Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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Growing interest and continuous development of social network sites like Facebook, Twitter, Flicker, and YouTube etc.turn to several researchers for research study, planning and rigorous development. Exact people behavior prediction is the most important challenge of these online social networking websites. This research focus to learn to predict collective behavior in social media networks. Particularly provided information about some person, how can we collect the behavior of unobserved persons in the same network? These tremendous growing networks in social media are of massive size, involving large number of actors. The computational scale of these networks makes necessary scalable learning for models for collective collaborative behavior prediction. This scalability issue is solved by the proposed k-means clustering algorithm which is used to partition the edges into disjoint distinct sets, with each set is showing one separate affiliation. This edge-centric structure represents that the extracted social dimensions are definitely sparse in nature. This model idealized on the sparse natured social dimensions, shows efficient prediction performance than earlier existing approaches The proposed approach can effectively able to work for sparse social networks of any growing size.
Umesh B.Shingote. 2014. \u201cExtended Edgecluster based Technique for Social Networking Collective Behavior Learning System\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C6): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
Total Score: 107
Country: India
Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering
Authors: Umesh B.Shingote, Dr. Setu Kumar Chaturvedi (PhD/Dr. count: 1)
View Count (all-time): 248
Total Views (Real + Logic): 8824
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Publish Date: 2014 09, Sat
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Growing interest and continuous development of social network sites like Facebook, Twitter, Flicker, and YouTube etc.turn to several researchers for research study, planning and rigorous development. Exact people behavior prediction is the most important challenge of these online social networking websites. This research focus to learn to predict collective behavior in social media networks. Particularly provided information about some person, how can we collect the behavior of unobserved persons in the same network? These tremendous growing networks in social media are of massive size, involving large number of actors. The computational scale of these networks makes necessary scalable learning for models for collective collaborative behavior prediction. This scalability issue is solved by the proposed k-means clustering algorithm which is used to partition the edges into disjoint distinct sets, with each set is showing one separate affiliation. This edge-centric structure represents that the extracted social dimensions are definitely sparse in nature. This model idealized on the sparse natured social dimensions, shows efficient prediction performance than earlier existing approaches The proposed approach can effectively able to work for sparse social networks of any growing size.
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