Accepted Papers


  • COMPARING THE CUCKOO ALGORITHM WITH OTHER ALGORITHMS FOR ESTIMATING TWO GLSD PARAMETERS
    Dr. Jane Jaleel Stephan, Dr. Haitham Sabah Hasan and Alaa Hamza Omran,University of Information Technology & Communications, Iraq
    ABSTRACT

    This study introduces and compares different methods for estimating the two parameters of generalized logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood estimation, and method of moments algorithms. All the required derivations and basic steps of each algorithm are explained. The applications for these algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean square error.

  • A Strategy of Dynamic Error Correction Code for Defect Tolerance Improvement on Flash Memory
    Yi-Cheng Chung and Chung-Ho Chen Department of Electrical Engineering and Institute of Computer and Communication Engineering National Cheng-Kung University,Taiwan
    ABSTRACT

    Conventional flash controller uses static error correction code (ECC) to recover limited bits of data from erroneous flash pages. The performance is not quite satisfactory because the error correction capability is limited by the static ECC field within the flash page. In order to utilize the spare area used for storing ECC field among each flash page more efficiently, this work proposes a dynamic ECC strategy that conceptually regards most of the ECC fields within a flash block as a single pool and adaptively allocates the ECC fields to each flash page in accordance with the emerging defects on the flash page. Two defect distribution models are introduced to the dynamic ECC strategy and the conventional error correcting method for comparison. The result shows the proposed method can greatly improve the defect tolerance if the defects occur densely within a region of a few data pages.

  • A Cohesion Based Friend Recommendation System
    ShamsuShehu,Al-qalam University Katsina,Nigeria
    ABSTRACT

    Social network sites have attracted millions of users with the social revolution in Web2.0. A social network is composed by communities of individuals or organizations that are connected by a common interest. Online social networking sites like Twitter, Facebook and Orkut are among the most visited sites in the Internet. In the social network sites, a user can register other users as friends and enjoy communication. However, the large amount of online users and their diverse and dynamic interests possess great challenges to support such a novel feature in online social networks. In this work, we design a general friend recommendation framework based on cohesion after analyzing the current method of friend recommendation. The main idea of the proposed method is consisted of the following stages- measuring the link strength in a network and find out possible link on this network that is yet to be established; detecting communities among the network using modularity and recommending friends. Considering the noticeable attraction of users to social networking sites, lots of research has been carried out to take advantage of the users ‘information available in these sites. Knowledge mining techniques have been developed in order to extract valuable pieces of information from the users’ activities. This paper deals with a methodology to generate a social graph of users’ actions and predict the future social activities of the users based upon the existing relationships. This graph is updated dynamically based on the changes in the selected social network. The forecasting performed is based upon some predefined rules applied on the graph elaborated.

  • A SECURITY PERIOD UPDATE METHOD USING EVALUATION FUNCTION FOR IMPROVING ENERGY EFFICIENCY OF STATISTICAL EN-ROUTE FILTERING BASED WSNs
    Jung-Sub Ahn and Tae-Ho Cho,Sungkyunkwan University,Republic of Korea
    ABSTRACT

    In recent years, WSN research is being carried out to obtain high security and energy efficiency. In WSN, sensor nodes are vulnerable to physical attacks because they are deployed in an open environment. An attacker can inject false report into networks using these vulnerabilities. F. Ye et al proposed Statistical En-route Filtering to prevent false report injection attacks. And in order to effectively use that scheme, that techniques for determining thresholds using fuzzy logic have been studied. So as to effectively apply these techniques to the network, an appropriate update period should be set according to the network environments. In this paper, we proposed a security period update method in order to improve the lifetime of the network in the Statistical En-route Filtering based on wireless sensor network of the cluster environment. Experimental results show up to 11.96 % improvement in energy efficiency is achieved when security threshold setting to the optimum period.

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