The contents of the latest issue of:

International Journal of Intelligent Information Technologies (IJIIT)

Official Publication of the Information Resources Management Association

Volume 7, Issue 4, October-December 2011

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

 

Towards a Possibilistic Information Retrieval System Using Semantic Query Expansion

 

Bilel Elayeb, ENSI Manouba University, Tunisia

Ibrahim Bounhas, Faculty of Sciences of Tunis, Tunisia

Oussama Ben Khiroun, ENSI Manouba University, Tunisia

Fabrice Evrard, Informatics Research Institute of Toulouse (IRIT), France

Narjès Bellamine-BenSaoud, ENSI Manouba University, Tunisia

 

This paper presents a new possibilistic information retrieval system using semantic query expansion. The work is involved in query expansion strategies based on external linguistic resources. In this case, the authors exploited the French dictionary “Le Grand Robert”. First, they model the dictionary as a graph and compute similarities between query terms by exploiting the circuits in the graph. Second, the possibility theory is used by taking advantage of a double relevance measure (possibility and necessity) between the articles of the dictionary and query terms. Third, these two approaches are combined by using two different aggregation methods. The authors also benefit from an existing approach for reweighting query terms in the possibilistic matching model to improve the expansion process. In order to assess and compare the approaches, the authors performed experiments on the standard ‘LeMonde94’ test collection.

 

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

http://www.igi-global.com/article/towards-possibilistic-information-retrieval-system/60655

 

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

http://www.igi-global.com/viewtitlesample.aspx?id=60655

 

PAPER TWO

 

Effective Fuzzy Ontology Based Distributed Document Using Non-Dominated Ranked Genetic Algorithm

 

M. Thangamani, Kongu Engineering College, India

P. Thangaraj, Bannari Amman Institute of Technology, India

 

The increase in the number of documents has aggravated the difficulty of classifying those documents according to specific needs. Clustering analysis in a distributed environment is a thrust area in artificial intelligence and data mining. Its fundamental task is to utilize characters to compute the degree of related corresponding relationship between objects and to accomplish automatic classification without earlier knowledge. Document clustering utilizes clustering technique to gather the documents of high resemblance collectively by computing the documents resemblance. Recent studies have shown that ontologies are useful in improving the performance of document clustering. Ontology is concerned with the conceptualization of a domain into an individual identifiable format and machine-readable format containing entities, attributes, relationships, and axioms. By analyzing types of techniques for document clustering, a better clustering technique depending on Genetic Algorithm (GA) is determined. Non-Dominated Ranked Genetic Algorithm (NRGA) is used in this paper for clustering, which has the capability of providing a better classification result. The experiment is conducted in 20 newsgroups data set for evaluating the proposed technique. The result shows that the proposed approach is very effective in clustering the documents in the distributed environment.

 

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

http://www.igi-global.com/article/effective-fuzzy-ontology-based-distributed/60656

 

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

http://www.igi-global.com/viewtitlesample.aspx?id=60656

 

PAPER THREE

 

A Dynamically Optimized Fluctuation Smoothing Rule for Scheduling Jobs in a Wafer Fabrication Factory

 

Toly Chen, Feng Chia University, Taiwan

 

This paper presents a dynamically optimized fluctuation smoothing rule to improve the performance of scheduling jobs in a wafer fabrication factory. The rule has been modified from the four-factor bi-criteria nonlinear fluctuation smoothing (4f-biNFS) rule, by dynamically adjusting factors. Some properties of the dynamically optimized fluctuation smoothing rule were also discussed theoretically. In addition, production simulation was also applied to generate some test data for evaluating the effectiveness of the proposed methodology. According to the experimental results, the proposed methodology was better than some existing approaches to reduce the average cycle time and cycle time standard deviation. The results also showed that it was possible to improve the performance of one without sacrificing the other performance metrics.

 

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

http://www.igi-global.com/article/dynamically-optimized-fluctuation-smoothing-rule/60657

 

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

http://www.igi-global.com/viewtitlesample.aspx?id=60657

 

PAPER FOUR

 

A Heuristic Method for Learning Path Sequencing for Intelligent Tutoring System (ITS) in E-Learning

 

Sami A. M. Al-Radaei, IT BHU, India

R. B. Mishra, IT BHU, India

 

Course sequencing is one of the vital aspects in an Intelligent Tutoring System (ITS) for e-learning to generate the dynamic and individual learning path for each learner. Many researchers used different methods like Genetic Algorithm, Artificial Neural Network, and TF-IDF (Term Frequency- Inverse Document Frequency) in E-leaning systems to find the adaptive course sequencing by obtaining the relation between the courseware. In this paper, heuristic semantic values are assigned to the keywords in the courseware based on the importance of the keyword. These values are used to find the relationship between courseware based on the different semantic values in them. The dynamic learning path sequencing is then generated. A comparison is made in two other important methods of course sequencing using TF-IDF and Vector Space Model (VSM) respectively, the method produces more or less same sequencing path in comparison to the two other methods. This method has been implemented using Eclipse IDE for java programming, MySQL as database, and Tomcat as web server.

 

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

http://www.igi-global.com/article/heuristic-method-learning-path-sequencing/60658

 

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

http://www.igi-global.com/viewtitlesample.aspx?id=60658

 

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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/InfoSciJournals.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@oakland.edu

 

 

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Vijayan Sugumaran, Ph.D.

Professor of Management Information Systems

Department of Decision and Information Sciences

School of Business Administration

Oakland University

Rochester, MI 48309

Phone: +1 248 370 2831

Fax: +1 248 370 4275

Email: sugumara@oakland.edu

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