The growing volume of spam Emails has generated the need for a more precise anti-spam filter to detect unsolicited Emails. One of the most common representations used in spam filters is the Bag-of-Words (BOW). Although BOW is very effective in the classification of the emails, it has a number of weaknesses. In this paper, we present a hybrid approach to spam filtering based on the Neural Network model Paragraph Vector-Distributed Memory (PV-DM). We use PV-DM to build up a compact representation of the context of an email and also of its pertinent features. This methodology represents a more comprehensive filter for classifying Emails. Furthermore, we have conducted an empirical experiment using Enron spam and Ling spam datasets, the results of which indicate that our proposed filter outperforms the PV-DM and the BOW email classification methods.
Abstract-Simple Web services can be provided directly from one mobile device acting as a server. However, complex services need a mobile cloud to provide computing resources and infrastructure to support seamless provision of its Web services in a light weight manner. Our approach focuses on the architecture of this mobile cloud, which consists of a set of collaborative mobile devices that relies on an Extended Mobile Host Complex Web service Framework (EMHCWF). The main building blocks of EMHCWF with their associated distributed mechanisms are defined and tested experimentally. Furthermore, the safety properties of mobile cloud infrastructure have been validated using formal methods that are based on process algebra.
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