[AISWorld] Contents of 10(4) - International Journal of Intelligent Information Technologies

Vijayan Sugumaran sugumara at oakland.edu
Wed Feb 25 14:20:22 EST 2015

The contents of the latest issue of:
International Journal of Intelligent Information Technologies (IJIIT)
Volume 10, Issue 4, October - December 2014
Published: Quarterly in Print and Electronically
ISSN: 1548-3657; EISSN: 1548-3665; 
Published by IGI Global Publishing, Hershey, USA

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

Note: There are no submission or acceptance fees for manuscripts submitted
to the International Journal of Intelligent Information Technologies
(IJIIT). All manuscripts are accepted based on a double-blind peer review
editorial process.


Relative Superiority of Key Centrality Measures for Identifying Influencers
on Social Media

Yifeng Zhang (Department of Management Information Systems, University of
Illinois at Springfield, Springfield, IL, USA), Xiaoqing Li (Department of
Management Information Systems, University of Illinois at Springfield,
Springfield, IL, USA)

Marketers have been increasingly turning to social media for marketing
campaigns, including viral marketing. A key step in viral marketing is to
identify influencers in order to maximize the reach of a marketing message.
Existing research shows that centrality measures, such as degree and
betweenness, are effective methods for influencer identification. However,
viral marketing models used in different studies vary greatly, making it
difficult to compare findings across the studies. In this paper, the authors
built an agent-based framework of viral marketing that supports different
experiment settings, such as different network structures and information
diffusion modes, and used it to study relative superiority of various
centrality measures. The results show that relative superiority of the
measures are affected by some factors, but not as much by others. Practical
implications of the results are discussed.

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

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


An Artificial Bee Colony (ABC) Algorithm for Efficient Partitioning of
Social Networks

Amal M. Abu Naser (Yarmouk University, Irbid, Jordan), Sawsan Alshattnawi
(Yarmouk University, Irbid, Jordan)

Social networks clustering is an NP-hard problem because it is difficult to
find the communities in a reasonable time; therefore, the solutions are
based on heuristics. Social networks clustering aims to collect people with
common interest in one group. Several approaches have been developed for
clustering social networks. In this paper the researchers, introduce a new
approach to cluster social networks based on Artificial Bee Colony
optimization algorithm, which is a swarm based meta-heuristic algorithm.
This approach aims to maximize the modularity, which is a measure that
represents the quality of network partitioning. The researchers cluster some
real known social networks with the proposed algorithm and compare it with
the other approaches. Their algorithm increases the modularity and gives
higher quality solutions than the previous approaches.

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

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


Towards Ontological Structures Extraction from Folksonomies: An Efficient
Fuzzy Clustering Approach

Marouf Zahia (EEDIS Laboratory, Djillali Liabes University of Sidi Bel
Abbes, Sidi Bel Abbes, Algeria), Benslimane Sidi Mohamed (EEDIS Laboratory,
Djillali Liabes University of Sidi Bel Abbes, Sidi Bel Abbes, Algeria)

Folksonomies are one of the technologies of Web 2.0 that permit users to
annotate resources on the Web. In this paper, the authors propose an
integrated approach to extract ontological structures from unstructured and
semi-structured resources. Our proposal overcome limitations of existing
approaches. It gives a formal, simple, and efficient solution to the tag
clustering and disambiguation problem. Moreover, their approach doesn't need
any ontology as an upper guide during the generation process. The generated
ontology can be used to enhance various tasks such as ontology evolution and

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

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


Fall Detection with Part-Based Approach for Indoor Environment

A. Annis Fathima (AU-KBC Research Centre, Madras Institute of Technology,
Anna University, Chennai, India), V. Vaidehi (AU-KBC Research Centre, Madras
Institute of Technology, Anna University, Chennai, India), K. Selvaraj
(AU-KBC Research Centre, Madras Institute of Technology, Anna University,
Chennai, India)

In the current scenario, majority of the aged people want to lead
independent life, and most of them prefer living at their own home.
According to recent case studies, the major cause of casualty among elder
people has been due to the accidental falls. Hence, it is eminent to have a
fall detection monitoring system at home. The prevailing method for fall
detection uses accelerometers to distinguish fall from other day to day
activities, these results are more erroneous. In this paper, vision based
"Fall detection with part-based approach (FDP)" is proposed to give accurate
information about the person activities in the indoor. The proposed scheme
uses background subtraction in association with aspect ratio and inclination
angle to detect the fall. Moreover, the proposed approach predicts the fall
even if the person is occluded by other objects or under self-occluded
condition. To detect the person even if only partly visible and occluded by
other non-moving objects, part based approach is adapted. To train the
system for detection purpose, Cascaded structure of Haar-rectangular
features with joint-boosting classifier is utilized. The detection
efficiency is measured by precision, recall and accuracy parameters.

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

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


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



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
*	DEST Register of Refereed Journals
*	Gale Directory of Publications & Broadcast Media
*	GetCited
*	Google Scholar
*	JournalTOCs
*	Library & Information Science Abstracts (LISA)
*	MediaFinder
*	Norwegian Social Science Data Services (NSD)
*	The Index of Information Systems Journals
*	The Standard Periodical Directory
*	Ulrich's Periodicals Directory

Coverage of IJIIT:

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

*  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

*  Intelligent Web mining and knowledge discovery systems 

*  Manufacturing information systems 

*  Models, architectures and behavior models for agent-oriented information

*  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


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



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