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A SYSTEM TO FILTER UNWANTED MESSAGES USING
Transcript of A SYSTEM TO FILTER UNWANTED MESSAGES USING
Disadvantages of existing system
Advantages of proposed system
System Requirements --Hardwarerequirements --Software requirements
Today OSNs provide very little support to prevent unwanted messages on user walls.
For example : FACEBOOK
No content-based preferences are supported and Therefore it is not possible to prevent undesired messages.
Providing this service is not only matter of using mining techniques ,rather it requires to design ad hoc classification strategies
OSN applications are most widely used in network sites like
A SYSTEM TO FILTER UNWANTED MESSAGES
USING OSN USER WALLS
SREE RAMA ENGINEERING COLLEGE
The users ability to control the messages posted on their own private space to avoid that unwanted content is displayed
In this paper , we propose a system allowing OSN
user to have a direct control over the messages
posted on their walls.
This is achieved through a flexible rule-based system, that allows users to customize the filtering criteria to be applied to their walls.
DISADVANTAGES OF EXISTING
No content-based preferences are supported
Short texts don’t provide sufficient word occurrences
An automated system, called Filtered Wall (FW), able
to filter unwanted messages from OSN user walls.
Machine Learning (ML) text categorization techniques to
automatically assign with each short text message a set of categories based on its content.
Inherit the learning model and the elicitation procedure for
generating pre-classified data.
The learning phase creates the premise for an adequate using OSN domains, as well as facilitates the experimental evaluation tasks.
ADVANTAGES OF PROPOSED
It supports both message content and messages creator relationships and characteristics.
It concerns both the rule layer and the classification module
Miss. P. Sailaja ( M.tech)
Department of CSE & IT
Batch No:11 (CSE)
A. Sowjanya 104c1a0504
M. Prasanna 104c1a0522
G. Shaju 104c1a0513
V. Suguna 104c1a0548
Online setup assistant for FRs threshold
Operating System : Windows95/98/2000/XP
Front End : java, jdk1.6
Database : My sql server 2005
Database Connectivity : JDBC
A. Adomavicius, and G.Tuzhilin, “Towards the next generation of recommender systems : A survey if the state-of-the-art and possible extensions,” IEEE Transaction on knowledge and Data Engineering, vol. 17, no.6, pp.734-749-2005.
M.chau and H.chen ,”A machine learning approach to web page filtering using content and structure analysis,” Decision Support System, vol. 44,no.2,pp. 482-494,2008
Processor - Pentium –IV
Speed - 1.1 Ghz
RAM - 256 MB(min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows
Mouse - Two or Three Button
Monitor - SVGA