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

This paper demonstrates the construction of a modern generalized Exponential Rayleigh distribution by merging two distributions with a single parameter. The "New generalized Exponential-Rayleigh distribution" specifies joining the Reliability function of exponential pdf with the Reliability function of Rayleigh pdf, and then adding a shape parameter for this distribution. Finally, the mathematical and statistical characteristics of such a distribution are accomplished

Gypsum is one of the important construction materials in Iraq in plastering surfaces and gypsum board , the ability of gypsum to give a comfortable an aesthetic ambiance as a construction material increase the need of gypsum , The particle size , total surface area and particle size distribution were factors affecting plaster properties used for construction properties . In this study gypsum paste was used with different mixing ratios of particle size and studied the physical properties of these types of pastes named (standard consistency ,setting time ,density) and compressive strength . The results showed that the water to gypsum ratio increased with increasing the fineness of the gypsum to (0.75%) and the setting time to the maxi

Because the Coronavirus epidemic spread in Iraq, the COVID-19 epidemic of people quarantined due to infection is our application in this work. The numerical simulation methods used in this research are more suitable than other analytical and numerical methods because they solve random systems. Since the Covid-19 epidemic system has random variables coefficients, these methods are used. Suitable numerical simulation methods have been applied to solve the COVID-19 epidemic model in Iraq. The analytical results of the Variation iteration method (VIM) are executed to compare the results. One numerical method which is the Finite difference method (FD) has been used to solve the Coronavirus model and for comparison purposes. The numerical simulat

Because the Coronavirus epidemic spread in Iraq, the COVID-19 epidemic of people quarantined due to infection is our application in this work. The numerical simulation methods used in this research are more suitable than other analytical and numerical methods because they solve random systems. Since the Covid-19 epidemic system has random variables coefficients, these methods are used. Suitable numerical simulation methods have been applied to solve the COVID-19 epidemic model in Iraq. The analytical results of the Variation iteration method (VIM) are executed to compare the results. One numerical method which is the Finite difference method (FD) has been used to solve the Coronavirus model and for comparison purposes. The numerical simulat

The purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals

In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to dete

In this study, the relationship between the bare soil temperature with respect to its salinity is presented, the bare soil feature is considered only by eliminating all other land features by classifying the site location by using the support vector machine algorithm, in the same time the salinity index that calculated from the spectral response from the satellite bands is calibrated using empirical salinity value calculated from field soil samples. A 2D probability density function is used to analyze the relationship between the temperature rising from the minimum temperature (from the sunrise time) due to the solar radiation duration tell the time of the satellite capturing the scene image and the calibrated salinity index is presented. T

This paper introduces a complete design and simulation of a controller for the double fed induction generator (DFIG) turbine. The work also included the solar updraft tower (SUT) design to supply Al-Mahmoudia hospital in Baghdad/Iraq. The design includes the daily average load estimation, annual solar irradiance and, temperature monitoring, and logging.

According to the data obtained from the Ministry of Science and Technology, Baghdad has low wind speed. Therefore, the (SUT) has been designed to generate electrical power depending on the difference between the external and internal air temperature. The temperature difference will generate a suitable airspeed to drive the wind turbine, connected to the proposed (DFIG) generators

Forecasting is one of the important topics in the analysis of time series, as the importance of forecasting in the economic field has emerged in order to achieve economic growth. Therefore, accurate forecasting of time series is one of the most important challenges that we seek to make the best decision, the aim of the research is to suggest employing hybrid models to predict daily crude oil prices. The hybrid model consists of integrating the linear component, which represents Box Jenkins models, and the non-linear component, which represents one of the methods of artificial intelligence, which is the artificial neural network (ANN), support vector regression (SVR) algorithm and it was shown that the proposed hybrid models in the predicti