A newly flow injection-turbidimetric method characterized by it is speed and sensitivity has been developed for the determination of Amiloride in pure and pharmaceutical preparations. It is based on the formation of yellowish white precipitate for the Amiloride-phosphomolybidic acid ion pair in aqueous medium. Turbidity was measured by Ayah 6Sx1-T-1D solar cell CFI analyser via the attenuation of incident light from the surfaces precipitated particles at 0-180. The Chemical and physical parameters were investigated. Linear dynamic range for the attenuation of incident light versus Amiloride concentration was of 0.005-10 mmol.L-1, with the correlation coefficient (r) of 0.9986 , while the percentage linearity (r2%) was 99.71%. The L.O.

A newly flow injection-turbidimetric method characterized by it is speed and sensitivity has been developed for the determination of Amiloride in pure and pharmaceutical preparations. It is based on the formation of yellowish white precipitate for the Amiloride-phosphomolybidic acid ion pair in aqueous medium. Turbidity was measured by Ayah 6Sx1-T-1D solar cell CFI analyser via the attenuation of incident light from the surfaces precipitated particles at 0-180. The Chemical and physical parameters were investigated. Linear dynamic range for the attenuation of incident light versus Amiloride concentration was of 0.005-10 mmol.L-1, with the correlation coefficient (r) of 0.9986 , while the percentage linearity (r2%) was 99.71%. The L.O.

 The aim of this paper, is to discuss several high performance training algorithms fall into two main categories. The first category uses heuristic techniques, which were developed from an analysis of the performance of the standard gradient descent algorithm. The second category of fast algorithms uses standard numerical optimization techniques such as: quasi-Newton . Other aim is to solve the drawbacks related with these training algorithms and propose an efficient training algorithm for FFNN

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. This work modernize the feedforward neural network, so the secret message will be encrypted by unsupervised neural network method to get the cipher text that can be decrypted using the same network to get the original text. 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 lengths. In this work, the key is the final weights

The research abstract included introduction and the importance of the research, also included display of the problem represented by weakness for the players when performing some of the basic skills in badminton and the shuttle not reaching to the back corners of the court which gives the player the opportunity to win through applying the pressure on the opponent and make him away from the control center(T) which definitely required level of a collection muscular strength contributed in performance perhaps this related to a number of reasons related with weakness in physical changes especially explosive and characterized by speed forces for the badminton players and be acquainted with them and knowing the extent of their effect in performanc

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

     Temperature predicting is the utilization to forecast the condition of the temperature for an upcoming date for a given area. Temperature predictions are done by gathering quantitative data in regard to the current state of the atmosphere. In this study, a proposed hybrid method to predication the daily maximum and minimum air temperature of Baghdad city which combines standard backpropagation with simulated annealing (SA). Simulated Annealing Algorithm are used for weights optimization for recurrent multi-layer neural network system. Experimental tests had been implemented using the data of maximum and minimum air temperature for month of July of Baghdad city that got from local records of Iraqi Meteorological O

The continuous increases in the size of current telecommunication infrastructures have led to the many challenges that existing algorithms face in underlying optimization. The unrealistic assumptions and low efficiency of the traditional algorithms make them unable to solve large real-life problems at reasonable times.
The use of approximate optimization techniques, such as adaptive metaheuristic algorithms, has become more prevalent in a diverse research area. In this paper, we proposed the use of a self-adaptive differential evolution (jDE) algorithm to solve the radio network planning (RNP) problem in the context of the upcoming generation 5G. The experimental results prove the jDE with best vecto