This study provides valuable information on secondary microbial infections in H1N1 patients compared to Seasonal Influenza in Iraqi Patients. Nasopharynx  swabs were collected from  (12 ) patients  infected with Seasonal influenza (11  from Baghdad  and 1 Patient from south of Iraq) ,and ( 22 ) samples from patients with 2009 H1N1 ( 20 from Baghdad and  2 from  south of Iraq). The results show that the patients infected with 2009 H1N1 Virus were younger than healthy subjects and those infected with seasonal influenza. And the difference reached to the level of significance     (p< 0.01) compared with healthy subjects.Two cases infected with 2009 H1N1 virus (9.1%) were fro

             bjective researcher through this research to Put a theoretical framework to strategic orientation the center on the market in the business and the diagnosis and interpretation of the nature of the link between relationships and influencing strategic orientation dimensions Almtmthelh organizations (Entrepreneurial Orientation, market Orientation, Interaction Orientation) and  the performance of the Iraqi Private Banks The research community and the  level of dimensional and through the use of scale (Balanced Scorecard) Bmnzawradtha the four, the test of this research in the Iraqi banking sector (Gulf Commercial Bank

Due to that the Ultra Wide Band (UWB) technology has some attractive features like robustness to multipath fading, high data rate, low cost and low power consumption, it is widely use to implement cognitive radio network. Intuitively, one of the most important tasks required for cognitive network is the spectrum sensing. A framework for implementing spectrum sensing for UWB-Cognitive Network will be presented in this paper. Since the information about primary licensed users are known to the cognitive radios then the best spectrum sensing scheme for UWB-cognitive network is the matched filter detection scheme. Simulation results verified and demonstrated the using of matched filter spectrum sensing in cognitive radio network with UWB and pro

 

            This paper presents a study of wavelet self-organizing maps (WSOM) for face recognition. The WSOM is a feed forward network that estimates optimized wavelet based for the discrete wavelet transform (DWT) on the basis of the distribution of the input data, where wavelet basis transforms are used as activation function.

 

 

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 limitations of wireless sensor nodes are power, computational capabilities, and memory. This paper suggests a method to reduce the power consumption by a sensor node. This work is based on the analogy of the routing problem to distribute an electrical field in a physical media with a given density of charges. From this analogy a set of partial differential equations (Poisson's equation) is obtained. A finite difference method is utilized to solve this set numerically. Then a parallel implementation is presented. The parallel implementation is based on domain decomposition, where the original calculation domain is decomposed into several blocks, each of which given to a processing element. All nodes then execute computations in parall

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the