[AISWorld] Contents of IJIIT 13(2) - International Journal of Intelligent Information Technologies (IJIIT)

[Vijayan Sugumaran]

The contents of the latest issue of:
International Journal of Intelligent Information Technologies (IJIIT)
Volume 13, Issue 2, April - June 2017
Indexed by: Compendex (Elsevier Engineering Index), INSPEC, SCOPUS, Web of
Science (All Journals)
Published: Quarterly in Print and Electronically
ISSN: 1548-3657; EISSN: 1548-3665; 
Published by IGI Global Publishing, Hershey, USA
www.igi-global.com/ijiit
<http://www.igi-global.com/journal/international-journal-intelligent-informa
tion-technologies/1089> 

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

Note: The International Journal of Intelligent Information Technologies
(IJIIT) has an Open Access option, which allows individuals and institutions
unrestricted access to its published content. Unlike traditional
subscription-based publishing models, open access content is available
without having to purchase or subscribe to the journal in which the content
is published. All IGI Global manuscripts are accepted based on a
double-blind peer review editorial process.

ARTICLE 1

Privacy Preserving Fuzzy Association Rule Mining in Data Clusters Using
Particle Swarm Optimization

Sathiyapriya Krishnamoorthy (PSG College of Technology, Department of
Computer Science & Engineering, Tamil Nadu, India), G. Sudha Sadasivam (PSG
College of Technology, Department of Computer Science & Engineering, Tamil
Nadu, India), M. Rajalakshmi (Coimbatore Institute of Technology, Department
of Computer Science & Engineering, Tamil Nadu, India), K. Kowsalyaa (PSG
College of Technology, Department of Computer Science & Engineering, Tamil
Nadu, India), M. Dhivya (SSN College of Engineering, Department of Computer
Science & Engineering, Tamil Nadu, India)

An association rule is classified as sensitive if its thread of revelation
is above certain confidence value. If these sensitive rules were revealed to
the public, it is possible to deduce sensitive knowledge from the published
data and offers benefit for the business competitors. Earlier studies in
privacy preserving association rule mining focus on binary data and has more
side effects. But in practical applications the transactions contain the
purchased quantities of the items. Hence preserving privacy of quantitative
data is essential. The main goal of the proposed system is to hide a group
of interesting patterns which contains sensitive knowledge such that
modifications have minimum side effects like lost rules, ghost rules, and
number of modifications. The proposed system applies Particle Swarm
Optimization to a few clusters of particles thus reducing the number of
modification. Experimental results demonstrate that the proposed approach is
efficient in terms of lost rules, number of modifications, hiding failure
with complete avoidance of ghost rules.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/privacy-preserving-fuzzy-association-rule-mining-
in-data-clusters-using-particle-swarm-optimization/179297
<http://www.igi-global.com/article/privacy-preserving-fuzzy-association-rule
-mining-in-data-clusters-using-particle-swarm-optimization/179297> 

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=179297
<http://www.igi-global.com/viewtitlesample.aspx?id=179297> 

ARTICLE 2

Language Relationship Model for Automatic Generation of Tamil Stories from
Hints

Rajeswari Sridhar (Anna University, Department of Computer Science and
Engineering, Tamil Nadu, India), V. Janani (Anna University, Tamil Nadu,
India), Rasiga Gowrisankar (Anna University, College of Engineering, Guindy,
Tamil Nadu, India), G. Monica (Anna University, Tamil Nadu, India)

In this paper, we propose to develop a Story Generator from hints using a
machine learning approach. During the learning phase, the system is fed with
stories which are POS tagged and are converted into a Language Relationship
model that is represented as a conceptual graph. During the synthesis phase,
the input hints which are delimited using hyphen and converted to a
conceptual graph. This graph is matched with the conceptual graph of the
corpus and probable words, its sequences along with the relationship are
determined using three proposed methods namely Randomized selection,
Weighted Selection using Bigram Probability of hint phrases and Weighted
Selection using product of Bigram Probability of Conceptual Graph and Bigram
Probability of hint phrases. Using the words, sequences and relationships, a
sentence assembler algorithm is designed to position the words to form a
sentence. To make the story complete and readable, suffixes are added using
Tamil grammar to the assembled words and a story is generated which is
syntactically and semantically correct.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/language-relationship-model-for-automatic-generat
ion-of-tamil-stories-from-hints/179298
<http://www.igi-global.com/article/language-relationship-model-for-automatic
-generation-of-tamil-stories-from-hints/179298> 

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=179298
<http://www.igi-global.com/viewtitlesample.aspx?id=179298> 

ARTICLE 3

A Framework for Applying CSFs to ERP Software Selection: An Extension of
Fuzzy TOPSIS Approach

Rekha Gupta (Jamia Millia Islamia, FTK-Center for Information Technology,
New Delhi, India), S. Kazim Naqvi (Jamia Millia Islamia, FTK-Center for
Information Technology, New Delhi, India)

The literature review studies on ERP system indicate two main areas of
thrust: the study of critical success factors for the risk aversion in ERP
implementation projects and the ERP system selection studies. However, ERP
system selection is a tedious and a time-consuming activity. The critical
success factors (CSFs) serve as vital input ingredients to the ERP selection
Models. It is however notable that, that none of the CSFs listing propagated
by the researchers find a straightforward application in the selection
procedure. The paper bridges the gap between the two thrust areas by
proposing a framework for applying the prioritized CSFs listed for direct
utilization in the selection process. An exhaustive review on the ERP
selection techniques reveals the focus on AHP and/or the Fuzzy Logic
approaches to ERP selection problem. A new approach to ERP selection problem
with the extensions of the Fuzzy-TOPSIS is subsequently introduced and is
illustrated by a solved numerical example. Also, the computational
simplicity of the extensions of Fuzzy TOPSIS is demonstrated.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/a-framework-for-applying-csfs-to-erp-software-sel
ection/179299
<http://www.igi-global.com/article/a-framework-for-applying-csfs-to-erp-soft
ware-selection/179299> 

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=179299
<http://www.igi-global.com/viewtitlesample.aspx?id=179299> 

ARTICLE 4

Comparative Study of CAMSHIFT and RANSAC Methods for Face and Eye Tracking
in Real-Time Video

T. Raghuveera (Anna University, Department of Computer Science and
Engineering, Tamil Nadu, India), S. Vidhushini (Anna University, Department
of Computer Science and Engineering, Tamil Nadu, India), M. Swathi (Anna
University, Department of Computer Science and Engineering, Tamil Nadu,
India)

Real-Time Facial and eye tracking is critical in applications like military
surveillance, pervasive computing, Human Computer Interaction etc. In this
work, face and eye tracking are implemented by using two well-known methods,
CAMSHIFT and RANSAC. In our first approach, a frontal face detector is run
on each frame of the video and the Viola-Jones face detector is used to
detect the faces. CAMSHIFT Algorithm is used in the real- time tracking
along with Haar-Like features that are used to localize and track eyes. In
our second approach, the face is detected using Viola-Jones, whereas RANSAC
is used to match the content of the subsequent frames. Adaptive Bilinear
Filter is used to enhance quality of the input video. Then, we run the
Viola-Jones face detector on each frame and apply both the algorithms.
Finally, we use Kalman filter upon CAMSHIFT and RANSAC and compare with the
preceding experiments. The comparisons are made for different real-time
videos under heterogeneous environments through proposed performance
measures, to identify the best-suited method for a given scenario.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/comparative-study-of-camshift-and-ransac-methods-
for-face-and-eye-tracking-in-real-time-video/179300
<http://www.igi-global.com/article/comparative-study-of-camshift-and-ransac-
methods-for-face-and-eye-tracking-in-real-time-video/179300> 

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=179300
<http://www.igi-global.com/viewtitlesample.aspx?id=179300> 

  _____  

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: www.igi-global.com/isj
<http://www.igi-global.com/e-resources/infosci-databases/infosci-journals/>
.

  _____  

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.

Indices of IJIIT:

*	ACM Digital Library
*	Australian Business Deans Council (ABDC)
*	Bacon's Media Directory
*	Burrelle's Media Directory
*	Cabell's Directories
*	Compendex (Elsevier Engineering Index)
*	CSA Illumina
*	DBLP
*	DEST Register of Refereed Journals
*	Gale Directory of Publications & Broadcast Media
*	GetCited
*	Google Scholar
*	INSPEC
*	JournalTOCs
*	Library & Information Science Abstracts (LISA)
*	MediaFinder
*	Norwegian Social Science Data Services (NSD)
*	SCOPUS
*	The Index of Information Systems Journals
*	The Standard Periodical Directory
*	Ulrich's Periodicals Directory
*	Web of Science (All Journals)
*	Web of Science Emerging Sources Citation Index (ESCI)

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/calls-for-papers/international-journal-intelligent-inform
ation-technologies/1089
<http://www.igi-global.com/calls-for-papers/international-journal-intelligen
t-information-technologies/1089> 

===========================================

Vijayan Sugumaran, Ph.D.

Professor of Management Information Systems

Chair, Department of Decision and Information Sciences

School of Business Administration

Oakland University

Rochester, MI 48309

Phone: 248-370-4649

Fax: 248-370-4275

Email:  <mailto:sugumara at oakland.edu> sugumara at oakland.edu

===========================================