2014 Dotnet IEEE projects:"Redundant Wall Scanning"

"In this Application, system is to scan undesired messages from Redundant
walls. The system exploits a ML soft classifier to enforce customizable contentdependent
FRs.Moreover, the flexibility of the system in terms of scanning
options is enhanced through the management of BLs.This work is the first step
of a wider project. The early encouraging results we have obtained on the classification
procedure prompt us to continue with other work that will aim to
improve the quality of classification. In particular,future plans contemplate a
deeper investigation on two interdependent tasks. The first concerns the extraction
and/ or selection of contextual features that have been shown to have a high
discriminative power. The second task involves the learning phase. Since the
underlying domain is dynamically changing, the collection of preclassified data
may not be representative in the longer term. The present batch learning strategy,
based on the preliminary collection of the entire set of labeled data from experts,
allowed an accurate experimental evaluation but needs to be evolved to include
new operational requirements. In future work, we plan to address this problem
by investigating the use of online learning paradigms able to include label feedbacks
from users. Additionally, we plan to enhance our system with a more
sophisticated approach to decide when a user should be inserted into a BL."

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