Accepted Papers

  • A System to Detect Inappropriate Messages in Online Social Networks
    Shivani Singh, Kalyani Nair, Rohan Shetty and Shantanu Nakhare,Savitribai Phule Pune University, India
    As social networking is growing at a rapid pace today it is vital that we work on improving its management. Research has shown that the content present in online social networks may have significant influence on impressionable minds. If such platforms are misused, it will lead to negative consequences. Detecting insults or inappropriate messages continues to be one of the most challenging aspects of Online Social Networks (OSNs) today. We address this problem through a Machine Learning Based Soft Text Classifier approach using Support Vector Machine algorithm. The proposed system acts as a screening mechanism the alerts the user about such messages. The messages are classified according to their subject matter and each comment is labeled for the presence of profanity and insults.
  • Facebook Implementation in Developing English Writing Skills: A Case Study of First Year Students Program in English for International Communication (EIC) at Rajamagala University of Technology Isan (RMUTI), Surin Campus
    Pisutpong Endoo,RMUTI, Surin Campus, Thailand
    The objectives of this were research to study FB implementation and attitudes in developing English writing skills of the first year students program in EICacademic year 1/2014 at RMUTI, Surin Campus. The Purposive sampling was designed for data collecting. There were 53 students studying of the first year program in EIC academic year 1/2014 at RMUTI, Surin Campus. The instruments for this research were questionnaires. The data analysis was analyzed by the Descriptive statistics to find out the value of the frequency and percentage.
  • A semantic Based Approach for Text Clustering Using an Advanced Concept-Based Mining Model
    Reshma R and Vinai George Biju,Christ University,Karnataka,India
    This paper introduces the fuzzy logic based image encryption for confidential data transfer using (2,2) secret sharing scheme. Fuzzy means vague, uncertain, ambiguous. It makes encryption and decryption method difficult to guess hence fuzzy logic using secret sharing concept is finer and enhanced way of image encryption and data is more secure. Visual Cryptography is a special encryption technique to hide information in images in such a way that it can be decrypted by the human vision. The (2,2) secret sharing scheme provides secured authentication. In secret sharing, random looking shares when brought together recreate the secret. As (2,2) divides the secret information into two shares.
    Mrs.D.MURUGESWARI,KN.SANGEETHA and M.SRIVANI,Panimalar Institute of Technology,Chennai,India
    Signalized intersection is one of the key components of road network. Its operations considerably affect the performance of the whole road system. The performance of signalized intersection measured in terms of Level of Service (LOS). Existing studies on LOS at signalized intersections are based on conventional regression model and thus the models are failed to estimate accurate LOS of signalized intersections. This paper explores the most popular fuzzy regression model and the application of fuzzy model in developing LOS model at signalized intersections. The proposed methodology derived in two steps. First step, membership function are developed and the fuzzy input values are defuzzified in crisp value by applying centroid method. Second step, fuzzy least square method is applied to develop the model. The proposed methodology is applied in two different applications in transportation engineering problem such as vehicle LOS and Pedestrian LOS model development. Finally MAPE values are compared between conventional regression model and fuzzy regression model and the results are shown that fuzzy regression models provided more precise and reliable solutions. The proposed new methodology in this paper can be used to develop LOS model transportation field.

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