[AISWorld] Contents of International Journal of Intelligent Information Technologies (IJIIT), Vol. 11, No. 4.

[Vijayan Sugumaran]

The contents of the latest issue of:
International Journal of Intelligent Information Technologies (IJIIT)
Volume 11, Issue 4, October - December 2015
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(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.

ARTICLE 1

A Novel Cloud Intrusion Detection System Using Feature Selection and
Classification

Anand Kannan (Department of ICT, KTH University, Stockholm, Sweden), Karthik
Gururajan Venkatesan (Department of ICT, KTH University, Stockholm, Sweden),
Alexandra Stagkopoulou (Department of ICT, KTH University, Stockholm,
Sweden), Sheng Li (Department of ICT, KTH University, Stockholm, Sweden),
Sathyavakeeswaran Krishnan (Department of IT, Uppsala University, Uppsala,
Sweden), Arifur Rahman (Department of WNE, Linköping University, Linköping,
Sweden)

This paper proposes a new cloud intrusion detection system for detecting the
intruders in a traditional hybrid virtualized, cloud environment. The paper
introduces an effective feature selection algorithm called Temporal
Constraint based on Feature Selection algorithm and also proposes a
classification algorithm called hybrid decision tree. This hybrid decision
tree has been developed by extending the Enhanced C4.5 algorithm an existing
decision tree based classifier. Furthermore, the experiments conducted on
the sample Cloud Intrusion Detection Datasets (CIDD) show that the proposed
cloud intrusion detection system provides better detection accuracy than the
existing work and reduces the false positive rate.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/a-novel-cloud-intrusion-detection-system-using-fe
ature-selection-and-classification/139737
<http://www.igi-global.com/article/a-novel-cloud-intrusion-detection-system-
using-feature-selection-and-classification/139737> 

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=139737
<http://www.igi-global.com/viewtitlesample.aspx?id=139737> 

ARTICLE 2

Ranking Pages of Clustered Users using Weighted Page Rank Algorithm with
User Access Period

G. Sumathi (Department of Information Science and Technology, Anna
University, Chennai, India), S. Sendhilkumar (Department of Information
Science and Technology, Anna University, Chennai, India), G.S. Mahalakshmi
(Department of Computer Science and Engineering, Anna University, Chennai,
India)

The World Wide Web comprises billions of web pages and a tremendous amount
of information accessible inside of web pages. To recover obliged data from
the World Wide Web, search engines perform number of tasks in light of their
separate structural planning. The point at which a user gives a query to the
search engine, it commonly returns a bulky number of pages related to the
user's query. To backing the users to explore in the returned list,
different ranking techniques are connected on the search results. The vast
majority of the ranking calculations, which are given in the related work,
are either link or content based. The existing works don't consider user
access patterns. In this paper, a page ranking approach of Weighted Page
Rank Score Algorithm taking user access is being conceived for search
engines, which deals with the premise of weighted page rank method and
considers user access period of web pages into record. For this reason, the
web users are clustered based on the Particle Swarm Optimization (PSO)
approach. From those groups, the pages are ranked by improving the weighted
page rank approach with usage based parameter of user access period. This
calculation is utilized to discover more applicable pages as per user's
query. In this way, this idea is extremely helpful to show the most
important pages on the uppermost part of the search list on the principle of
user searching behavior, which shrinks the search space on a huge scale.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/ranking-pages-of-clustered-users-using-weighted-p
age-rank-algorithm-with-user-access-period/139738
<http://www.igi-global.com/article/ranking-pages-of-clustered-users-using-we
ighted-page-rank-algorithm-with-user-access-period/139738> 

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=139738
<http://www.igi-global.com/viewtitlesample.aspx?id=139738> 

ARTICLE 3

Queue Based Q-Learning for Efficient Resource Provisioning in Cloud Data
Centers

A. Meera (Department of Information Science and Technology, Anna University,
Chennai, India), S. Swamynathan (Department of Information Science and
Technology, Anna University, Chennai, India)

Cloud Computing is a novel paradigm that offers virtual resources on demand
through internet. Due to rapid demand to cloud resources, it is difficult to
estimate the user's demand. As a result, the complexity of resource
provisioning increases, which leads to the requirement of an adaptive
resource provisioning. In this paper, the authors address the problem of
efficient resource provisioning through Queue based Q-learning algorithm
using reinforcement learning agent. Reinforcement learning has been proved
in various domains for automatic control and resource provisioning. In the
absence of complete environment model, reinforcement learning can be used to
define optimal allocation policies. The proposed Queue based Q-learning
agent analyses the CPU utilization of all active Virtual Machines (VMs) and
detects the least loaded virtual machine for resource provisioning. It
detects the least loaded virtual machines through Inter Quartile Range.
Using the queue size of virtual machines it looks ahead by one time step to
find the optimal virtual machine for provisioning.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/queue-based-q-learning-for-efficient-resource-pro
visioning-in-cloud-data-centers/139739
<http://www.igi-global.com/article/queue-based-q-learning-for-efficient-reso
urce-provisioning-in-cloud-data-centers/139739> 

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=139739
<http://www.igi-global.com/viewtitlesample.aspx?id=139739> 

ARTICLE 4

Prediction of User Interests for Providing Relevant Information Using
Relevance Feedback and Re-ranking

L. Sai Ramesh (Department of Information Science and Technology, Anna
University, Chennai, India), S. Ganapathy (Department of Information Science
and Technology, Anna University, Chennai, India), R. Bhuvaneshwari
(Department of Information Science and Technology, Anna University, Chennai,
India), K. Kulothungan (Department of Information Science and Technology,
Anna University, Chennai, India), V. Pandiyaraju (Department of Information
Science and Technology, Anna University, Chennai, India), A. Kannan
(Department of Information Science and Technology, Anna University, Chennai,
India)

Predicting user interest based on their browsing pattern is useful in
relevant information retrieval. In such a scenario, queries must be
unambiguous and precise. For a broad-topic and ambiguous query, different
users may with different interests may search for information from the
internet. The inference and analysis of user search goals using rules will
be helpful to enhance the relevancy and user experience. A major deficiency
of generic search system is that they have static model which is to be
applied for all the users and hence are not adaptable to individual users.
User interest is important when performing clustering so that it is possible
to enhance the personalization. In this paper, a new approach is proposed to
infer user interests based on their queries and fast profile logs and to
provide relevant information to users based on personalization. For this
purpose, a framework is designed to analyze different user profiles and
interests while query processing including relevance analysis. Implicit
Feedback sessions are also constructed from user profiles based on mouse and
button clicks made in their current and past queries. In addition, browsing
behaviors of users are analyzed using rules and also using the feedback
sessions. Temporary documents are generated in this work for representing
the feedback sessions effectively. Finally, personalization is made based on
browsing behavior and relevant information is provided to the users. From
the experiments conducted in this work, it is observed that the proposed
model provide most accurate and relevant contents to the users when compared
with other related work.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/prediction-of-user-interests-for-providing-releva
nt-information-using-relevance-feedback-and-re-ranking/139740
<http://www.igi-global.com/article/prediction-of-user-interests-for-providin
g-relevant-information-using-relevance-feedback-and-re-ranking/139740> 

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=139740

  _____  

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
*	Thomson Reuters
*	Ulrich's Periodicals Directory
*	Web of Science

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

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