Deoxyribonucleic acid (DNA) micro-arrays present a powerful means of observing thousands of gene terms levels at the same time. They consist of high dimensional datasets, which challenge conventional clustering methods. The data’s high dimensionality calls for Self Organizing Maps (SOMs) to cluster DNA micro-array data. The DNA micro-array dataset are stored in huge biological databases for several purposes . The proposed methods are based on the idea of selecting a gene subset to distinguish all classes, it will be more effective to solve a multi-class problem, and we will propose a genetic programming (GP) based approach to analyze multi-class micro-array datasets. This biological dataset will be derived from multiple biological databases. The procedure responsible for extracting datasets called DNA-Aggregator. We will design a biological aggregator, which aggregates various datasets via DNA micro-array community-developed ontology based upon the concept of semantic Web for integrating and exchanging biological data. Our aggregator is composed of modules that retrieve the data from various biological databases. It will also enable queries by other applications to recognize the genes. The genes will be categorized in groups based on a classification method, which collects similar expression patterns. Using a clustering method such as k-mean is required either to discover the groups of similar objects from the biological database to characterize the underlying data distribution. arabic 10 English 60
Mariam Abojela Msaad, Zakaria Suliman Zubi(1-2010)

A performance evaluation of sound recognition techniques in recognizing some spoken Arabic words, namely digits from zero to nine, is proposed. One of the main characteristics of all Arabic digits is polysyllabic words except for zero. The performance analysis is based on different features of phonetic isolated Arabic digits. The main aim of this paper is to compare, analyze, and discuss the outcomes of spoken Arabic digits recognition systems based on three recognition features: the Yule-Walker spectrum features, the Walsh spectrum features, and the Mel frequency Cepstral coefficients (MFCC) features. The MFCC based recognition system achieves the best average correct recognition. On the other hand, the Yule-Walker based recognition system achieves the worst average correct recognition. arabic 7 English 60
A. Ganoun, I. Almerhag(6-2012)

This paper describes a Fat tree based Network-on- Chip system using MPLS networking technique. A discrete-event simulator has been developed in C++ in in order to demonstrate the applicability of the MPLS to Network-on-Chip systems. The correctness and the efficiency of the MPLS based Fat networkon- chip system is measured against well-known wormhole with virtual channels technique. The results of the MPLS-based NoC in terms of throughput and latency are clearly proves the applicability of the MPLS to implement future NoC designs arabic 8 English 38
Azeddien M. Sllame, Nagwa Abobaker Salama(11-2016)