International Journal Information and Communication Technology Research
Volume:7 Issue: 4, Autumn 2015

There has been an increasing demand for automatic classification of digital signal formats during the past decades, which seems to be a continouning trend in future too. Most of the previously proposed classifiers can only classify a few kinds of digital signals and/or a low order of digital signals. In addition, They usually require a high level of Signal to Noise Ratio (SNR). This paper presents a hybrid intelligent system for recognition of digital signal types, including three main modules: a feature extraction module, a classifier module, i.e., a Probabilistic Neural Networks (PNN), and an optimization module. Simulation results validate the high recognitionaccuracy of the proposed system even at low SNRs.

In this paper, the stable or long life route selection problem in Mobile Ad-hoc Wireless Networks (MANETs) is addressed. The objective is to develop an on demand routing scheme to find a long life route between a given source and destination assuming each node has an estimate of neighbors’ mobilities. Formulating the problem as a MinMax optimization one, we use a dynamic programming based scheme for route selection. The proposed MinMax Routing Algorithm (MRA) is an on demand routing that can be implemented in the traditional Ad-hoc On- Demand Distance Vector (AODV) structure. In the route request phase, tail subproblems of finding the most stable route from the source to each intermediate node are solved. MRA finds the most stable route in the route reply phase deploying the solutions of these subproblems. Simulation results using NS2 simulator are provided to show the performance of MRA compared to AODV and stable AODV schemes in terms of the lifetime of selected route and routing overhead. Also, the tradeoff between the route discovery delay and finding more stable routes is discussed and justified by simulations.

In recent years, online social networks (OSNs) have been expanded with a lot of facilities and many users and enthusiasts have joined to OSNs. On the other hand, the proportion of low-value content such as spam is rapidly growing and releasing in the OSNs. Sometimes the spam advertising purposes, commercial purposes or spreading lies in the different mailing lists are placed and shipped in bulk to send for social network users. Spams not only damage the interests of users, usage time and bandwidth, but also are a threat to productivity, reliability and security of the network. In this paper, we present an online spam filtering system that can be deployed as a component of the OSN platform to inspect message generated by users in real time. Our filtering method is working on the basis of different features such as like, replay, hash tag, followers, and the existing URLs in the posts of Facebook social network. We employ three clustering algorithms for this purpose and we also use naïve Bayes and decision tree to detect spam from non-spam. We evaluate the system using 2000 wall posts collected from Facebook.

A dynamic threshold secret sharing (DTSS) scheme allows the secret to be updated without changing the shares. The first DTSS scheme was proposed by Laih et al. in 1991. Several other schemes based on different methods have been proposed since then. In 2007, Chen et al. proposed a verifiable DTSS scheme based on elliptic curves and bilinear maps, which is almost efficient. In this paper, we propose an alternative verifiable DTSS scheme using elliptic curves and bilinear maps. The proposed scheme is computationally secure, and the secret and/or threshold parameter can change to any arbitrary values multiple times. Furthermore, in our scheme, there is no secure channel and participants do not need to save any information or extra shares ahead of time. Since the running time is an important factor for practical applications, we provide a complexity comparison of our approach with respect to Chen et al.’s scheme. The comparison between the proposed scheme and that of Chen et al. indicates that the new scheme is more efficient, that it means, it has much lower computational complexity, as well as smaller storage requirements.

Advantages of cloud computing have attracted a large number of companies and encouraged the IT industry for adoption. However, for migration from traditional media to a new environment, there are requirements to adopt a well-defined strategic management model. The strategic management framework that we have proposed for cloud environment includes a strategic management model, a decision making technique, and a computation and evaluation process of the technique. This method has a comprehensive analysis for capabilities and the entities involved in a cloud environment. We present a decision making technique in cloud environment based on extended BSC (DMTC-EBSC) as a blend of modified BSC model, and SWOT tools for both cloud providers and consumers in a holistic approach for cloud computing environment. Also, the method distributes decision making process overstrengths, weaknesses, opportunities, and threats to give a full view of environmental aspects of cloud ecosystems. To validate the proposed technique, we consider a case study in which we have applied the DMTC-EBSC to a cloud environment for a small and medium-sized enterprise (SME). Evaluation results indicate superiority of the DMTCEBSC over the original BSC-SWOT technique which is an extended BSC in terms of standard KPIs.

State-of-the-art researches in unsupervised automatic keyphrase extraction focused on graph analysis. Keyphrase ranking is critical step in graph-based approaches. In this paper, we follow two main purposes including choice of good candidate phrases and computing importance of candidate phrase by considering the mutual information between words. Our documents representation improves the process of candidate phrases selection by constructing a single graph for all documents in the collection. We enjoy from parallel minimum spanning tree to prune irrelevant edge relations. We also consider second order co-occurrence of words by point-wise mutual information as a similarity measure and importance of terms to increase the performance of keyphrase ranking. We formed a single graph of cooccurrence network for all documents in the collection and analyze co-occurrence network with different settings. We compare our method with three baseline approaches of keyphrase extraction. Experimental results show that applying second order co-occurrence analysis improves keyphrases identification accuracy.

Nowadays data mining is the way of extracting hidden knowledge from raw data whereas sequence mining aims to find sequential patterns that are frequent in the database, so publishing these data may lead to the disclosure of private information about organizations or individuals. Knowledge hiding is the process of hiding sensitive knowledge extracted previously from the database, to ensure that no abuse will be caused. This paper addresses the problem of sequential pattern hiding and proposes an efficient algorithm which uses a multi-objective approach to overcome the problem of sequence hiding as well as maintaining database fidelity as much as possible. It also shows that the proposed algorithm outperforms existing methods in terms of both speed and memory usage.

This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document. (Abstract should not be longer than 150 words).