Arabic text classification using Polynomial Networks

In this paper, an Arabic statistical learning-based text classification system has been developed using Polynomial Neural Networks. Polynomial Networks have been recently applied to English text classification, but they were never used for Arabic text classification. In this research, we investigate the performance of Polynomial Networks in classifying Arabic texts. Experiments are conducted on a widely used Arabic dataset in text classification: Al-Jazeera News dataset. We chose this dataset to enable direct comparisons of the performance of Polynomial Networks classifier versus other well-known classifiers on this dataset in the literature of Arabic text classification. Results of experiments show that Polynomial Networks classifier is a competitive algorithm to the state-of-the-art ones in the field of Arabic text classification

MVF: A Novel Technique to Reduce the Voip Packet Payload Length

The adoption of the Voice over Internet Protocol (VoIP) system is growing due to several factors, including its meagre rate and the numerous contours that can be joined with VoIP systems. However, the wasteful utilisation of the computer network is an inevitable problem that limits the rapid growth of VoIP systems. The essential explanation behind this wasteful utilisation of the computer network bandwidth (BW) is the considerable preamble length of the VoIP packet. In this study, we invent a technique that addresses the considerable preamble length of the VoIP packet. The designed technique is known as the manikin voice frame (MVF). The primary idea of the MVF technique is to utilise the VoIP packet preamble tuples that are not essential to the voice calls, particularly client-to-client calls (voice calls between only two users). Specifically, these tuples will be utilised for reserving the data of the VoIP packet. In certain instances, this will make the VoIP packet data manikin or even make it empty. The performance assessment of the introduced MVF technique demonstrated that the utilisation of the computer network BW has enhanced by 33%. Along these lines, the MVF technique indicates potential progress in resolving the inefficient usage of the computer network BW