The objective of this work is to design and implement a cryptography system that enables the sender to send message through any channel (even if this channel is insecure) and the receiver to decrypt the received message without allowing any intruder to break the system and extracting the secret information. In this work, we implement an interaction between the feedforward neural network and the stream cipher, so the secret message will be encrypted by unsupervised neural network method in addition to the first encryption process which is performed by the stream cipher method. The security of any cipher system depends on the security of the related keys (that are used by the encryption and the decryption processes) and their corresponding le

In this paper, precision agriculture system is introduced based on Wireless Sensor Network (WSN). Soil moisture considered one of environment factors that effect on crop. The period of irrigation must be monitored. Neural network capable of learning the behavior of the agricultural soil in absence of mathematical model. This paper introduced modified type of neural network that is known as Spiking Neural Network (SNN). In this work, the precision agriculture system  is modeled, contains two SNNs which have been identified off-line based on logged data, one of these SNNs represents the monitor that located at sink where the period of irrigation is calculated and the other represents the soil. In addition, to reduce p

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability

Thin-walled members are increasingly used in structural applications, especially in light structures like in constructions and aircraft structures because of their high strength-to-weight ratio. Perforations are often made on these structures for reducing weight and to facilitate the services and maintenance works like in aircraft wing ribs. This type of structures suffers from buckling phenomena due to its dimensions, and this suffering increases with the presence of holes in it. This study investigated experimentally and numerically the buckling behavior of aluminum alloy 6061-O thin-walled lipped channel beam with specific holes subjected to compression load. A nonlinear finite elements analysis was used to obtain the

Sn effect on the phase transformation behavior, microstructure, and micro hardness of equiatomic Ni-Ti shape memory alloy was studied. NiTi and NiTiSn alloys were produced using vacuum induction melting process with alloys composition (50% at. Ni, 50% at.Ti) and (Ni 48% at., Ti 50% at., Sn 2% at.). The characteristics of both alloys were investigated by utilizing Differential Scanning Calorimetry, X- ray Diffraction Analysis, Scanning Electron Microscope, optical microscope and vicker's micro hardness test. The results showed that adding Sn element leads to decrease the phase transformation temperatures evidently. Both alloy samples contain NiTi matrix phase and Ti₂Ni secondary phase, but the Ti₂Ni phase content dec

This study is concerned with the effect of adding two kinds of ceramic materials on the mechanical properties of (Al-7%Si- 0.3%Mg) alloy, which are zirconia with particle size (20μm > P.S ≥ 0.1μm) and alumina with particle size (20μm > P.S ≥ 0.1μm) and adding them to the alloy with weight ratios (0.2, 0.4, 0.6, 0.8 and 1%). Stirring casting method has been used to make composite material by using vortex technique which is used to pull the particles to inside the melted metals and distributed them homogenously.

After that solution treatment was done to the samples at (520ºC) and artificial ageing at (170ºC) in different times, it has been noticed that the values of hardness is increased with the aging time of the o

AA3003-H14 aluminum alloy plates were welded by friction stir welding and TIG welding.
Fatigue properties of the welded joints were evaluated based on the superior tensile properties for
FSW at 1500 rpm rotational speed and 80 mm/min welding speed. However, there is not much
information available on effect of welding parameters with evolution of fatigue life of friction stir
welds. The present study experimentally analyzed fatigue properties for base, FSW, and TIG welds
of AA 3003-H14 aluminum alloy. Fatigue properties of FSW joints were slightly lower than the
base metal and higher than TIG welding.

The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed