Browsing by Author "K, SathiyaKumari"

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AN ANALYSIS OF LINK STRENGTH IN SOCIAL NETWORKS
(International Journal of Engineering Research & Technology, 2012-11) R, Hema Latha; K, SathiyaKumari
A social structure made of nodes that are generally individuals or organizations. A social network represents relationships and flows between people, groups, organizations, animals, computers or other information or knowledge processing entities. Social networking websites allow users to be part of a virtual community. Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Link prediction in Facebook and Twitter can be done at a familiar class of graph generation model, where the nodes are united with locations in a latent metric space and connections are most likely between closer nodes. In this paper, GEPHI and NODEXL tools are used for the comparison measures to predict betweenness centrality of particular users account in Facebook and Twitter.
PREDICTION OF THE COMPRESSIVE STRENGTH OF HIGH PERFORMANCE CONCRETE MIX USING TREE BASED MODELING
(International Journal of Computer Applications, 2010) C, Deepa; V, Pream Sudha; K, SathiyaKumari
Concrete is the safest and sustainable construction material which is most widely used in the world as it provides superior fire resistance, gains strength over time and gives an extremely long service life. Its annual consumption is estimated between 21 and 31 billion tones. Designing a concrete mix involves the process of selecting suitable ingredients of concrete and determining their relative amounts with the objective of producing a concrete of the required, strength, durability, and workability as economically as possible. According to the National Council for Cement and Building Materials (NCBM), New Delhi, the compressive strength of concrete is governed generally, by the water-cement ratio. The mineral admixtures like fly ash, ground granulated blast furnace, silica fume and fine aggregates also influence it. The main purpose of this paper is to predict the compressive strength of the high performance concrete by using classification algorithms like Multilayer Perceptron, M5P Tree models and Linear Regression. The result from this study suggests that tree based models perform remarkably well in predicting the compressive strength of the concrete mix.
SURVEY ON LINK PREDICTION IN FACEBOOK AND TWITTER
(International Journal of Engineering Research and Applications, 2012-09) R, Hema Latha; K, SathiyaKumari
Social media have gained increased usage rapidly for a discrepancy of reasons. Participants were asked to view one of six mock Twitter.com pages that varied both the number of followers and the ratio between followers and follows on the page and report their perceived source worthy of trust. This research examines mediocre factors and speculative relevant contextual variables that affect sensational formation and the willingness to initiate virtual friendship. In this paper [ Network Analysis of Recurring YouTube Spam Campaigns] demonstrated tracking of spam campaigns over time using network motif profiling. Heterogeneous social network services, such as Facebook and Twitter, have emerged as popular, and often effective channels for Web users to capture updates from their friends. In this paper [Learning to blend vitality rankings from heterogeneous social networks], demonstrated the problem of blending vitality denotes all kinds of updates user receives in various social networks. It proposed variety of content, users, and users correlation features for this task. Since vitalities from different social networks are likely to have different sets of features, employed divide-and-conquer strategy in order to fully exploit all available features for vitalities from each social network respectively.