This paper describes the usage of OPNET simulation tool to evaluate VoIP over MPLS networks which employs RSVP protocol as QoS mechanism with SIP or H.323 as signaling protocols for setting up VoIP calls over the Internet. The results showed that the H.323 with RSVP over MPLS networks produced minimal end-to-end delay than SIP protocol; making it a good choice as a signaling protocol with VoIP over MPLS networks for places requiring minimal end-to-end delay, while SIP reported lowest delay jitter with minimal call setup time
Azeddien M. Sllame, Asra Hema, Huda Alnajem(3-2018)

This paper describes a comparative study of the performance of VoIP over wireless networks using OPNET tool. The simulation study is completed by running VoIP application in different network scenarios with IEEE 802.16 (UGS, ertPS) and IEEE 802.11 (b, g) with best effort service and interactive service The result clearly illustrated that the WiMax type ertPS has the best performance among all tested cases
Azeddien M. Sllame, Hana Soso, Mona Aown, Lamya Abdelmajeed(9-2016)

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)