a mean

In the present work the Buildup factor for gamma rays were studied in shields from epoxy reinforced by lead powder and by aluminum powder, for NaI(Tl) scintillation detector size ( ×? ), using two radioactive sources (Co-60 and Cs-137). The shields which are used (epoxy reinforced by lead powder with concentration (10-60)% and epoxy reinforced by aluminum powder with concentration (10-50)% by thick (6mm) and epoxy reinforced by lead powder with concentration (50%) with thick (2,4,6,8,10)mm. The experimental results show that: The linear absorption factor and Buildup factor increase with increase the concentration for the powders which used in reinforcement and high for aluminum powder than the lead powder and decrease with inc

The research aims to investigate the effects of GMAW or MIG welding process on the mechanical properties of dissimilar aluminum alloys 2024-T351 and AA 6061- T651. A series of experimental techniques have been conducted to evaluate mechanical properties of the alloys, by carrying out hardness, tensile and bending tests for welded and un-welded specimens.

Metal inert gas (MIG) has been carried out on sheet metal using ER- 4043(AlSi₅) as a filler metal and argon as shielded gas. The welded joints were tested by X-ray radiography and Faulty pieces were excluded.

Welding joints without defects are subjected to heat treatment including heating the joints in furnace to 170 °C for half an hour then air cooling to rel

In this work, Pure and Cu: doped titanium dioxide nano-powder was prepared through a solid-state method. the dopant concentration [Cu/TiO₂ in atomic percentage (wt%)] is derived from 0 to 7 wt.%. structural properties of the samples performed with XRD revealed all nanopowders are of titanium dioxide having polycrystalline nature. Physical and Morphological studies were conducted using a scanning electronic microscope SEM test instrument to confirm the grain size and texture. The other properties of samples were examined using an optical microscope, Lee's Disc, Shore D hardness instrument, Fourier-transform infrared spectroscopy (FTIR), and Energy-dispersive X-ray spectroscopy (EDX). Results showed that the thermal conductivity

Cloud computing provides huge amount of area for storage of the data, but with an increase of number of users and size of their data, cloud storage environment faces earnest problem such as saving storage space, managing this large data, security and privacy of data. To save space in cloud storage one of the important methods is data deduplication, it is one of the compression technique that allows only one copy of the data to be saved and eliminate the extra copies. To offer security and privacy of the sensitive data while supporting the deduplication, In this work attacks that exploit the hybrid cloud deduplication have been identified, allowing an attacker to gain access to the files of other users based on very small hash signatures of

In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc