Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through that region. The result

Most intrusion detection systems are signature based that work similar to anti-virus but they are unable to detect the zero-day attacks. The importance of the anomaly based IDS has raised because of its ability to deal with the unknown attacks. However smart attacks are appeared to compromise the detection ability of the anomaly based IDS. By considering these weak points the proposed
system is developed to overcome them. The proposed system is a development to the well-known payload anomaly detector (PAYL). By
combining two stages with the PAYL detector, it gives good detection ability and acceptable ratio of false positive. The proposed system improve the models recognition ability in the PAYL detector, for a filtered unencrypt

       The multimetric Phytoplankton Index of Biological Integrity (P-IBI) was applied throughout Rostov on Don city (Russia) on 8 Locations in Don River from April – October 2019. The P-IBI is composed from seven metrics: Species Richness Index (SRI), Density of Phytoplankton and total biomass of phytoplankton and Relative Abundance (RA) for blue-green Algae, Green Algae, Bacillariophyceae and Euglenaphyceae Algae. The average P-IBI values fell within the range of (45.09-52.4). Therefore, water throughout the entire study area was characterized by the equally "poor" quality. Negative points of anthropogenic impact detected at the stations are: Above the city of Rostov-on-Don (1 km, higher duct Aksai) was 38.57 i

The main aim of image compression is to reduce the its size to be able for transforming and storage, therefore many methods appeared to compress the image, one of these methods is "Multilayer Perceptron ". Multilayer Perceptron (MLP) method which is artificial neural network based on the Back-Propagation algorithm for compressing the image. In case this algorithm depends upon the number of neurons in the hidden layer only the above mentioned will not be quite enough to reach the desired results, then we have to take into consideration the standards which the compression process depend on to get the best results. We have trained a group of TIFF images with the size of (256*256)  in our research, compressed them by using MLP for each

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

In the present study, an attempt has been to develop a new water quality index (WQI) method that depends on the Iraqi specifications for drinking water (IQS 417, 2009) to assess the validity of the Euphrates River for drinking by classifying the quality of the river water at different stations along its entire reach inside the Iraqi lands. The proposed classifications by this method are: Excellent, Good, Acceptable, Poor, and Very poor. Eight water quality parameters have been selected to represent the quality of the river water these are: Ion Hydrogen Concentration (pH), Calcium (Ca), Magnesium (Mg), Sodium (Na), Chloride (Cl), Sulphate (SO_4), Nitrate (NO_3), and Total Dissolved Solids (TDS). The variation of the water quality parameters

Solar energy is one of the immeasurable renewable energy in power generation for a green, clean and healthier environment. The silicon-layer solar panels absorb sun energy and converts it into electricity by off-grid inverter. Electricity is transferred either from this inverter or from transformer, consumed by consumption unit(s) available for residential or economic purposes. The artificial neural network is the foundation of artificial intelligence and solves many complex problems which are difficult by statistical methods or by humans. In view of this, the purpose of this work is to assess the performance of the Solar - Transformer - Consumption (STC) system. The system may be in complete breakdown situation due to failure of both so

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

Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp