AIRCC's International Journal of Computer Science and Information Technology

S. M. ELseuofi ¹

Affiliations
1 Department of Programs, College of Community Service and Continuing Education, Umm Al-Qura University, SA

Abstract

In this paper, a Particle Swarm Optimization algorithm used to solve the quality of service routing problems is proposed. Each Multicast Particle Swarm Optimization system in the Multicast Particle Swarm Optimization system router will uses an efficient objective function that reflect one or more of the QoS parameters to evaluate the multicast Tree between one source node and multiple destination nodes according to Class of Service (CoS). We first classify the applications into classes according to its sensitivity to one or more QoS parameters. Our proposed multicast PSO router finds the multicast Tree with minimum cost subject to specific QoS parameter(s) and for the specific application that belong to appropriate CoS in computer networks. The multicast PSO router system is distributed at each node in communication network and it makes its decision based on a database of alternate routes between each pairs of nodes in the network dynamically. The simulation results explain that the proposed multicast PSO systems in the Multicast Particle Swarm Optimization system router exhibits a good quality of solution and a good rate of convergence to optimal solution for each CoS that lead to high speed response in computer networks.

Keywords

Multicasting, Particle Swarms Optimizer (PSO), QoS, Communication Networks, and Combinatorial Optimization.

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W. A. Awad ¹, S. M. ELseuofi ²

Affiliations
1 Math. and Comp. Sci. Dept., Port Said University, EG
2 Inf. System Dept., Ras El Bar High Inst., EG

Abstract

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Machine learning techniques now days used to automatically filter the spam e-mail in a very successful rate. In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and Rough sets) and of their applicability to the problem of spam Email classification. Descriptions of the algorithms are presented, and the comparison of their performance on the SpamAssassin spam corpus is presented.

Keywords

Spam, E-Mail Classification, Machine Learning Algorithms.

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