[AISWorld] FYI - Contents of IJIIT 9(1)

[Vijayan Sugumaran]

The contents of the latest issue of:

International Journal of Intelligent Information Technologies (IJIIT)

Official Publication of the Information Resources Management Association

Volume 9, Issue 1, January - March 2013

Published: Quarterly in Print and Electronically

ISSN: 1548-3657 EISSN: 1548-3665

Published by IGI Publishing, Hershey-New York, USA

www.igi-global.com/ijiit

 

Editor-in-Chief: Vijayan Sugumaran, Oakland University, USA

 

PAPER ONE

 

Identifying Influencers in Online Social Networks: The Role of Tie Strength

 

Yifeng Zhang (Department of Management Information Systems, University of
Illinois at Springfield, Springfield, Illinois, USA), Xiaoqing Li
(Department of Management Information Systems, University of Illinois at
Springfield, Springfield, Illinois, USA) and Te-Wei Wang (Department of
Management Information Systems, University of Illinois at Springfield,
Springfield, Illinois, USA)

 

Online social networks (OSNs) are quickly becoming a key component of the
Internet. With their widespread acceptance among the general public and the
tremendous amount time that users spend on them, OSNs provide great
potentials for marketing, especially viral marketing, in which marketing
messages are spread among consumers via the word-of-mouth process. A
critical task in viral marketing is influencer identification, i.e. finding
a group of consumers as the initial receivers of a marketing message. Using
agent-based modeling, this paper examines the effectiveness of tie strength
as a criterion for influencer identification on OSNs. Results show that
identifying influencers by the number of strong connections that a user has
is superior to doing so by the total number of connections when the strength
of strong connections is relatively high compared to that of weak
connections or there is a relatively high percentage of strong connections
between users. Implications of the results are discussed.

 

To obtain a copy of the entire article, click on the link below.

http://www.igi-global.com/article/identifying-influencers-online-social-netw
orks/75543

 

To read a PDF sample of this article, click on the link below.

http://www.igi-global.com/viewtitlesample.aspx?id=75543
<http://www.igi-global.com/viewtitlesample.aspx?id=75543&ptid=71340&t=identi
fying+influencers+in+online+social+networks%3a+the+role+of+tie+strength>
&ptid=71340&t=identifying+influencers+in+online+social+networks%3a+the+role+
of+tie+strength

 

PAPER TWO

 

A Novel Approach for Detecting and Classifying Breast Cancer in Mammogram
Images

 

S. Shanthi (Kongu Engineering College, Erode, Tamil Nadu, India) and V.
Murali Bhaskaran (Paavai College of Engineering, Namakkal, Tamil Nadu,
India)

 

This study uses data mining techniques for computer-aided diagnosis that
involves the feature extraction for cancer detection, so as to help doctors
towards making optimal decisions quickly and accurately. Features play an
important role in detecting the cancer in the digital mammogram and feature
extraction stage is the most vital and difficult stage. In this paper, an
enhanced feature extraction method named Multiscale Surrounding Region
Dependence Method (MSRDM) is proposed to be effective in classifying the
mammogram images into normal or benign or malignant. This proposed system is
based on a four-step procedure: Regions of Interest specification, two
dimensional discrete wavelet transformation, and multiscale surrounding
region dependence matrix computation and feature extraction. The performance
of the proposed feature set is compared with the conventional
texture-analysis methods such as gray level cooccurence matrix features and
surrounding region dependence method features. Experiments have been
conducted on both real and benchmark data and the results have been proved
to be progressive.

 

To obtain a copy of the entire article, click on the link below.

http://www.igi-global.com/article/novel-approach-detecting-classifying-breas
t/75544

 

To read a PDF sample of this article, click on the link below.

http://www.igi-global.com/viewtitlesample.aspx?id=75544
<http://www.igi-global.com/viewtitlesample.aspx?id=75544&ptid=71340&t=a+nove
l+approach+for+detecting+and+classifying+breast+cancer+in+mammogram+images>
&ptid=71340&t=a+novel+approach+for+detecting+and+classifying+breast+cancer+i
n+mammogram+images

 

PAPER THREE


Query Optimization: An Intelligent Hybrid Approach using Cuckoo and Tabu
Search

 

Mukul Joshi (Department of Computer Science and Information System, Birla
Institute of Technology & Science, Pilani Rajasthan, India) and Praveen
Ranjan Srivastava (Department of Computer Science and Information System,
Birla Institute of Technology & Science, Pilani Rajasthan, India)

 

Query optimization is an important aspect in designing database management
systems, aimed to find an optimal query execution plan so that overall time
of query execution is minimized. Multi join query ordering (MJQO) is an
integral part of query optimizer. This paper aims to propose a solution for
MJQO problem, which is an NP complete problem. This paper proposes a
heuristic based algorithm as a solution of MJQO problem. The proposed
algorithm is a combination of two basic search algorithms, cuckoo and tabu
search. Simulation shows some exciting results in favour of the proposed
algorithm and concludes that proposed algorithm can solve MJQO problem in
less amount of time than the existing methods.

 

To obtain a copy of the entire article, click on the link below.

http://www.igi-global.com/article/query-optimization-intelligent-hybrid-appr
oach/75545

 

To read a PDF sample of this article, click on the link below.

http://www.igi-global.com/viewtitlesample.aspx?id=75545
<http://www.igi-global.com/viewtitlesample.aspx?id=75545&ptid=71340&t=query+
optimization%3a+an+intelligent+hybrid+approach+using+cuckoo+and+tabu+search>
&ptid=71340&t=query+optimization%3a+an+intelligent+hybrid+approach+using+cuc
koo+and+tabu+search

 

 

 

PAPER FOUR

 

Detecting Distributed Predicates Using Genetic Algorithms

 

Eslam Al Maghayreh (Computer Sciences Department, Yarmouk University, Irbid,
Jordan), Iyad Abu Doush (Computer Sciences Department, Yarmouk University,
Irbid, Jordan) and Faisal Alkhateeb (Computer Sciences Department, Yarmouk
University, Irbid, Jordan)

 

Detection of distributed predicates is one of the techniques that have been
used in the literature to improve the dependability of distributed programs.
This technique (sometimes referred to as runtime verification) is used to
verify that a given run of a distributed program satisfies certain
properties (specified as predicates). In general, the detection of a
distributed predicate can incur significant overhead due to the existence of
multiple processes running concurrently. Several techniques have been
introduced in the literature to efficiently detect distributed predicates.
However, most of these techniques work efficiently for certain classes of
predicates, like conjunctive predicates. In this paper, the authors have
presented a technique based on genetic algorithms to efficiently detect
distributed predicates under the possibly modality. The authors have used
JGAP (Java Genetic Algorithms Package) to implement the algorithm and
conducted several experiments to demonstrate its effectiveness.

 

To obtain a copy of the entire article, click on the link below.

http://www.igi-global.com/article/detecting-distributed-predicates-using-gen
etic/75546

 

To read a PDF sample of this article, click on the link below.

http://www.igi-global.com/viewtitlesample.aspx?id=75546
<http://www.igi-global.com/viewtitlesample.aspx?id=75546&ptid=71340&t=detect
ing+distributed+predicates+using+genetic+algorithms>
&ptid=71340&t=detecting+distributed+predicates+using+genetic+algorithms

 

*****************************************************

For full copies of the above articles, check for this issue of the
International Journal of Intelligent Information Technologies (IJIIT) in
your institution's library. This journal is also included in the IGI Global
aggregated "InfoSci-Journals" database:
http://www.igi-global.com/eresources/infosci-journals.aspx.

*****************************************************

 

CALL FOR PAPERS

 

Mission of IJIIT:

 

The advent of the World Wide Web has sparked renewed interest in the area of
intelligent information technologies. There is a growing interest in
developing intelligent technologies that enable users to accomplish complex
tasks in web-centric environments with relative ease, utilizing such
technologies as intelligent agents, distributed computing in heterogeneous
environments, and computer supported collaborative work. The mission of the
International Journal of Intelligent Information Technologies (IJIIT) is to
bring together researchers in related fields such as information systems,
distributed AI, intelligent agents, and collaborative work, to explore and
discuss various aspects of design and development of intelligent
technologies. This journal provides a forum for academics and practitioners
to explore research issues related to not only the design, implementation
and deployment of intelligent systems and technologies, but also economic
issues and organizational impact. Papers related to all aspects of
intelligent systems including theoretical work on agent and multi-agent
systems as well as case studies offering insights into agent-based problem
solving with empirical or simulation based evidence are welcome.

 

Coverage of IJIIT:

 

The International Journal of Intelligent Information Technologies (IJIIT)
encourages quality research dealing with (but not limited to) the following
topics:

 

.         Agent-based auction, contracting, negotiation, and ecommerce

.         Agent-based control and supply chain

.         Agent-based simulation and application integration

.         Cooperative and collaborative systems

.         Distributed intelligent systems and technologies

.         Human-agent interaction and experimental evaluation

.         Implementation, deployment, diffusion, and organizational impact

.         Integrating business intelligence from internal and external
sources

.         Intelligent agent and multi-agent systems in various domains

.         Intelligent decision support systems

.         Intelligent information retrieval and business intelligence

.         Intelligent information systems development using design science
principles

.         Intelligent Web mining and knowledge discovery systems

.         Manufacturing information systems

.         Models, architectures and behavior models for agent-oriented
information systems

.         Multimedia information processing

.         Privacy, security, and trust issues

.         Reasoning, learning, and adaptive systems

.         Semantic Web, Web services, and ontologies 

 

Interested authors should consult the journal's manuscript submission
guidelines www.igi-global.com/ijiit.

 

All inquiries and submissions should be sent to:

Editor-in-Chief: Dr. Vijayan Sugumaran at sugumara at oakland.edu
<mailto:%20sugumara at oakland.edu> 

 

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