[AISWorld] Contents of IJIIT 13(3) - 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 3, July - September 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

Functional Link Neural Network with Modified Artificial Bee Colony for Data
Classification

Tutut Herawan (Technology University of Yogyakarta, Yogyakarta, Indonesia),
Yana Mazwin Mohmad Hassim (Tun Hussein Onn University of Malaysia, Faculty
of Computer Science and Information Technology, Batu Pahat, Malaysia),
Rozaida Ghazali (Tun Hussein Onn University of Malaysia, Faculty of Computer
Science and Information Technology, Batu Pahat, Malaysia)

Functional Link Neural Network (FLNN) has emerged as an important tool for
solving non-linear classification problem and has been successfully applied
in many engineering and scientific problems. The FLNN structure is much more
modest than ordinary feed forward network like the Multilayer Perceptron
(MLP) due to its flat network architecture which employs less tuneable
weights for training. However, the standard Backpropagation (BP) learning
uses for FLNN training prone to get trap in local minima which affect the
FLNN classification performance. To recover the BP-learning drawback, this
paper proposes an Artificial Bee Colony (ABC) optimization with modification
on bee foraging behaviour (mABC) as an alternative learning scheme for FLNN.
This is motivated by good exploration and exploitation capabilities of
searching optimal weight parameters exhibit by ABC algorithm. The result of
the classification accuracy made by FLNN with mABC (FLNN-mABC) is compared
with the original FLNN architecture with standard Backpropagation (BP)
(FLNN-BP) and standard ABC algorithm (FLNN-ABC). The FLNN-mABC algorithm
provides better learning scheme for the FLNN network with average overall
improvement of 4.29% as compared to FLNN-BP and FLNN-ABC.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/functional-link-neural-network-with-modified-arti
ficial-bee-colony-for-data-classification/181872
<http://www.igi-global.com/article/functional-link-neural-network-with-modif
ied-artificial-bee-colony-for-data-classification/181872> 

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=181872
<http://www.igi-global.com/viewtitlesample.aspx?id=181872> 

ARTICLE 2

An Efficient Coronary Disease Diagnosis System Using Dual-Phase
Multi-Objective Optimization and Embedded Feature Selection

Priyatharshini R. (Easwari Engineering College, Department of Information
Technology, Chennai, India), Chitrakala S. (Anna University, Department of
Computer Science and Engineering, Chennai, India)

Developments in healthcare technologies have significantly enhanced spatial
resolution and improved contrast resolution, permitting analysis of
additional subtle structures than formerly attainable. An approach for
Automatic recognition and quantification of calcifications from arteries in
computed tomography (CT) scans is developed which is a key necessity in
planning the treatment of individuals with suspected coronary artery
disease. First, a Dual-Phase Multi-_objective Optimization approach using an
Active Contour Model-based region-growing technique is developed. Second, an
embedded feature selection method is developed with an expert classifier to
detect calcified objects in the segmented artery with great accuracy.
Finally, the Agatston scoring method is utilized to quantify the level of
coronary artery calcium plaque. Coronary CT images from the AS+CT scanner
with a slice thickness of 3 mm were obtained from clinical practice.
Experimental results demonstrate that our proposed method improves the
accuracy of lesion detection for better treatment planning.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/an-efficient-coronary-disease-diagnosis-system-us
ing-dual-phase-multi-objective-optimization-and-embedded-feature-selection/1
81873
<http://www.igi-global.com/article/an-efficient-coronary-disease-diagnosis-s
ystem-using-dual-phase-multi-objective-optimization-and-embedded-feature-sel
ection/181873> 

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=181873
<http://www.igi-global.com/viewtitlesample.aspx?id=181873> 

ARTICLE 3

Gravizor: A Graphical Tool for the Visualization of Web Search Engines
Results with Multi-Agent Based Modeling

Abdelkrim Bouramoul (Constantine 2 University, Department of Fundamental
Computer Science and Its Applications, Constantine, Algeria)

Users of Web search engines are generally confronted to numerous responses
that are rarely structured, making it difficult to analyze the available
results. Indeed, the linear results displayed through lists ordered
according to a relevance criterion, although still widely used, seem often
limitless. A solution to this problem is to improve the interfaces for
better visualization of large number of results. In this paper, we propose
modeling and implementation of a tool for graphical visualization and
manipulation of results returned by search engines. The goal is to
facilitate the analysis, the interpretation and the supervision of users'
information needs. The architecture of the 'Gravisor' tool is based on
Multi-Agent paradigm. It is composed of four agents working in full
cooperation and coordination. We hope that besides the web information
retrieval field, the three graphical visualization modes offered by the
'Gravisor' tool will be a promising alternative for better information
visualization in other areas.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/gravizor/181874
<http://www.igi-global.com/article/gravizor/181874> 

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=181874
<http://www.igi-global.com/viewtitlesample.aspx?id=181874> 

ARTICLE 4

Lexical Co-Occurrence and Contextual Window-Based Approach with Semantic
Similarity for Query Expansion

Jagendra Singh (Jawaharlal Nehru University, School of Computer and System
Sciences, New Delhi), Rakesh Kumar (Jawaharlal Nehru University, School of
Computer and System Sciences, New Delhi)

Query expansion (QE) is an efficient method for enhancing the efficiency of
information retrieval system. In this work, we try to capture the
limitations of pseudo-feedback based QE approach and propose a hybrid
approach for enhancing the efficiency of feedback based QE by combining
corpus-based, contextual based information of query terms, and semantic
based knowledge of query terms. First of all, this paper explores the use of
different corpus-based lexical co-occurrence approaches to select an optimal
combination of query terms from a pool of terms obtained using
pseudo-feedback based QE. Next, we explore semantic similarity approach
based on word2vec for ranking the QE terms obtained from top pseudo-feedback
documents. Further, we combine co-occurrence statistics, contextual window
statistics, and semantic similarity based approaches together to select the
best expansion terms for query reformulation. The experiments were performed
on FIRE ad-hoc and TREC-3 benchmark datasets. The statistics of our proposed
experimental results show significant improvement over baseline method.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/lexical-co-occurrence-and-contextual-window-based
-approach-with-semantic-similarity-for-query-expansion/181875
<http://www.igi-global.com/article/lexical-co-occurrence-and-contextual-wind
ow-based-approach-with-semantic-similarity-for-query-expansion/181875> 

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=181875
<http://www.igi-global.com/viewtitlesample.aspx?id=181875> 

  _____  

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

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