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SEABIRDS
IEEE 2012 - 2013
SOFTWARE PROJECTS IN
VARIOUS DOMAINS
| JAVA | J2ME | J2EE |
DOTNET |MATLAB |NS2 |
SBGC
SBGC
24/83, O Block, MMDA COLONY
4th FLOOR SURYA COMPLEX,
ARUMBAKKAM
SINGARATHOPE BUS STOP,
CHENNAI-600106
OLD MADURAI ROAD, TRICHY- 620002

Web: www.ieeeproject.in

E-Mail: ieeeproject@hotmail.com

Trichy
Chennai
Mobile:- 09003012150
Mobile:- 09944361169
Phone:- 0431-4012303


SBGC Provides IEEE 2012-2013 projects for all Final Year Students. We do assist the students
with Technical Guidance for two categories.
Category 1 : Students with new project ideas / New or Old
IEEE Papers.
Category 2 : Students selecting from our project list.
When you register for a project we ensure that the project is implemented to your fullest
satisfaction and you have a thorough understanding of every aspect of the project.
SBGC PROVIDES YOU THE LATEST IEEE 2012 PROJECTS / IEEE 2013 PROJECTS
FOR FOLLOWING DEPARTMENT STUDENTS
B.E, B.TECH, M.TECH, M.E, DIPLOMA, MS, BSC, MSC, BCA, MCA, MBA, BBA, PHD,
B.E (ECE, EEE, E&I, ICE, MECH, PROD, CSE, IT, THERMAL, AUTOMOBILE,
MECATRONICS, ROBOTICS) B.TECH(ECE, MECATRONICS, E&I, EEE, MECH , CSE, IT,
ROBOTICS) M.TECH(EMBEDDED SYSTEMS, COMMUNICATION SYSTEMS, POWER
ELECTRONICS, COMPUTER SCIENCE, SOFTWARE ENGINEERING, APPLIED
ELECTRONICS, VLSI Design) M.E(EMBEDDED SYSTEMS, COMMUNICATION
SYSTEMS,

POWER

ELECTRONICS,
COMPUTER
SCIENCE,
SOFTWARE
ENGINEERING, APPLIED ELECTRONICS, VLSI Design) DIPLOMA (CE, EEE, E&I, ICE,
MECH,PROD, CSE, IT)
MBA(HR, FINANCE, MANAGEMENT, HOTEL MANAGEMENT, SYSTEM
MANAGEMENT, PROJECT MANAGEMENT, HOSPITAL MANAGEMENT, SCHOOL
MANAGEMENT, MARKETING MANAGEMENT, SAFETY MANAGEMENT)
We also have training and project, R & D division to serve the students and make them job
oriented professionals




PROJECT SUPPORTS AND DELIVERABLES
Project Abstract
IEEE PAPER
IEEE Reference Papers, Materials &
Books in CD
PPT / Review Material
Project Report (All Diagrams & Screen
shots)
Working Procedures
Algorithm Explanations
Project Installation in Laptops
Project Certificate


TECHNOLOGY
: JAVA
DOMAIN

: IEEE TRANSACTIONS ON DATA MINING
S.No
IEEE TITLE
ABSTRACT
IEEE
.
YEAR
1. A Framework
Due to a wide range of potential applications, research 2012
for Personal
on mobile commerce has received a lot of interests from
Mobile
both of the industry and academia. Among them, one of
Commerce
the active topic areas is the mining and prediction of
Pattern Mining
users' mobile commerce behaviors such as their
and Prediction
movements and purchase transactions. In this paper, we
propose a novel framework, called Mobile Commerce
Explorer (MCE), for mining and prediction of mobile
users' movements and purchase transactions under the
context of mobile commerce. The MCE framework
consists of three major components: 1) Similarity
Inference Model SIM for measuring
the similarities among stores and items, which are two
basic mobile commerce entities considered in this paper;
2) Personal Mobile Commerce Pattern Mine (PMCP-
Mine) algorithm for efficient discovery of mobile users'
Personal Mobile Commerce Patterns (PMCPs); and 3)
Mobile Commerce Behavior Predictor MCBP for
prediction of possible mobile user behaviors. To our
best knowledge, this is the first work that facilitates
mining and prediction of mobile users' commerce
behaviors in
order to recommend stores and items previously
unknown to a user. We perform an extensive
experimental evaluation by simulation and show that our
proposals produce excellent results.
2. Efficient
Extended Boolean retrieval (EBR) models were 2012
Extended
proposed nearly three decades ago, but have had little
Boolean
practical impact, despite their significant advantages
Retrieval
compared to either ranked keyword or pure Boolean
retrieval. In particular, EBR models produce meaningful
rankings; their query model allows the representation of
complex concepts in an and-or format; and they are
scrutable, in that the score assigned to a document
depends solely on the content of that document,
unaffected by any collection statistics or other external
factors. These characteristics make EBR models
attractive in domains typified by medical and legal
searching, where the emphasis is on iterative
development of reproducible complex queries of dozens
or even hundreds of terms. However, EBR is much more


computationally expensive than the alternatives. We
consider the implementation of the p-norm approach to
EBR, and demonstrate that ideas used in the max-score
and wand exact optimization techniques for ranked
keyword retrieval can be adapted to allow selective
bypass of documents via a low-cost screening process
for this and similar retrieval models. We also propose
term independent bounds that are able to further reduce
the number of score calculations for short, simple
queries under the extended Boolean retrieval model.
Together, these methods yield an overall saving from 50
to 80 percent of the evaluation cost on test queries
drawn from biomedical search.
3. Improving
Recommender systems are becoming increasingly 2012
Aggregate
important to individual users and businesses for
Recommendati
providing personalized
on Diversity
recommendations. However, while the majority of
Using Ranking-
algorithms proposed in recommender systems literature
Based
have focused on
Techniques
improving recommendation accuracy (as exemplified by
the recent Netflix Prize competition), other important
aspects of
recommendation quality, such as the diversity of
recommendations, have often been overlooked. In this
paper, we introduce and explore a number of item
ranking techniques that can generate substantially more
diverse recommendations across all users while
maintaining comparable levels of recommendation
accuracy.
Comprehensive
empirical
evaluation
consistently shows
the diversity gains of the proposed techniques using
several real-world rating data sets and different rating
prediction
algorithms.
4. Effective
Many data mining techniques have been proposed for 2012
Pattern
mining useful patterns in text documents. However, how
Discovery for
to effectively use and update discovered patterns is still
Text Mining
an open research issue, especially in the domain of text
mining. Since most existing text mining methods
adopted term-based approaches, they all suffer from the
problems of polysemy and synonymy. Over the years,
people have often held the hypothesis that pattern (or
phrase)-based approaches should perform better than the
term-based ones, but many experiments do not support
this hypothesis. This paper presents an innovative and
effective pattern discovery technique which includes the


processes of pattern deploying and pattern evolving, to
improve the effectiveness of using and updating
discovered patterns for finding relevant and interesting
information. Substantial experiments on RCV1 data
collection and TREC topics demonstrate that the
proposed solution achieves encouraging performance.
5. Incremental
Information
extraction
systems
are
traditionally 2012
Information
implemented as a pipeline of special-purpose processing
Extraction
modules targeting
Using
the extraction of a particular kind of information. A
Relational
major drawback of such an approach is that whenever a
Databases
new extraction goal emerges or a module is improved,
extraction has to be reapplied from scratch to the entire
text corpus even though only a small part of the corpus
might be affected. In this paper, we describe a novel
approach for information extraction in which extraction
needs are expressed in the form of database queries,
which are evaluated and optimized by database systems.
Using database queries for information extraction
enables generic extraction and minimizes reprocessing
of data by performing incremental extraction to identify
which part of the data is affected by the change of
components or goals. Furthermore, our approach
provides automated query generation components so
that casual users do not have to learn the query language
in order to perform extraction. To demonstrate the
feasibility of our incremental extraction approach, we
performed experiments to highlight two important
aspects of an information extraction system: efficiency
and quality of extraction results. Our experiments show
that in the event of deployment of a new module, our
incremental extraction approach reduces the processing
time by 89.64 percent as compared to a traditional
pipeline approach. By applying our methods to a corpus
of 17 million biomedical abstracts, our experiments
show that the query performance is efficient for real-
time applications. Our experiments also revealed that
our approach achieves high quality extraction results.
6. A Framework
XML has become the universal data format for a wide 2012
for Learning
variety of information systems. The large number of
Comprehensibl
XML documents existing on the web and in other
e Theories in
information storage systems makes classification an
XML
important task. As a typical type of semi structured data,
Document
XML documents have both structures and contents.
Classification
Traditional text learning techniques are not very suitable
for XML document classification as structures are not


considered. This paper presents a novel complete
framework for XML document classification. We first
present a knowledge representation method for XML
documents which is based on a typed higher order logic
formalism. With this representation method, an XML
document is represented as a higher order logic term
where both its contents and structures are captured. We
then present a decision-tree learning algorithm driven by
precision/recall breakeven point (PRDT) for the XML
classification
problem
which
can
produce
comprehensible
theories. Finally, a semi-supervised learning algorithm is
given which is based on the PRDT algorithm and the
cotraining framework. Experimental results demonstrate
that our framework is able to achieve good performance
in both supervised and semi-supervised learning with
the bonus of producing comprehensible learning
theories.
7. A Link-Based
Although attempts have been made to solve the problem 2012
Cluster
of clustering categorical data via cluster ensembles, with
Ensemble
the results being competitive to conventional algorithms,
Approach for
it is observed that these techniques unfortunately
Categorical
generate a final data partition based on incomplete
Data Clustering information. The underlying ensemble-information
matrix presents only cluster-data point relations, with
many entries being left unknown. The paper presents an
analysis that suggests this problem degrades the quality
of the clustering result, and it presents a new link-based
approach, which improves the conventional matrix by
discovering unknown entries through similarity between
clusters in an ensemble. In particular, an efficient link-
based algorithm is proposed for the underlying
similarity assessment. Afterward, to obtain the final
clustering result, a graph partitioning technique is
applied to a weighted bipartite graph that is formulated
from the refined matrix. Experimental results on
multiple real data sets suggest that the proposed link-
based method almost always outperforms both
conventional clustering algorithms for categorical data
and well-known cluster ensemble techniques.
8. Evaluating Path The recent advances in the infrastructure of Geographic 2012
Queries over
Information Systems (GIS), and the proliferation of GPS
Frequently
technology, have resulted in the abundance of geodata in
Updated Route
the form of sequences of points of interest (POIs),
Collections
waypoints, etc. We refer to sets of such sequences as
route collections. In this work, we consider path queries


on frequently updated route
collections: given a route collection and two points ns
and nt, a path query returns a path, i.e., a sequence of
points, that connects ns to nt. We introduce two path
query evaluation paradigms that enjoy the benefits of
search algorithms (i.e., fast index maintenance) while
utilizing transitivity information to terminate the search
sooner. Efficient indexing
schemes and appropriate updating procedures are
introduced. An extensive experimental evaluation
verifies the advantages
of our methods compared to conventional graph-based
search.
9. Optimizing
Peer-to-Peer
multi
keyword
searching
requires 2012
Bloom Filter
distributed intersection/union operations across wide
Settings in
area networks,
Peer-to-Peer
raising a large amount of traffic cost. Existing schemes
Multi keyword
commonly utilize Bloom Filters (BFs) encoding to
Searching
effectively
reduce the traffic cost during the intersection/union
operations. In this paper, we address the problem of
optimizing the settings of a BF. We show, through
mathematical proof, that the optimal setting of BF in
terms of traffic cost is determined by the statistical
information of the involved inverted lists, not the
minimized false positive rate as claimed by previous
studies. Through numerical analysis, we demonstrate
how to obtain optimal settings. To better evaluate the
performance of this design, we conduct comprehensive
simulations on TREC WT10G test collection and query
logs of a major commercial web search engine. Results
show that our design significantly reduces the search
traffic and latency of the existing approaches.
10. Privacy
Privacy preservation is important for machine learning 2012
Preserving
and data mining, but measures designed to protect
Decision Tree
private information often result in a trade-off: reduced
Learning Using utility of the training samples. This paper introduces a
Unrealized
privacy preserving approach that can be applied to
Data Sets
decision tree learning, without concomitant loss of
accuracy. It describes an approach to the preservation of
the privacy of collected data samples in cases where
information from the sample database has been partially
lost. This approach converts the original sample data
sets into a group of unreal data sets, from which the
original samples cannot be reconstructed without the
entire group of unreal data sets. Meanwhile, an accurate


decision tree can be built directly from those unreal data
sets. This novel approach can be applied directly to the
data storage as soon as the first sample is collected. The
approach is compatible with other privacy preserving
approaches, such as cryptography, for extra protection.

TECHNOLOGY
: DOTNET
DOMAIN

: IEEE TRANSACTIONS ON DATA MINING

S.No. IEEE TITLE ABSTRACT
IEEE
YEAR

1. A
Databases enable users to precisely express their 2012
Probabilistic
informational needs using structured queries. However,
Scheme
for database query construction is a laborious and error-
Keyword-
prone process, which cannot be performed well by most
Based
end users. Keyword search alleviates the usability
Incremental
problem at the price of query expressiveness. As
Query
keyword search algorithms do not differentiate between
Construction
the possible informational needs represented by a
keyword query, users may not receive adequate results.
This paper presents IQP--a novel approach to bridge the
gap between usability of keyword search and
expressiveness of database queries. IQP enables a user to
start with an arbitrary keyword query and incrementally
refine it into a structured query through an interactive
interface. The enabling techniques of IQP include: 1) a
probabilistic
framework
for
incremental
query
construction; 2) a probabilistic model to assess the
possible informational needs represented by a keyword
query; 3) an algorithm to obtain the optimal query
construction process. This paper presents the detailed
design of IQP, and demonstrates its effectiveness and
scalability through experiments over real-world data and
a user study.
2. Anomaly
This survey attempts to provide a comprehensive and 2012
Detection for structured overview of the existing research for the
Discrete
problem of detecting anomalies in discrete/symbolic
Sequences: A sequences. The objective is to provide a global
Survey
understanding of the sequence anomaly detection
problem and how existing techniques relate to each other.
The key contribution of this survey is the classification of
the existing research into three distinct categories, based
on the problem formulation that they are trying to solve.


These problem formulations are: 1) identifying
anomalous sequences with respect to a database of
normal sequences; 2) identifying an anomalous
subsequence within a long sequence; and 3) identifying a
pattern in a sequence whose frequency of occurrence is
anomalous. We show how each of these problem
formulations is characteristically distinct from each other
and discuss their relevance in various application
domains. We review techniques from many disparate and
disconnected application domains that address each of
these formulations. Within each problem formulation, we
group techniques into categories based on the nature of
the underlying algorithm. For each category, we provide
a basic anomaly detection technique, and show how the
existing techniques are variants of the basic technique.
This approach shows how different techniques within a
category are related or different from each other. Our
categorization reveals new variants and combinations
that have not been investigated before for anomaly
detection. We also provide a discussion of relative
strengths and weaknesses of different techniques. We
show how techniques developed for one problem
formulation can be adapted to solve a different
formulation, thereby providing several novel adaptations
to solve the different problem formulations. We also
highlight the applicability of the techniques that handle
discrete sequences to other related areas such as online
anomaly detection and time series anomaly detection.
3. Combining
Web databases generate query result pages based on a 2012
Tag
and user's query. Automatically extracting the data from
Value
these query result pages is very important for many
Similarity for applications, such as data integration, which need to
Data
cooperate with multiple web databases. We present a
Extraction
novel data extraction and alignment method called CTVS
and
that combines both tag and value similarity. CTVS
Alignment
automatically extracts data from query result pages by
first identifying and segmenting the query result records
(QRRs) in the query result pages and then aligning the
segmented QRRs into a table, in which the data values
from the same attribute are put into the same column.
Specifically, we propose new techniques to handle the
case when the QRRs are not contiguous, which may be
due to the presence of auxiliary information, such as a
comment, recommendation or advertisement, and for
handling any nested structure that may exist in the QRRs.
We also design a new record alignment algorithm that

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